{ "name": "Embodied AI Radar - curated index", "source": "https://embodied-ai-radar.com/", "license": "CC BY 4.0 (curated text and data); arXiv metadata CC0", "built": "2026-11-10", "count": 1361, "fields": [ "id", "title", "type", "arxiv", "year", "venue", "org", "url", "code", "project", "topics", "tags", "robot_types", "highlight", "added", "takeaway_en", "takeaway_zh", "github_stars", "page", "labs", "code_license", "rating" ], "items": [ { "id": "arxiv-2212.06817", "title": "RT-1: Robotics Transformer for Real-World Control at Scale", "type": "paper", "arxiv": "2212.06817", "year": 2022, "venue": null, "org": "Google", "url": null, "code": "https://github.com/google-research/robotics_transformer", "project": null, "topics": [ "vla" ], "tags": [ "real", "arm", "transformer" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Tokenized actions plus a Transformer trained on 130k real demos showed that robot policies scale with data diversity.", "takeaway_zh": "将动作离散化为 token，用 13 万条真实演示训练 Transformer，证明机器人策略会随数据多样性扩展。", "github_stars": 1741, "page": "https://embodied-ai-radar.com/e/arxiv-2212.06817.html", "labs": [ "google-deepmind" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2307.15818", "title": "RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control", "type": "paper", "arxiv": "2307.15818", "year": 2023, "venue": null, "org": "Google DeepMind", "url": null, "code": null, "project": "https://robotics-transformer2.github.io/", "topics": [ "vla" ], "tags": [ "real", "arm", "vlm" ], "robot_types": [], "highlight": true, "rating": 2, "added": null, "takeaway_en": "Coined \"VLA\" — co-fine-tuning a web-scale VLM to output action tokens transfers semantic knowledge to robot control.", "takeaway_zh": "提出 \"VLA\" 概念：把网络规模的视觉语言模型联合微调为输出动作 token，把语义知识迁移到机器人控制。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2307.15818.html", "labs": [ "google-deepmind" ], "code_license": null }, { "id": "arxiv-2405.12213", "title": "Octo: An Open-Source Generalist Robot Policy", "type": "paper", "arxiv": "2405.12213", "year": 2024, "venue": null, "org": "UC Berkeley, Stanford, CMU", "url": null, "code": "https://github.com/octo-models/octo", "project": null, "topics": [ "vla" ], "tags": [ "real", "arm", "diffusion", "cross-embodiment", "open-weights" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "A small open diffusion-head Transformer pretrained on Open X-Embodiment that fine-tunes quickly to new robots and sensors.", "takeaway_zh": "在 Open X-Embodiment 上预训练的开源扩散头 Transformer 策略，可快速微调到新机器人和新传感器。", "github_stars": 1821, "page": "https://embodied-ai-radar.com/e/arxiv-2405.12213.html", "labs": [ "berkeley", "stanford", "cmu" ], "code_license": "MIT" }, { "id": "arxiv-2406.09246", "title": "OpenVLA: An Open-Source Vision-Language-Action Model", "type": "paper", "arxiv": "2406.09246", "year": 2024, "venue": null, "org": "Stanford, UC Berkeley, TRI", "url": null, "code": "https://github.com/openvla/openvla", "project": null, "topics": [ "vla" ], "tags": [ "real", "arm", "vlm", "open-weights" ], "robot_types": [], "highlight": true, "rating": 2, "added": null, "takeaway_en": "A 7B open VLA built on Llama 2 + DINOv2/SigLIP; the de-facto open baseline for VLA research and LoRA fine-tuning.", "takeaway_zh": "基于 Llama 2 与 DINOv2/SigLIP 的 7B 开源 VLA，是 VLA 研究与 LoRA 微调的事实开源基线。", "github_stars": 7288, "page": "https://embodied-ai-radar.com/e/arxiv-2406.09246.html", "labs": [ "berkeley", "stanford", "tri" ], "code_license": "MIT" }, { "id": "arxiv-2502.19645", "title": "Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success", "type": "paper", "arxiv": "2502.19645", "year": 2025, "venue": null, "org": "Stanford", "url": null, "code": "https://github.com/moojink/openvla-oft", "project": null, "topics": [ "vla" ], "tags": [ "real", "sim", "arm", "open-weights" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "OpenVLA-OFT — parallel decoding, action chunking and continuous actions make VLA fine-tuning much faster and more accurate.", "takeaway_zh": "OpenVLA-OFT：并行解码、动作分块与连续动作让 VLA 微调更快、成功率更高。", "github_stars": 1443, "page": "https://embodied-ai-radar.com/e/arxiv-2502.19645.html", "labs": [ "stanford" ], "code_license": "MIT" }, { "id": "arxiv-2410.24164", "title": "$π_0$: A Vision-Language-Action Flow Model for General Robot Control", "type": "paper", "arxiv": "2410.24164", "year": 2024, "venue": null, "org": "Physical Intelligence", "url": null, "code": "https://github.com/Physical-Intelligence/openpi", "project": null, "topics": [ "vla" ], "tags": [ "real", "arm", "bimanual", "flow-matching", "cross-embodiment", "open-weights" ], "robot_types": [ "arm", "hand" ], "highlight": true, "rating": 3, "added": null, "takeaway_en": "A VLM backbone with a flow-matching action expert, trained across many robots, doing long dexterous tasks like laundry folding.", "takeaway_zh": "VLM 主干加流匹配动作专家，跨多种机器人训练，能完成叠衣服等长时程灵巧任务。", "github_stars": 14603, "page": "https://embodied-ai-radar.com/e/arxiv-2410.24164.html", "labs": [ "physical-intelligence" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2504.16054", "title": "$π_{0.5}$: a Vision-Language-Action Model with Open-World Generalization", "type": "paper", "arxiv": "2504.16054", "year": 2025, "venue": null, "org": "Physical Intelligence", "url": null, "code": "https://github.com/Physical-Intelligence/openpi", "project": null, "topics": [ "vla" ], "tags": [ "real", "mobile", "co-training" ], "robot_types": [ "arm", "mobile" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Co-training on heterogeneous data lets a mobile manipulator clean kitchens and bedrooms in homes it has never seen.", "takeaway_zh": "通过异构数据联合训练，让移动操作机器人在从未见过的家庭中整理厨房和卧室。", "github_stars": 14603, "page": "https://embodied-ai-radar.com/e/arxiv-2504.16054.html", "labs": [ "physical-intelligence" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2501.09747", "title": "FAST: Efficient Action Tokenization for Vision-Language-Action Models", "type": "paper", "arxiv": "2501.09747", "year": 2025, "venue": null, "org": "Physical Intelligence, UC Berkeley, Stanford", "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "real", "tokenization" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Compresses action chunks with a DCT-based tokenizer so autoregressive VLAs can learn high-frequency, dexterous control.", "takeaway_zh": "用基于 DCT 的分词器压缩动作块，使自回归 VLA 能学习高频灵巧控制。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2501.09747.html", "labs": [ "berkeley", "stanford", "physical-intelligence" ], "code_license": null }, { "id": "arxiv-2410.07864", "title": "RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation", "type": "paper", "arxiv": "2410.07864", "year": 2024, "venue": null, "org": "Tsinghua University", "url": null, "code": "https://github.com/thu-ml/RoboticsDiffusionTransformer", "project": null, "topics": [ "vla" ], "tags": [ "real", "bimanual", "diffusion", "open-weights" ], "robot_types": [ "arm" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "A 1.2B diffusion Transformer with a unified action space across robots, targeted at bimanual manipulation.", "takeaway_zh": "12 亿参数扩散 Transformer，采用跨机器人统一动作空间，面向双臂操作。", "github_stars": 1816, "page": "https://embodied-ai-radar.com/e/arxiv-2410.07864.html", "labs": [ "tsinghua" ], "code_license": "MIT" }, { "id": "arxiv-2503.14734", "title": "GR00T N1: An Open Foundation Model for Generalist Humanoid Robots", "type": "paper", "arxiv": "2503.14734", "year": 2025, "venue": null, "org": "NVIDIA", "url": null, "code": "https://github.com/NVIDIA/Isaac-GR00T", "project": null, "topics": [ "vla" ], "tags": [ "real", "sim", "humanoid", "open-weights" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "A dual-system (VLM \"slow\" + diffusion \"fast\") humanoid policy trained on real, human-video and synthetic data.", "takeaway_zh": "双系统（VLM 慢思考 + 扩散快执行）人形机器人策略，用真实、人类视频与合成数据训练。", "github_stars": 8346, "page": "https://embodied-ai-radar.com/e/arxiv-2503.14734.html", "labs": [ "nvidia" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2506.01844", "title": "SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics", "type": "paper", "arxiv": "2506.01844", "year": 2025, "venue": null, "org": "Hugging Face", "url": null, "code": "https://github.com/huggingface/lerobot", "project": null, "topics": [ "vla" ], "tags": [ "real", "arm", "open-weights", "low-cost" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "A ~450M-parameter VLA trained on community LeRobot data that runs on consumer GPUs.", "takeaway_zh": "约 4.5 亿参数的 VLA，用社区 LeRobot 数据训练，可在消费级 GPU 上运行。", "github_stars": 28795, "page": "https://embodied-ai-radar.com/e/arxiv-2506.01844.html", "labs": [ "hugging-face" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2503.20020", "title": "Gemini Robotics: Bringing AI into the Physical World", "type": "paper", "arxiv": "2503.20020", "year": 2025, "venue": null, "org": "Google DeepMind", "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "real", "bimanual", "vlm" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Gemini 2.0-based VLA and embodied-reasoning models for dexterous, general robot control.", "takeaway_zh": "基于 Gemini 2.0 的 VLA 与具身推理模型，面向灵巧、通用的机器人控制。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2503.20020.html", "labs": [ "google-deepmind" ], "code_license": null }, { "id": "arxiv-2303.03378", "title": "PaLM-E: An Embodied Multimodal Language Model", "type": "paper", "arxiv": "2303.03378", "year": 2023, "venue": null, "org": "Google", "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "real", "vlm", "planning" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Injects continuous sensor embeddings into a 562B language model for embodied reasoning and planning.", "takeaway_zh": "将连续传感器嵌入注入 5620 亿参数语言模型，用于具身推理与规划。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2303.03378.html", "labs": [ "google-deepmind" ], "code_license": null }, { "id": "arxiv-2205.06175", "title": "A Generalist Agent", "type": "paper", "arxiv": "2205.06175", "year": 2022, "venue": null, "org": "DeepMind", "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "sim", "real", "transformer" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Gato — one Transformer with one set of weights plays games, captions images and stacks blocks with a real arm.", "takeaway_zh": "Gato：同一套权重的 Transformer 既能玩游戏、写图像描述，也能用真实机械臂堆积木。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2205.06175.html", "labs": [ "google-deepmind" ], "code_license": null }, { "id": "arxiv-2610.22178", "title": "REKI: Registering Knowledge in VLAs", "type": "paper", "arxiv": "2610.22178", "year": 2026, "venue": null, "org": "Kyutai", "url": null, "code": "https://github.com/kyutai-labs/reki", "project": null, "topics": [ "vla" ], "tags": [ "sim", "flow-matching", "memory", "open-weights", "public-data" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-19", "takeaway_en": "A 2.4B VLM + flow-matching VLA pretrained only on public data; reports beating all but ~2× larger models, plus cheap register-token memory.", "takeaway_zh": "仅用公开数据预训练的 2.4B VLM + 流匹配 VLA；报告称只输给约两倍大的模型，并用少量寄存器 token 实现低成本记忆。", "github_stars": 37, "page": "https://embodied-ai-radar.com/e/arxiv-2610.22178.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.21362", "title": "LTM: Latent Transition Based In-Context Memory for Vision-Language-Action Models", "type": "paper", "arxiv": "2610.21362", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/buaa-colalab/LTM-pi", "project": null, "topics": [ "vla" ], "tags": [ "sim", "memory", "pi0.5" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-19", "takeaway_en": "Gives π0.5 memory by caching compact latent transitions between observations, aimed at tasks where key objects go out of view.", "takeaway_zh": "通过缓存相邻观测之间的紧凑潜在转移，为 π0.5 加入记忆，面向关键物体移出视野的任务。", "github_stars": 6, "page": "https://embodied-ai-radar.com/e/arxiv-2610.21362.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.22146", "title": "FastVLA: Task-Conditioned and Temporally Adaptive Visual Token Compression for Efficient Vision-Language-Action Models", "type": "paper", "arxiv": "2610.22146", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/jiuyixu25/FastVLA", "project": null, "topics": [ "vla" ], "tags": [ "efficiency", "token-compression", "inference" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-19", "takeaway_en": "Speeds up VLA inference by keeping only the visual tokens the current task needs, with a token budget that changes from step to step.", "takeaway_zh": "只保留当前任务所需的视觉 token、并让 token 预算随时间步变化，以加快 VLA 推理。", "github_stars": 4, "page": "https://embodied-ai-radar.com/e/arxiv-2610.22146.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.23271", "title": "NestedVLA: Learning to Consolidate and Generate Skills for Vision-Language-Action Models", "type": "paper", "arxiv": "2610.23271", "year": 2026, "venue": "NeurIPS 2026", "org": null, "url": null, "code": "https://github.com/WanyuGroup/NeurIPS2026-NestedVLA", "project": null, "topics": [ "vla" ], "tags": [ "multitask", "skill-consolidation", "hypernetwork" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-19", "takeaway_en": "Learns skills separately, then consolidates them after the fact with a nested hypernetwork that generates VLA parameters instead of merging weights.", "takeaway_zh": "先独立学习各项技能，再用嵌套超网络“生成”VLA 参数来事后整合技能，而不是直接合并权重。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2610.23271.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2411.19650", "title": "CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation", "type": "paper", "arxiv": "2411.19650", "year": 2024, "venue": null, "org": "Microsoft Research Asia, Tsinghua", "url": null, "code": "https://github.com/microsoft/CogACT", "project": null, "topics": [ "vla" ], "tags": [ "real", "sim", "diffusion", "open-weights" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Instead of emitting action tokens from the VLM, a separate diffusion action transformer is conditioned on the VLM's \"cognition\" feature; reports clear gains over OpenVLA in sim and on real robots.", "takeaway_zh": "不再让 VLM 直接输出动作 token，而是用独立的扩散动作 Transformer 以 VLM 的“认知”特征为条件生成动作；报告在仿真和真机上明显优于 OpenVLA。", "github_stars": 435, "page": "https://embodied-ai-radar.com/e/arxiv-2411.19650.html", "labs": [ "microsoft", "tsinghua" ], "code_license": "MIT" }, { "id": "arxiv-2507.02029", "title": "RoboBrain 2.0 Technical Report", "type": "paper", "arxiv": "2507.02029", "year": 2025, "venue": null, "org": "BAAI", "url": null, "code": "https://github.com/FlagOpen/RoboBrain2.0", "project": null, "topics": [ "vla" ], "tags": [ "vlm", "planning", "spatial-reasoning", "open-weights" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Open embodied VLMs (7B and 32B) trained for spatial grounding, affordances, trajectories and multi-step task planning — a \"brain\" that sits above low-level policies.", "takeaway_zh": "开源具身视觉语言模型（7B 与 32B），面向空间定位、可供性、轨迹预测与多步任务规划，作为底层策略之上的“大脑”。", "github_stars": 1159, "page": "https://embodied-ai-radar.com/e/arxiv-2507.02029.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.21306", "title": "VLA-$Δ$: Learning Physical State Transition for Vision-Language-Action Models", "type": "paper", "arxiv": "2610.21306", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/ZJU-OmniAI/VLA-Delta", "project": "https://zju-omniai.github.io/VLA-Delta/", "topics": [ "vla" ], "tags": [ "state-transition", "auxiliary-supervision" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-19", "takeaway_en": "Argues VLAs overfit observation→action pairs and adds explicit supervision of the physical state change an action should cause, rather than predicting future states in language or pixels.", "takeaway_zh": "认为 VLA 容易过拟合“观测→动作”的对应关系，因此显式监督动作应引起的物理状态变化，而不是在语言或像素空间里预测未来状态。", "github_stars": 29, "page": "https://embodied-ai-radar.com/e/arxiv-2610.21306.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.22951", "title": "MambaAct: Efficient Mamba Action Experts for Flow-Matching Vision-Language-Action Models", "type": "paper", "arxiv": "2610.22951", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/giakhuyendihoc/MambaAct", "project": "https://giakhuyendihoc.github.io/mambaact-page/", "topics": [ "vla" ], "tags": [ "efficiency", "mamba", "flow-matching", "pi0.5" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-19", "takeaway_en": "Keeps π0.5's pretrained vision-language encoder but swaps its Transformer action expert — run many times per denoising loop — for a compact Mamba expert to cut inference cost.", "takeaway_zh": "保留 π0.5 预训练的视觉语言编码器，把在去噪循环中被反复调用的 Transformer 动作专家换成紧凑的 Mamba 专家，以降低推理开销。", "github_stars": 5, "page": "https://embodied-ai-radar.com/e/arxiv-2610.22951.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2502.19417", "title": "Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models", "type": "paper", "arxiv": "2502.19417", "year": 2025, "venue": null, "org": "Physical Intelligence", "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "real", "hierarchical", "language-feedback" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A high-level VLM reads open-ended prompts and mid-task corrections and turns them into short language commands for a low-level π0 policy.", "takeaway_zh": "高层 VLM 理解开放式指令与执行中的纠正，并将其转化为简短语言指令交给底层 π0 策略执行。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2502.19417.html", "labs": [ "physical-intelligence" ], "code_license": null }, { "id": "arxiv-2210.03094", "title": "VIMA: General Robot Manipulation with Multimodal Prompts", "type": "paper", "arxiv": "2210.03094", "year": 2022, "venue": "ICML 2023", "org": "Stanford, NVIDIA, Caltech", "url": null, "code": "https://github.com/vimalabs/VIMA", "project": null, "topics": [ "vla" ], "tags": [ "multimodal-prompt", "transformer", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Casts many tabletop tasks as interleaved text-and-image prompts to one Transformer policy, with the VIMA-Bench suite for systematic generalization tests.", "takeaway_zh": "把多种桌面任务统一成图文交错的提示，交给一个 Transformer 策略执行，并配套 VIMA-Bench 用于系统性泛化评测。", "github_stars": 857, "page": "https://embodied-ai-radar.com/e/arxiv-2210.03094.html", "labs": [ "stanford", "nvidia" ], "code_license": "MIT" }, { "id": "arxiv-2610.25927", "title": "CondenseVLA: Learnable History Condensation for Efficient Multi-Frame VLA", "type": "paper", "arxiv": "2610.25927", "year": 2026, "venue": "NeurIPS 2026", "org": null, "url": null, "code": "https://github.com/FeiyangHong/CondenseVLA", "project": null, "topics": [ "vla" ], "tags": [ "history", "memory", "efficiency" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-20", "takeaway_en": "Instead of feeding a VLA a longer frame history, learns to condense past frames into a few tokens that keep short-lived cues such as brief contacts and occlusions.", "takeaway_zh": "不靠简单加长历史帧窗口，而是学习把过去的帧压缩成少量 token，保留短暂接触、遮挡等转瞬即逝的线索。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25927.html", "labs": [], "code_license": "" }, { "id": "arxiv-2507.15493", "title": "GR-3 Technical Report", "type": "paper", "arxiv": "2507.15493", "year": 2025, "venue": null, "org": "ByteDance Seed", "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "real", "bimanual", "mobile-manipulation", "long-horizon" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "ByteDance Seed's generalist VLA: robot imitation data co-trained with web vision-language data, adapted quickly with VR-collected human trajectories; shown on long-horizon bimanual mobile tasks.", "takeaway_zh": "字节跳动 Seed 的通用 VLA——机器人数据模仿学习并与网络视觉-语言数据联合训练，再用 VR 采集的人类轨迹快速适配；在 ByteMini 双臂移动机器人上展示长时程与衣物操作任务。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2507.15493.html", "labs": [ "bytedance" ], "code_license": null }, { "id": "arxiv-2505.06111", "title": "UniVLA: Learning to Act Anywhere with Task-centric Latent Actions", "type": "paper", "arxiv": "2505.06111", "year": 2025, "venue": "RSS 2025", "org": "HKU, OpenDriveLab, AgiBot", "url": null, "code": "https://github.com/OpenDriveLab/UniVLA", "project": null, "topics": [ "vla", "learning" ], "tags": [ "latent-actions", "cross-embodiment", "video-pretraining" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Learns task-centric latent actions from videos (in DINO feature space, filtered by language) so one policy can be pretrained across robots and human video; reports beating OpenVLA with far less pretraining compute.", "takeaway_zh": "在 DINO 特征空间中、借助语言指令过滤，从视频里学习“任务相关”的隐动作，使策略可跨机器人与人类视频预训练；报告以少得多的预训练算力超过 OpenVLA。", "github_stars": 1145, "page": "https://embodied-ai-radar.com/e/arxiv-2505.06111.html", "labs": [ "hku", "agibot" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2501.15830", "title": "SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model", "type": "paper", "arxiv": "2501.15830", "year": 2025, "venue": "RSS 2025", "org": "Shanghai AI Lab, ShanghaiTech, TeleAI", "url": null, "code": "https://github.com/SpatialVLA/SpatialVLA", "project": null, "topics": [ "vla" ], "tags": [ "3d", "spatial", "action-tokenization" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Adds 3D-aware position encoding to visual tokens and adaptive action grids, pretrained on 1.1M real robot episodes; new robot setups are handled by re-discretizing the grids.", "takeaway_zh": "为视觉 token 加入 3D 位置编码，并用自适应动作网格表示动作，在 110 万条真机轨迹上预训练；迁移到新机器人时只需重新划分动作网格。", "github_stars": 736, "page": "https://embodied-ai-radar.com/e/arxiv-2501.15830.html", "labs": [ "shanghai-ai-lab" ], "code_license": "" }, { "id": "arxiv-2610.29730", "title": "Post-hoc Epistemic Uncertainty for Vision-Language-Action Policies", "type": "paper", "arxiv": "2610.29730", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/post-hoc-vla-uncertainty/post-hoc-vla-uncertainty", "project": null, "topics": [ "vla" ], "tags": [ "uncertainty", "safety", "gaussian-process", "failure-detection" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-22", "takeaway_en": "Post-hoc kernel-variance heads (Gaussian-process variance over training-demo embeddings) estimate one VLA policy's epistemic uncertainty without an ensemble, so the robot knows when to ask for help.", "takeaway_zh": "在训练完成后为单个 VLA 策略加上轻量的核方差头（基于训练示教嵌入的高斯过程方差）来估计认知不确定性，无需训练和运行策略集成，让机器人知道何时该求助或补充示教。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2610.29730.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.30735", "title": "When One Step Is Enough: An Empirical Study of Euler Integration in Flow-Matching Policies", "type": "paper", "arxiv": "2610.30735", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla", "learning" ], "tags": [ "flow-matching", "inference-speed", "empirical-study" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-23", "takeaway_en": "Tests six flow-matching VLAs and reports that one-step Euler action generation largely matches multi-step sampling at scale — a useful check before adding special one-shot objectives to speed up control (CoRL 2026).", "takeaway_zh": "在六个基于流匹配的 VLA 上实验，报告单步 Euler 生成动作在规模足够时与多步采样效果基本相当——在为提速引入专门的单步生成目标之前值得先看（CoRL 2026）。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.30735.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.25043", "title": "VLA-Rolling: Fewer Denoising Steps, Better Control via Adaptive Action Reuse", "type": "paper", "arxiv": "2610.25043", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/ZJU-OmniAI/VLA-Rolling", "project": "https://chenbao123ww.github.io/VLA-Rolling/", "topics": [ "vla" ], "tags": [ "flow-matching", "inference-speed", "action-chunking", "training-free-backbone" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-23", "takeaway_en": "Reuses the unexecuted tail of a flow-matching VLA's action chunk to start the next one, with a small confidence model setting how much to re-denoise; reports lower inference cost on π0.5, SmolVLA and others.", "takeaway_zh": "流匹配 VLA 每次只执行动作块的前几步，剩余部分通常丢弃；本文把这段\"动作尾巴\"作为下一块的起点，并用小型置信度模型决定还需去噪多少步；论文报告在 π0.5、SmolVLA 等模型上降低推理开销。", "github_stars": 17, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25043.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.22471", "title": "Has GPT-6-Astra Achieved Embodied Intelligence?", "type": "paper", "arxiv": "2610.22471", "year": 2026, "venue": null, "org": "SenseNova, ACE Robotics, Ropedia, NTU, UTokyo", "url": null, "code": "https://github.com/OpenSenseNova/SenseNova-RoboRSI", "project": "https://github.com/OpenSenseNova/SenseNova-RoboRSI", "topics": [ "vla", "manipulation", "navigation" ], "tags": [ "evaluation", "llm-as-policy", "zero-shot", "sim", "real" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-10-23", "takeaway_en": "Drives robots with a frozen frontier LLM plus in-context 'knowledge packages' on 12 sim benchmarks + 8 real robots; reports beating prior bests on 9 of 12, but weak real-robot results (competence, not yet intelligence).", "takeaway_zh": "用冻结的前沿大模型加上下文“知识包”直接控制机器人，覆盖 12 个仿真基准和 8 台真机；报告在 12 个基准中的 9 个超过此前最好结果，但真机表现较弱——结论是具备了具身“能力”，尚未达到具身“智能”。", "github_stars": 601, "page": "https://embodied-ai-radar.com/e/arxiv-2610.22471.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2405.14093", "title": "A Survey on Vision-Language-Action Models for Embodied AI", "type": "paper", "arxiv": "2405.14093", "year": 2024, "venue": null, "org": "CUHK, Huawei Noah's Ark Lab", "url": null, "code": null, "project": "https://github.com/yueen-ma/Awesome-VLA", "topics": [ "vla" ], "tags": [ "survey", "vla", "taxonomy", "awesome-list" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "An early survey of VLA models, sorting them into component work (vision, language, world models), low-level control policies and high-level task planners, plus datasets, simulators and benchmarks; companion paper list.", "takeaway_zh": "较早的 VLA 综述：把相关工作分为组件研究（视觉、语言、世界模型等）、底层控制策略与高层任务规划器，并汇总数据集、仿真器与基准；附配套论文列表。", "github_stars": 694, "page": "https://embodied-ai-radar.com/e/arxiv-2405.14093.html", "labs": [ "cuhk" ], "code_license": "" }, { "id": "arxiv-2312.07843", "title": "Foundation Models in Robotics: Applications, Challenges, and the Future", "type": "paper", "arxiv": "2312.07843", "year": 2023, "venue": "IJRR 2024", "org": "Stanford, Princeton, UT Austin, Google DeepMind and others", "url": null, "code": null, "project": "https://github.com/robotics-survey/Awesome-Robotics-Foundation-Models", "topics": [ "vla" ], "tags": [ "survey", "foundation-models", "llm", "vlm" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Survey of how pretrained foundation models are used in robot perception, decision-making and control, and what holds them back — scarce robot data, safety and uncertainty, real-time execution.", "takeaway_zh": "综述预训练基础模型如何用于机器人的感知、决策与控制，以及主要瓶颈：机器人数据稀缺、安全性与不确定性量化、实时执行。", "github_stars": 1423, "page": "https://embodied-ai-radar.com/e/arxiv-2312.07843.html", "labs": [ "stanford", "google-deepmind", "ut-austin", "princeton" ], "code_license": "MIT" }, { "id": "arxiv-2407.06886", "title": "Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI", "type": "paper", "arxiv": "2407.06886", "year": 2024, "venue": null, "org": "Sun Yat-sen University (HCPLab)", "url": null, "code": null, "project": "https://github.com/HCPLab-SYSU/Embodied_AI_Paper_List", "topics": [ "vla", "sim", "navigation", "perception" ], "tags": [ "survey", "embodied-ai", "sim2real", "world-models" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Broad embodied-AI survey — robots, simulators, embodied perception, interaction, agents and sim-to-real — with the role of multimodal and world models; backed by the widely used HCPLab paper list.", "takeaway_zh": "覆盖面很广的具身智能综述：机器人本体、仿真器、具身感知、交互、具身智能体与 sim-to-real，并讨论多模态大模型与世界模型的作用；配套中山大学 HCPLab 维护的常用论文列表。", "github_stars": 2194, "page": "https://embodied-ai-radar.com/e/arxiv-2407.06886.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.18568", "title": "WAVE-0: Bridging VLA Policies and WAMs with Lightweight Latent Future Prediction", "type": "paper", "arxiv": "2610.18568", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/AutoLab-SAI-SJTU/WAVE-0", "project": "https://hmwang2002.github.io/release/wave0/", "topics": [ "vla", "world-models" ], "tags": [ "world-action-model", "latent-world-model", "sim", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-24", "takeaway_en": "Adds a 257M latent world model to a pretrained VLA so predicted future features condition the action expert, with no pixel generation or video pretraining; reports 98.7% on LIBERO and real-robot gains over π0.5.", "takeaway_zh": "在预训练 VLA 上加一个 2.57 亿参数的潜空间世界模型，用预测的未来表征去条件化动作专家，无需像素生成或大规模视频预训练；报告 LIBERO 成功率 98.7%，真机任务上明显优于 π0.5。", "github_stars": 39, "page": "https://embodied-ai-radar.com/e/arxiv-2610.18568.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.20019", "title": "MorphAct: Robot Policies Morphing with the Worlds They Act In", "type": "paper", "arxiv": "2610.20019", "year": 2026, "venue": null, "org": "Shanghai Jiao Tong University", "url": null, "code": "https://github.com/MINT-SJTU/MorphAct", "project": "https://mint-sjtu.github.io/MorphAct.io/", "topics": [ "vla", "learning" ], "tags": [ "lora", "contextual-parameters", "multi-task", "sim", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-24", "takeaway_en": "Instead of one fixed set of VLA weights, generates context-dependent low-rank updates for both the vision-language and action modules; reports gains up to 16.3% on LIBERO/Meta-World and up to 14% over π0.5 on real tasks.", "takeaway_zh": "不再让 VLA 在所有交互情境下共用固定参数，而是按当前情境为视觉语言模块和动作模块生成耦合的低秩参数更新；报告在 LIBERO/Meta-World 上最高提升 16.3%，真机上相对 π0.5 最高提升 14%。", "github_stars": 42, "page": "https://embodied-ai-radar.com/e/arxiv-2610.20019.html", "labs": [ "sjtu" ], "code_license": "MIT" }, { "id": "arxiv-2610.21453", "title": "CarryOn: A Dual-Loop Harness for Personalized and Reliable Robot Assistance", "type": "paper", "arxiv": "2610.21453", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/JIA-Lab-research/CarryOn", "project": "https://vincent2311.github.io/Projects/CarryOn/", "topics": [ "vla", "manipulation" ], "tags": [ "agentic", "harness", "personalization", "safety", "failure-recovery", "sim", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-24", "takeaway_en": "Wraps a frozen VLA in two loops — one keeps user preferences and scene memory for planning, one blocks hazardous actions and calls recovery skills; reports violations down from 52.6% to 6.1%, recovery up from 13% to 80%.", "takeaway_zh": "给冻结的 VLA 套上双环框架：规划环保存用户偏好与场景记忆以做个性化任务分解，执行环拦截危险接触并调用可复用的恢复技能；报告安全违规率从 52.6% 降到 6.1%，失败恢复成功率从 13% 提升到 80%。", "github_stars": 7, "page": "https://embodied-ai-radar.com/e/arxiv-2610.21453.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.20442", "title": "Towards Agentic Robots: Model, Data, Environment, and Harness", "type": "paper", "arxiv": "2610.20442", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://github.com/chonghanqin/Awesome-Agentic-Robots", "topics": [ "vla" ], "tags": [ "survey", "agentic", "awesome-list", "world-action-model" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-24", "takeaway_en": "Survey of 'agentic robots' — foundation-model agents that plan, collect data, build environments and recover from errors — organised into 17 categories under model, data, environment and harness; companion paper list.", "takeaway_zh": "关于\"智能体化机器人\"的综述：基础模型智能体负责规划、采集与筛选数据、构建环境与测试并自主纠错；把相关研究归为模型、数据、环境、执行框架四大领域下的 17 个类别，附配套论文列表。", "github_stars": 173, "page": "https://embodied-ai-radar.com/e/arxiv-2610.20442.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.30108", "title": "Multimodal Embodied Agents: A Unified Capability-centric Perspective from Computer-Use to Robot-Use", "type": "paper", "arxiv": "2610.30108", "year": 2026, "venue": null, "org": "Show Lab, National University of Singapore", "url": null, "code": null, "project": "https://github.com/showlab/Awesome-Multimodal-Embodied-Agent", "topics": [ "vla" ], "tags": [ "survey", "agentic", "awesome-list", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-25", "takeaway_en": "Survey framing computer-use and robot agents with five functions — perceive, anticipate, plan, act, verify; across 64 benchmarks it finds only 5 test anticipation and 3 test verification. Companion paper list.", "takeaway_zh": "用\"感知、预判、规划、执行、验证\"五种能力统一看待电脑操作智能体和机器人智能体的综述；统计 64 个基准后发现只有 5 个评测预判、3 个评测验证。附配套论文列表。", "github_stars": 290, "page": "https://embodied-ai-radar.com/e/arxiv-2610.30108.html", "labs": [ "nus" ], "code_license": "CC0-1.0" }, { "id": "arxiv-2508.07917", "title": "MolmoAct: Action Reasoning Models that can Reason in Space", "type": "paper", "arxiv": "2508.07917", "year": 2025, "venue": null, "org": "Ai2, University of Washington", "url": null, "code": "https://github.com/allenai/molmoact", "project": "https://allenai.org/blog/molmoact", "topics": [ "vla" ], "tags": [ "real", "arm", "vlm", "open-weights", "reasoning", "spatial" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "An open 7B VLA that first outputs depth-aware perception tokens, then an editable 2D trajectory, then actions; reports 70.5% zero-shot on SimplerEnv and 86.6% on LIBERO, with weights, code and data released.", "takeaway_zh": "开源 7B VLA，先输出深度感知 token，再生成可编辑的二维轨迹，最后输出动作；论文报告 SimplerEnv 零样本 70.5%、LIBERO 86.6%，并开放权重、代码和数据。", "github_stars": 393, "page": "https://embodied-ai-radar.com/e/arxiv-2508.07917.html", "labs": [ "uw", "ai2" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2605.02881", "title": "MolmoAct2: Action Reasoning Models for Real-world Deployment", "type": "paper", "arxiv": "2605.02881", "year": 2026, "venue": "CoRL 2026", "org": "Ai2", "url": null, "code": "https://github.com/allenai/molmoact2", "project": "https://allenai.org/blog/molmoact2", "topics": [ "vla" ], "tags": [ "real", "arm", "bimanual", "vlm", "open-weights", "open-data", "reasoning", "flow-matching", "tokenizer" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Fully open successor to MolmoAct: a flow-matching action expert on an embodied-reasoning VLM, the OpenFAST tokenizer and a 720 h bimanual YAM dataset; reports beating π0.5 on 7 sim and real benchmarks.", "takeaway_zh": "MolmoAct 的全开源后继：在具身推理 VLM（MolmoER）上挂接流匹配连续动作专家，并发布开源动作分词器 OpenFAST 与 720 小时双臂 YAM 遥操作数据集；论文报告在 7 个仿真与真机基准上超过 π0.5 等基线，权重、训练代码与数据全部开放。", "github_stars": 825, "page": "https://embodied-ai-radar.com/e/arxiv-2605.02881.html", "labs": [ "ai2" ], "code_license": "Apache-2.0" }, { "id": "unifolm-vla-0", "title": "UnifoLM-VLA-0", "type": "code", "arxiv": null, "year": 2026, "venue": null, "org": "Unitree", "url": null, "code": "https://github.com/unitreerobotics/unifolm-vla", "project": "https://unigen-x.github.io/unifolm-vla.github.io", "topics": [ "vla", "locomotion" ], "tags": [ "real", "humanoid", "unitree-g1", "open-weights", "libero" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Unitree's open VLA for humanoid manipulation: a VLM further pretrained on robot data, fine-tuned on 12 open Unitree G1 datasets; the README reports one policy handling 12 task categories on a real G1.", "takeaway_zh": "宇树开源的人形机器人操作 VLA：在 UnifoLM-VLM 基础上用机器人操作数据继续预训练，再在 12 个公开的 Unitree G1 数据集上微调；README 称单一策略可在真实 G1 上完成 12 类复杂操作任务，另提供 LIBERO 微调权重。", "github_stars": 650, "page": "https://embodied-ai-radar.com/e/unifolm-vla-0.html", "labs": [], "code_license": "" }, { "id": "arxiv-2505.03233", "title": "GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data", "type": "paper", "arxiv": "2505.03233", "year": 2025, "venue": "CoRL 2025", "org": "Peking University (PKU-EPIC)", "url": null, "code": "https://github.com/PKU-EPIC/GraspVLA", "project": null, "topics": [ "vla", "manipulation", "sim" ], "tags": [ "sim2real", "arm", "grasping", "synthetic-data", "flow-matching" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Pretrains a grasping VLA almost only on SynGrasp-1B, a billion-frame photorealistic sim dataset, co-trained with web data; reports zero-shot sim-to-real open-vocabulary grasping and few-shot adaptation.", "takeaway_zh": "几乎只用十亿帧级的照片级仿真抓取数据集 SynGrasp-1B（并联合网络语义数据）预训练抓取 VLA；论文报告可零样本从仿真迁移到真实开放词汇抓取，并支持少样本适配。", "github_stars": 430, "page": "https://embodied-ai-radar.com/e/arxiv-2505.03233.html", "labs": [ "pku" ], "code_license": "" }, { "id": "arxiv-2511.14759", "title": "π*0.6: a VLA That Learns From Experience", "type": "paper", "arxiv": "2511.14759", "year": 2025, "venue": "RSS 2026", "org": "Physical Intelligence", "url": null, "code": null, "project": "https://pi.website/blog/pistar06", "topics": [ "vla", "learning" ], "tags": [ "real", "arm", "mobile-manipulation", "rl", "offline-rl", "interventions" ], "robot_types": [], "highlight": true, "rating": 2, "added": null, "takeaway_en": "RECAP trains a VLA with RL via advantage conditioning on demos, on-robot rollouts and teleop corrections; reports it more than doubles throughput on hard tasks like laundry folding and espresso making.", "takeaway_zh": "RECAP 通过优势条件化，用演示、机器人自主运行数据和遥操作纠正对 VLA 做强化学习；论文报告在叠衣服、做意式咖啡等难任务上吞吐量提升一倍以上、失败率约减半。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2511.14759.html", "labs": [ "physical-intelligence" ], "code_license": null }, { "id": "arxiv-2510.10274", "title": "X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model", "type": "paper", "arxiv": "2510.10274", "year": 2025, "venue": "ICLR 2026", "org": "Tsinghua AIR, Shanghai AI Lab", "url": null, "code": "https://github.com/2toinf/X-VLA", "project": "https://thu-air-dream.github.io/X-VLA/", "topics": [ "vla" ], "tags": [ "real", "sim", "cross-embodiment", "flow-matching", "open-weights" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Gives each data source its own small set of learnable soft-prompt embeddings, so one plain-Transformer flow-matching policy (0.9B) trains across embodiments; evaluated on 6 sim benchmarks and 3 real robots.", "takeaway_zh": "为每个数据来源配一组可学习的软提示嵌入，使一个基于标准 Transformer 的流匹配策略（0.9B）能跨本体联合训练；在 6 个仿真基准和 3 台真实机器人上评测。", "github_stars": 761, "page": "https://embodied-ai-radar.com/e/arxiv-2510.10274.html", "labs": [ "shanghai-ai-lab", "tsinghua" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2510.13778", "title": "InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy", "type": "paper", "arxiv": "2510.13778", "year": 2025, "venue": null, "org": "Shanghai AI Lab (Intern Robotics)", "url": null, "code": "https://github.com/InternRobotics/InternVLA-M1", "project": null, "topics": [ "vla" ], "tags": [ "real", "sim", "arm", "spatial", "grounding", "synthetic-data" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Pretrains spatial grounding on 2.3M samples ('where to act'), then post-trains actions with spatial prompts ('how to act'); reports +14.6% on SimplerEnv Google Robot over the same model without spatial guidance.", "takeaway_zh": "先在 230 万条空间推理样本上做空间定位预训练（\"在哪里做\"），再用空间提示做动作后训练（\"怎么做\"）；论文报告在 SimplerEnv Google Robot 上比无空间引导的同一模型高 14.6%。", "github_stars": 439, "page": "https://embodied-ai-radar.com/e/arxiv-2510.13778.html", "labs": [ "shanghai-ai-lab" ], "code_license": "MIT" }, { "id": "arxiv-2502.13130", "title": "Magma: A Foundation Model for Multimodal AI Agents", "type": "paper", "arxiv": "2502.13130", "year": 2025, "venue": "CVPR 2025", "org": "Microsoft Research", "url": null, "code": "https://github.com/microsoft/Magma", "project": "https://microsoft.github.io/Magma", "topics": [ "vla" ], "tags": [ "real", "sim", "arm", "vlm", "agents", "open-weights" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "One model for UI navigation and robot manipulation, pretrained on images, videos and robot data labelled with Set-of-Mark (what to act on) and Trace-of-Mark (how things move); model and code are public.", "takeaway_zh": "同一个模型同时做界面操作与机器人操作：在图像、视频和机器人数据上预训练，用 Set-of-Mark 标注可操作对象、用 Trace-of-Mark 标注运动轨迹；模型与代码公开。", "github_stars": 1951, "page": "https://embodied-ai-radar.com/e/arxiv-2502.13130.html", "labs": [ "microsoft" ], "code_license": "MIT" }, { "id": "arxiv-2507.15597", "title": "Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos", "type": "paper", "arxiv": "2507.15597", "year": 2025, "venue": "ICML 2026", "org": "BeingBeyond", "url": null, "code": "https://github.com/BeingBeyond/Being-H0", "project": "https://beingbeyond.github.io/Being-H0", "topics": [ "vla", "manipulation" ], "tags": [ "real", "dexterous-hand", "human-video", "motion-tokenization" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A dexterous VLA pretrained on human hand motion from mocap, VR and RGB video ('physical instruction tuning'), with part-level motion tokens, then aligned to 3D space and post-trained for real robot hands.", "takeaway_zh": "灵巧手 VLA：先在动捕、VR 与普通视频中的人手动作上预训练（\"物理指令微调\"），使用部件级动作 token，再做三维空间对齐，并针对真实机器人灵巧手后训练。", "github_stars": 61, "page": "https://embodied-ai-radar.com/e/arxiv-2507.15597.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2509.11766", "title": "Igniting VLMs toward the Embodied Space", "type": "paper", "arxiv": "2509.11766", "year": 2025, "venue": null, "org": "X Square Robot", "url": null, "code": "https://github.com/X-Square-Robot/wall-x", "project": null, "topics": [ "vla" ], "tags": [ "real", "arm", "vlm", "reasoning", "open-weights" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "WALL-OSS: an open embodied foundation model that links instruction reasoning, subgoal decomposition and action generation in one differentiable model ('unified cross-level CoT'); training code and checkpoints are public.", "takeaway_zh": "WALL-OSS：开源具身基础模型，在同一可微模型中串联指令推理、子目标分解与细粒度动作生成（\"统一跨层级思维链\"）；训练代码与模型权重已开源。", "github_stars": 1320, "page": "https://embodied-ai-radar.com/e/arxiv-2509.11766.html", "labs": [ "x-square" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2409.20537", "title": "Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers", "type": "paper", "arxiv": "2409.20537", "year": 2024, "venue": "NeurIPS 2024", "org": "MIT", "url": null, "code": "https://github.com/liruiw/HPT", "project": "https://liruiw.github.io/hpt/", "topics": [ "vla", "learning" ], "tags": [ "real", "sim", "cross-embodiment", "pretraining", "open-weights" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "HPT: per-robot stems turn proprioception and vision into a few tokens for one shared transformer trunk, pretrained on 52 datasets (robot, sim, human video); reports 20%+ gains on unseen tasks.", "takeaway_zh": "HPT：为每种机器人设计“输入头”，把本体感知与视觉映射成短 token 序列，送入共享的 Transformer 主干；在 52 个数据集（真机、仿真、人类视频）上预训练，报告在未见任务上微调提升 20% 以上。", "github_stars": 547, "page": "https://embodied-ai-radar.com/e/arxiv-2409.20537.html", "labs": [ "mit" ], "code_license": "MIT" }, { "id": "arxiv-2410.11758", "title": "Latent Action Pretraining from Videos", "type": "paper", "arxiv": "2410.11758", "year": 2024, "venue": "ICLR 2025", "org": "KAIST / UW / Microsoft Research / NVIDIA", "url": null, "code": "https://github.com/LatentActionPretraining/LAPA", "project": "https://latentactionpretraining.github.io/", "topics": [ "vla", "learning" ], "tags": [ "real", "sim", "arm", "latent-actions", "human-video", "pretraining" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "LAPA pretrains a VLA without action labels: it learns discrete latent actions between video frames, predicts them from image + text, then maps them to robot actions; reports beating OpenVLA on real tasks.", "takeaway_zh": "LAPA 无需动作标注即可预训练 VLA：先从视频相邻帧间学习离散隐动作，再训练模型由图像和指令预测隐动作，最后用少量机器人数据映射到真实动作；报告在真机任务上优于 OpenVLA。", "github_stars": 587, "page": "https://embodied-ai-radar.com/e/arxiv-2410.11758.html", "labs": [ "nvidia", "uw", "microsoft", "kaist" ], "code_license": "MIT" }, { "id": "arxiv-2502.05485", "title": "HAMSTER: Hierarchical Action Models for Open-World Robot Manipulation", "type": "paper", "arxiv": "2502.05485", "year": 2025, "venue": "ICLR 2025", "org": "NVIDIA / UW / USC", "url": null, "code": null, "project": "https://hamster-robo.github.io/", "topics": [ "vla", "manipulation" ], "tags": [ "real", "arm", "hierarchical", "vlm-planner", "2d-paths" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A hierarchical VLA in which a fine-tuned VLM draws a coarse 2D path for the end effector on the image and a low-level 3D policy follows it, so the VLM can be trained on cheap off-domain data such as sim or videos.", "takeaway_zh": "分层 VLA：微调后的 VLM 在图像上画出末端执行器的粗略二维路径，再由底层三维策略跟随执行；因此 VLM 可以用仿真、视频等低成本的非本域数据训练。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2502.05485.html", "labs": [ "nvidia", "uw", "usc" ], "code_license": null }, { "id": "arxiv-2403.09631", "title": "3D-VLA: A 3D Vision-Language-Action Generative World Model", "type": "paper", "arxiv": "2403.09631", "year": 2024, "venue": "ICML 2024", "org": "UMass Amherst / MIT", "url": null, "code": "https://github.com/UMass-Embodied-AGI/3D-VLA", "project": "https://vis-www.cs.umass.edu/3dvla", "topics": [ "vla", "world-models" ], "tags": [ "3d", "point-cloud", "goal-generation", "dataset" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A 3D-aware LLM with scene, object and action tokens, aligned with diffusion models so it can imagine goal images and point clouds before planning actions; trained on ~2M 3D-language-action pairs the authors assembled.", "takeaway_zh": "具备三维感知的大语言模型，引入场景、物体与动作交互 token，并与扩散模型对齐，可在规划动作前“想象”目标图像和点云；训练数据为作者整理的约 200 万条三维-语言-动作样本。", "github_stars": 639, "page": "https://embodied-ai-radar.com/e/arxiv-2403.09631.html", "labs": [ "mit" ], "code_license": "" }, { "id": "arxiv-2602.14979", "title": "RynnBrain: Open Embodied Foundation Models", "type": "paper", "arxiv": "2602.14979", "year": 2026, "venue": null, "org": "Alibaba DAMO Academy", "url": null, "code": "https://github.com/alibaba-damo-academy/RynnBrain", "project": "https://alibaba-damo-academy.github.io/RynnBrain.github.io", "topics": [ "vla", "perception", "navigation" ], "tags": [ "vlm", "embodied-brain", "spatiotemporal", "planning", "open-weights" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Open spatiotemporal embodied foundation models (2B, 8B, 30B-A3B MoE) for egocentric understanding, localization, grounded reasoning and planning, plus post-trained Nav, Plan, VLA and CoP variants.", "takeaway_zh": "开源时空具身基础模型（2B、8B 与 30B-A3B MoE），覆盖第一视角理解、时空定位、物理落地推理与规划，并提供导航、规划、VLA、CoP 等后训练版本。", "github_stars": 904, "page": "https://embodied-ai-radar.com/e/arxiv-2602.14979.html", "labs": [ "alibaba" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.31859", "title": "Full-Weight VLA Fine-Tuning Without an fp32 Master Copy Silently Erases Most Updates", "type": "paper", "arxiv": "2610.31859", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "vla", "learning" ], "tags": [ "fine-tuning", "training-recipe", "mixed-precision", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Practical warning: fine-tuning VLA weights in bf16 without an fp32 master copy rounds away AdamW updates below 1/512 of a weight; finds this recipe in 9 of 12 open VLA codebases and 79%+ of updates lost on LIBERO.", "takeaway_zh": "实用警示：VLA 权重以 bf16 微调且不保留 fp32 主副本时，小于权重 1/512 的 AdamW 更新会被舍入抹掉；报告 12 个开源 VLA 代码库中 9 个的默认或文档配方如此训练，LIBERO 实验中至少 79% 的更新被抹除。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31859.html", "labs": [], "code_license": null }, { "id": "arxiv-2410.06158", "title": "GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation", "type": "paper", "arxiv": "2410.06158", "year": 2024, "venue": null, "org": "ByteDance Research", "url": null, "code": null, "project": "https://gr2-manipulation.github.io", "topics": [ "vla", "world-models" ], "tags": [ "real", "arm", "video-pretraining", "generalist" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Pretrained to generate future video on 38M clips, then fine-tuned to predict frames and actions together; reports 97.7% average success over 100+ real tabletop tasks and roughly double GR-1's success in unseen scenes.", "takeaway_zh": "先在 3800 万段视频上预训练未来帧生成，再在机器人数据上微调，同时预测视频帧与动作轨迹；报告在 100 多个真机桌面任务上平均成功率 97.7%，在未见场景中成功率约为 GR-1 的两倍。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2410.06158.html", "labs": [ "bytedance" ], "code_license": null }, { "id": "arxiv-2412.10345", "title": "TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies", "type": "paper", "arxiv": "2412.10345", "year": 2024, "venue": "ICLR 2025", "org": "UMD / Microsoft Research", "url": null, "code": "https://github.com/umd-huang-lab/tracevla", "project": "https://tracevla.github.io/", "topics": [ "vla" ], "tags": [ "real", "sim", "arm", "visual-prompting", "open-weights" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Draws the robot's recent motion traces on the input image ('visual trace prompting') and fine-tunes OpenVLA on 150K trajectories; reports ~10% gains on SimplerEnv and 3.5x on real WidowX tasks; 4B variant too.", "takeaway_zh": "把机器人近期的运动轨迹画在输入图像上（“视觉轨迹提示”），并在 15 万条轨迹上微调 OpenVLA；报告在 SimplerEnv 上提升约 10%、真实 WidowX 任务上提升 3.5 倍，另有基于 Phi-3-Vision 的 4B 精简版。", "github_stars": 76, "page": "https://embodied-ai-radar.com/e/arxiv-2412.10345.html", "labs": [ "microsoft" ], "code_license": "MIT" }, { "id": "arxiv-2505.11917", "title": "OneTwoVLA: A Unified Vision-Language-Action Model with Adaptive Reasoning", "type": "paper", "arxiv": "2505.11917", "year": 2025, "venue": "ICLR 2026", "org": "Tsinghua / Shanghai Qi Zhi / Shanghai AI Lab / Fudan / Spirit AI", "url": null, "code": "https://github.com/Fanqi-Lin/OneTwoVLA", "project": "https://one-two-vla.github.io", "topics": [ "vla" ], "tags": [ "real", "arm", "reasoning", "long-horizon", "hri" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "One model that decides when to think and when to act: it reasons at key moments (subtask done, error, human input) and otherwise acts on its latest reasoning; co-trained with synthetic reasoning-rich VL data.", "takeaway_zh": "单一模型自主决定何时“想”、何时“做”：在子任务完成、出错或人类插话等关键时刻进行推理，其余时间依据最新推理结果生成动作；并与合成的推理型视觉-语言数据共同训练。", "github_stars": 242, "page": "https://embodied-ai-radar.com/e/arxiv-2505.11917.html", "labs": [ "shanghai-ai-lab", "tsinghua", "qizhi", "fudan" ], "code_license": "MIT" }, { "id": "arxiv-2502.05855", "title": "DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control", "type": "paper", "arxiv": "2502.05855", "year": 2025, "venue": "CoRL 2025", "org": "Midea Group / East China Normal University", "url": null, "code": null, "project": "https://dex-vla.github.io/", "topics": [ "vla" ], "tags": [ "real", "sim", "cross-embodiment", "diffusion-policy", "bimanual", "dexterous-hand" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Pairs a 2B Qwen2-VL with a ~1B plug-in diffusion action expert with one head per robot, trained in three stages (cross-embodiment, alignment, task); reports shirt folding without task-specific tuning and 97.3 on LIBERO.", "takeaway_zh": "将 2B 的 Qwen2-VL 与约 1B 参数、按机器人分头输出的可插拔扩散动作专家结合，分三阶段训练（跨本体预训练、本体对齐、任务适配）；报告无需任务专门微调即可叠衬衫，LIBERO 平均 97.3。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2502.05855.html", "labs": [ "midea", "ecnu" ], "code_license": null }, { "id": "arxiv-2409.12514", "title": "TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation", "type": "paper", "arxiv": "2409.12514", "year": 2024, "venue": null, "org": "East China Normal University / Midea Group", "url": null, "code": "https://github.com/liyaxuanliyaxuan/TinyVLA", "project": "https://tiny-vla.github.io/", "topics": [ "vla" ], "tags": [ "real", "sim", "arm", "bimanual", "efficient", "diffusion-policy" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A compact VLA built from a small multimodal model (up to 1.4B) with LoRA and a diffusion policy head, without large-scale robot pretraining; reports higher real-robot success than OpenVLA and ~20x faster inference.", "takeaway_zh": "由小型多模态模型（至多 1.4B）加 LoRA 微调与扩散策略头构成的紧凑 VLA，无需大规模机器人预训练；报告真机成功率高于 OpenVLA，推理速度约快 20 倍。", "github_stars": 96, "page": "https://embodied-ai-radar.com/e/arxiv-2409.12514.html", "labs": [ "midea", "ecnu" ], "code_license": "MIT" }, { "id": "arxiv-2502.14420", "title": "ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model", "type": "paper", "arxiv": "2502.14420", "year": 2025, "venue": "EMNLP 2025", "org": "Midea Group / East China Normal University / Shanghai University / Beijing Innovation Center of Humanoid Robotics / Tsinghua", "url": null, "code": "https://github.com/tutujingyugang1/ChatVLA_public", "project": "https://chatvla.github.io", "topics": [ "vla" ], "tags": [ "real", "arm", "vlm", "mixture-of-experts" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Keeps a VLA able to chat: phased alignment training plus a mixture-of-experts split to fight forgetting and task interference; reports strong VQA/MMMU scores and beats OpenVLA on 25 real tasks.", "takeaway_zh": "让 VLA 保留“对话”能力：通过分阶段对齐训练和混合专家结构缓解遗忘与任务干扰；论文报告在保持 VQA/MMMU 等多模态理解成绩的同时，在 25 个真实操作任务上优于 OpenVLA。", "github_stars": 15, "page": "https://embodied-ai-radar.com/e/arxiv-2502.14420.html", "labs": [ "tsinghua", "midea", "ecnu" ], "code_license": "MIT" }, { "id": "arxiv-2505.21432", "title": "Hume: Introducing System-2 Thinking in Visual-Language-Action Model", "type": "paper", "arxiv": "2505.21432", "year": 2025, "venue": null, "org": "Shanghai AI Lab / SJTU / Fudan / AgiBot", "url": null, "code": "https://github.com/hume-vla/hume", "project": "https://hume-vla.github.io", "topics": [ "vla" ], "tags": [ "real", "sim", "dual-system", "flow-matching" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Dual-system VLA: a value-query head scores sampled action-chunk candidates (System 2, ~4 Hz) and a light cascaded denoiser refines it (System 1, ~90 Hz); reports gains over π0 on LIBERO, SIMPLER and real robots.", "takeaway_zh": "双系统 VLA：价值查询头对多组候选动作块打分并择优（System 2，约 4 Hz），轻量级级联去噪策略再将其细化为流畅动作（System 1，约 90 Hz）；论文报告在 LIBERO、SIMPLER 和真机上均优于 π0。", "github_stars": 150, "page": "https://embodied-ai-radar.com/e/arxiv-2505.21432.html", "labs": [ "shanghai-ai-lab", "sjtu", "agibot", "fudan" ], "code_license": "" }, { "id": "arxiv-2511.05275", "title": "TwinVLA: Data-Efficient Bimanual Manipulation with Twin Single-Arm Vision-Language-Action Models", "type": "paper", "arxiv": "2511.05275", "year": 2025, "venue": "ICLR 2026", "org": "Yonsei University / Microsoft Research", "url": null, "code": "https://github.com/jellyho/TwinVLA", "project": "https://jellyho.github.io/TwinVLA/", "topics": [ "vla" ], "tags": [ "real", "sim", "bimanual" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Builds a bimanual VLA by composing two copies of a pretrained single-arm VLA instead of pretraining on bimanual data; reports beating a similar-size RDT-1B and narrowing the gap to π0 on real and sim tasks.", "takeaway_zh": "不做双臂预训练，而是把两份预训练单臂 VLA 组合成协同的双臂 VLA；论文报告在真实与仿真双臂任务上优于同规模 RDT-1B，并缩小了与依赖大量双臂数据的 π0 的差距。", "github_stars": 22, "page": "https://embodied-ai-radar.com/e/arxiv-2511.05275.html", "labs": [ "microsoft" ], "code_license": "MIT" }, { "id": "arxiv-2507.01016", "title": "VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers", "type": "paper", "arxiv": "2507.01016", "year": 2025, "venue": "ICCV 2025", "org": "Shanghai AI Lab / SJTU", "url": null, "code": "https://github.com/xiaoxiao0406/VQ-VLA", "project": null, "topics": [ "vla" ], "tags": [ "real", "sim", "arm", "action-tokenizer" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Replaces OpenVLA's per-dimension action bins with a convolutional residual VQ-VAE tokenizer trained on 100x more (real + synthetic) data; reports higher LIBERO-90 and real Franka success and ~2.8x faster inference.", "takeaway_zh": "用卷积残差 VQ-VAE 动作分词器（以百倍规模的真实+合成数据训练）替换 OpenVLA 的逐维离散分箱；论文报告 LIBERO-90 和真实 Franka 任务成功率提升，推理速度约快 2.8 倍。", "github_stars": 134, "page": "https://embodied-ai-radar.com/e/arxiv-2507.01016.html", "labs": [ "shanghai-ai-lab", "sjtu" ], "code_license": "MIT" }, { "id": "arxiv-2510.19430", "title": "GigaBrain-0: A World Model-Powered Vision-Language-Action Model", "type": "paper", "arxiv": "2510.19430", "year": 2025, "venue": null, "org": "GigaAI", "url": null, "code": "https://github.com/open-gigaai/giga-brain-0", "project": "https://gigabrain0.github.io", "topics": [ "vla" ], "tags": [ "real", "bimanual", "mobile-manipulation", "world-model-data", "flow-matching", "edge-deployment" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Trains a VLA largely on world-model-generated data (re-rendered real videos, new viewpoints, sim2real, human-to-robot video); reports gains over π0 on dexterous and mobile tasks, plus a 402M variant for Jetson Orin.", "takeaway_zh": "VLA 训练大量依赖世界模型生成的数据（真实视频重渲染、新视角、仿真转真实、人类视频转机器人）；论文报告在灵巧与移动操作任务上优于 π0，并提供可在 Jetson Orin 上运行的 4.02 亿参数小模型。", "github_stars": 2674, "page": "https://embodied-ai-radar.com/e/arxiv-2510.19430.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2602.12099", "title": "GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning", "type": "paper", "arxiv": "2602.12099", "year": 2026, "venue": null, "org": "GigaBrain Team", "url": null, "code": "https://github.com/open-gigaai/giga-brain-0", "project": "https://gigabrain05m.github.io", "topics": [ "vla", "world-models", "learning" ], "tags": [ "real", "bimanual", "rl", "world-model", "long-horizon" ], "robot_types": [ "arm" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Post-trains a VLA (GigaBrain-0.5, 10k+ h of robot data) with RL conditioned on a video world model's predictions (RAMP); reports ~30% over the RECAP baseline on laundry folding, box packing and espresso making.", "takeaway_zh": "在 GigaBrain-0.5（预训练于 1 万多小时机器人操作数据）基础上，用以视频世界模型预测为条件的强化学习（RAMP）做后训练；论文报告在叠衣服、装箱和制作浓缩咖啡等难任务上比 RECAP 基线提升约 30%。", "github_stars": 2674, "page": "https://embodied-ai-radar.com/e/arxiv-2602.12099.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2509.00576", "title": "Galaxea Open-World Dataset and G0 Dual-System VLA Model", "type": "paper", "arxiv": "2509.00576", "year": 2025, "venue": null, "org": "Galaxea", "url": null, "code": "https://github.com/OpenGalaxea/GalaxeaVLA", "project": null, "topics": [ "vla", "datasets" ], "tags": [ "real", "mobile-manipulation", "bimanual", "dual-system", "flow-matching", "dataset" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "~500 h / 100K trajectories on one mobile bimanual robot (R1 Lite) across 50 real scenes, plus G0: a VLM planner over a flow-matching VLA; reports that single-embodiment pretraining matters most for few-shot transfer.", "takeaway_zh": "在单一移动双臂机器人（R1 Lite）上采集约 500 小时、10 万条轨迹、覆盖 50 个真实场景，并提出 G0：VLM 规划器 + 流匹配 VLA 执行器的双系统模型；论文报告单本体预训练对少样本迁移最关键。", "github_stars": 822, "page": "https://embodied-ai-radar.com/e/arxiv-2509.00576.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2505.03673", "title": "RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration", "type": "paper", "arxiv": "2505.03673", "year": 2025, "venue": null, "org": "BAAI / Peking University", "url": null, "code": "https://github.com/FlagOpen/RoboOS", "project": null, "topics": [ "vla" ], "tags": [ "real", "multi-robot", "cross-embodiment", "planning", "system" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Open-source 'brain–cerebellum' stack: a RoboBrain MLLM plans and assigns tasks across heterogeneous robots, per-robot skill libraries execute, and a shared spatial/temporal/robot memory keeps them in sync.", "takeaway_zh": "开源的“大脑–小脑”分层系统：RoboBrain 多模态大模型负责跨异构机器人的任务规划与分配，各机器人上的技能库负责执行，共享的空间/时间/本体记忆保持多机协同。", "github_stars": 634, "page": "https://embodied-ai-radar.com/e/arxiv-2505.03673.html", "labs": [ "pku" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.30051", "title": "From Reasoning to Action: Benchmarking Frontier AI Models on LIBERO-10 Robot Manipulation Tasks", "type": "paper", "arxiv": "2610.30051", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/IRVLUTD/LLM_LIBERO", "project": "https://irvlutd.github.io/LLM-LIBERO/", "topics": [ "vla", "manipulation" ], "tags": [ "benchmark", "sim", "llm", "tool-use", "zero-shot", "reasoning" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Six frontier general-purpose models drive a LIBERO-10 arm through tool calls (end-effector poses, gripper), no robot training; best reported success is 44%. More reasoning effort usually helps but costs more.", "takeaway_zh": "让六个前沿通用大模型通过工具调用（指定末端位姿与夹爪开合）直接控制 LIBERO-10 机械臂，无任何机器人专门训练；报告的最高成功率为 44%。提高推理强度通常有帮助，但推理成本更高。", "github_stars": 10, "page": "https://embodied-ai-radar.com/e/arxiv-2610.30051.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.31427", "title": "FastHarness: Experience-Driven Review Scheduling for Vision-Language-Action Policies", "type": "paper", "arxiv": "2610.31427", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/FastHarness/fast-harness", "project": null, "topics": [ "vla", "manipulation" ], "tags": [ "sim", "agentic", "harness", "llm", "failure-recovery", "frozen-policy" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Harness where a frontier LLM reviews a frozen π0.5 policy less often as experience builds up and focuses on recovery after errors; reports ~57% fewer reviewer tokens and higher success on four sim tasks.", "takeaway_zh": "让前沿大模型监督冻结的 π0.5 策略的执行框架：随着经验积累逐步减少审查频率，并把监督集中在出错后的恢复上；报告在四个仿真任务上审查 token 平均减少约 57%，成功率也更高。", "github_stars": 11, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31427.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.25874", "title": "Vision-Language-Delta-Action: Learning Compact Deltas as Actionable Transition Interfaces for Robotic Manipulation", "type": "paper", "arxiv": "2610.25874", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/baidu-baige/LoongForge/tree/master/cookbook/loongforge-vlda", "project": null, "topics": [ "vla", "world-models", "manipulation" ], "tags": [ "latent-dynamics", "action-refinement", "open-framework" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Between direct action prediction and full video prediction: VLDA learns a compact latent 'Delta' of task-relevant state change and uses it to seed action denoising. Built on Baidu's open LoongForge.", "takeaway_zh": "介于直接预测动作与完整预测未来画面之间：VLDA 学习一个紧凑的隐变量 Delta 表示与任务相关的状态变化，并用它引导动作去噪；实现于百度开源训练框架 LoongForge。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25874.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.32102", "title": "Coupling Execution Horizon and Sampling Depth in Flow-Matching VLA Policies", "type": "paper", "arxiv": "2610.32102", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://jiabingyang01.github.io/Horizon-Depth-VLA/", "topics": [ "vla", "manipulation" ], "tags": [ "deployment", "inference", "flow-matching", "training-free", "sim", "real", "arm" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Deployment knob for flow-matching VLAs: fewer solver steps hurt under fast replanning but help with long open-loop chunks. Tuning both together, training-free, reports higher success and lower latency for π0/π0.5/GR00T.", "takeaway_zh": "流匹配 VLA 的部署调参发现：求解步数少在频繁重规划时会降低精度，但在长时开环执行动作块时反而有利。报告在 π0、π0.5、GR00T 上联合调整两者，无需训练即可提高成功率并降低推理延迟。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.32102.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.35832", "title": "Profiling a Frontier Agent with EBench: From Direct Control to Harness with Policy", "type": "paper", "arxiv": "2610.35832", "year": 2026, "venue": null, "org": "InternRobotics", "url": null, "code": null, "project": "https://internrobotics.github.io/EBench-Astra-Tech-Report/", "topics": [ "vla", "manipulation" ], "tags": [ "benchmark", "llm", "harness", "evaluation", "frontier-model", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Report: GPT-6-Astra drives a robot on EBench via waypoints or by calling a VLA/WAM policy. Direct control leads on short tasks, trails on long-horizon and contact-rich ones; calling a policy cuts token cost.", "takeaway_zh": "评测报告：在 EBench 上让 GPT-6-Astra 直接输出路径点控制机器人，或通过执行框架调用 VLA/WAM 策略。直接控制在短时任务上领先，但在长时程与接触密集任务上落后；调用策略可显著减少 token 开销。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.35832.html", "labs": [ "shanghai-ai-lab" ], "code_license": null }, { "id": "arxiv-2610.35626", "title": "Robot-Aligned 3D Gaussian Splatting for Precise Reaching with Wrist-Camera VLA Policies", "type": "paper", "arxiv": "2610.35626", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/001-Wang/PrecisionReach-GS", "project": "https://001-wang.github.io/PrecisionReach-GS/", "topics": [ "vla", "perception" ], "tags": [ "gaussian-splatting", "wrist-camera", "data-augmentation", "real", "arm" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Renders wrist-camera views from a 3D Gaussian Splatting scan of the real workspace to fine-tune π0.5 for precise reaching in e-waste disassembly; reports 80% vs. 58.7% with 150 teleop demos.", "takeaway_zh": "用真实工作台的 3D 高斯泼溅重建渲染腕部相机训练视角，微调 π0.5 以完成电子垃圾拆解中的精确抵达；报告成功率 80%，而 150 条遥操作示教为 58.7%，人工采集不到 10 分钟。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2610.35626.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.33074", "title": "AGQ: Action-Guided Ultra-Low-Bit Quantization for Vision-Language-Action Models", "type": "paper", "arxiv": "2610.33074", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Qiuxu00/AGQ", "project": null, "topics": [ "vla", "manipulation" ], "tags": [ "quantization", "compression", "deployment", "edge", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Mixed-precision quantization for VLAs that sets each weight matrix's bit width by how much it affects actions; reports 92.7% LIBERO success at 3 bits (above AWQ and GPTQ) and 70.5% at 2.5 bits.", "takeaway_zh": "面向 VLA 的混合精度量化：根据每个权重矩阵对输出动作的影响分配比特数；报告在 3 比特下 LIBERO 成功率 92.7%（高于 AWQ 与 GPTQ），2.5 比特下仍有 70.5%。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2610.33074.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.34356", "title": "RynnVLA-Latent: Scaling Robotic Manipulation Foundation Models with 3D-Grounded Latent Actions", "type": "paper", "arxiv": "2610.34356", "year": 2026, "venue": null, "org": "Alibaba DAMO Academy", "url": null, "code": "https://github.com/alibaba-damo-academy/RynnVLA-Latent", "project": "https://alibaba-damo-academy.github.io/RynnVLA-Latent.github.io/", "topics": [ "vla", "manipulation", "learning" ], "tags": [ "latent-actions", "pretraining", "human-video", "flow-matching", "cross-embodiment" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Pretrains a 4B VLA on 3D-grounded latent actions mined from 30K hours of human, robot and sim video, then adapts to real robot actions. Reports 98.8% on LIBERO and 88.6% on RoboTwin 2.0.", "takeaway_zh": "从 3 万小时人类、机器人与仿真视频中学习带 3D 约束的连续潜在动作（RynnLAM），用于预训练 4B 参数 VLA，再适配到具体机器人动作空间。报告 LIBERO 98.8%、RoboTwin 2.0 平均 88.6%。", "github_stars": 16, "page": "https://embodied-ai-radar.com/e/arxiv-2610.34356.html", "labs": [ "alibaba" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.35051", "title": "VISTA: Active Exploration for Self-Improvement in Embodied Agents", "type": "paper", "arxiv": "2610.35051", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/AgenticEI/VISTA", "project": "https://agenticei.github.io/VISTA-page/", "topics": [ "vla", "learning" ], "tags": [ "harness", "agent", "self-improvement", "libero", "robotwin", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Meta-agent that improves a robot's harness by exploring: keeps a lineage graph of harness versions and runs new rollouts to probe failures before editing. Reports more success with fewer rollouts than static reflection.", "takeaway_zh": "通过主动探索改进机器人 harness 的元智能体：维护 harness 版本谱系图，先主动生成新回放探查失败与恢复行为再修改；报告在 LIBERO-Pro、RoboCasa、RoboTwin 上比静态反思用更少仿真回放取得更高成功率。", "github_stars": 6, "page": "https://embodied-ai-radar.com/e/arxiv-2610.35051.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.31569", "title": "Echo: Coupled Action and Task Status Signal Denoising for Vision Language Action Models", "type": "paper", "arxiv": "2610.31569", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/lyfeng001/Echo-Sim", "project": "https://lyfeng001.github.io/Echo/", "topics": [ "vla", "manipulation" ], "tags": [ "vla", "failure-detection", "recovery", "diffusion", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Teaches a VLA to know when it is done or failing: stop and failure signals are two extra action dimensions, denoised faster than the actions so they can guide them; no frame-level progress labels. Real-robot tests.", "takeaway_zh": "让 VLA 自己判断任务完成或失败：把停止与失败信号作为动作块的两个额外维度，与动作一起去噪且去噪更快，从而引导后续动作生成；无需逐帧进度标注，在真机操作实验中验证。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31569.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.27794", "title": "Multi-View Latent Action Pretraining for Cross-Embodiment Robot Learning from Videos", "type": "paper", "arxiv": "2610.27794", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://learnsyslab.github.io/multiview_latent_action_pretraining/", "topics": [ "vla", "learning" ], "tags": [ "latent-actions", "multi-view", "cross-embodiment", "human-video", "flow-matching" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Latent action model that fuses synchronized camera views to resolve depth and occlusion. Reports 17% better 6D action decodability than single-view and +13.5% policy success (vs +6.2%); human-hand demos help on hardware.", "takeaway_zh": "融合多路同步相机视角的潜在动作模型（LAM），用交叉注意力解决深度与遮挡问题；报告 6D 动作可解码性比单视角高 17%，下游策略成功率提升 13.5%（单视角为 6.2%），真机上用人手演示也比纯动作策略高 20%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.27794.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.37219", "title": "Vision-Language-Action or World-Action? Just Action Models", "type": "paper", "arxiv": "2610.37219", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/y0urOy/JAM", "project": "https://y0uroy.github.io/JAM", "topics": [ "vla", "world-models" ], "tags": [ "vla", "wam", "foresight-loss", "libero", "robotwin" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Splits the VLA vs WAM debate into backbone, future loss and inference machinery: a foresight loss predicts frozen future features, no video generation. Reports beating FastWAM on LIBERO-Plus (64.5% vs 55.3%).", "takeaway_zh": "把 VLA 与世界动作模型（WAM）之争拆成骨干初始化、未来预测损失和推理时是否保留预测模块三个选择：用预测冻结未来特征的前瞻损失，训练与推理都不生成视频；报告在 LIBERO-Plus 上 64.5%，FastWAM 为 55.3%。", "github_stars": 11, "page": "https://embodied-ai-radar.com/e/arxiv-2610.37219.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.25961", "title": "PaceVLA: Adaptive Temporal Action Quantization in Vision-Language-Action Models", "type": "paper", "arxiv": "2610.25961", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://pacevla.github.io/", "topics": [ "vla", "manipulation" ], "tags": [ "vla", "inference-speed", "action-chunking", "real", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Lets a VLA pick, at each step, between fine and temporally coarser action decoders: redundant motion runs faster while precision phases stay fine-grained. Tested on 3 sim benchmarks and 6 real tasks.", "takeaway_zh": "让 VLA 在每一步自行选择细粒度或时间上更粗的动作解码器：冗余动作阶段加速执行，精细接触阶段保持细粒度控制；在 3 个仿真基准和 6 个真实任务上验证效率提升且成功率不降。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25961.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.36013", "title": "OmniBrain-0: A Unified Agentic Model for Embodied Intelligence", "type": "paper", "arxiv": "2610.36013", "year": 2026, "venue": null, "org": "Xiaomi", "url": null, "code": null, "project": "https://public-bots.github.io/OmniBrain-0/", "topics": [ "vla", "navigation" ], "tags": [ "embodied-agent", "planning", "distillation", "self-improvement", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Xiaomi's compact embodied agent that plans, tracks progress and recovers from failures across manipulation and navigation, distilled from stronger teachers with a rubric-driven self-improvement loop.", "takeaway_zh": "小米具身智能团队的轻量统一具身智能体：在操作与导航中负责任务理解、规划、进度跟踪和失败恢复，由更强的教师模型多教师在线蒸馏而来，并配有基于评分细则的自我改进数据飞轮。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36013.html", "labs": [ "xiaomi" ], "code_license": null }, { "id": "arxiv-2610.37666", "title": "ThinkWorld: World Imagination Reinforces Chain-of-Dynamics Reasoning", "type": "paper", "arxiv": "2610.37666", "year": 2026, "venue": null, "org": "NVIDIA", "url": null, "code": null, "project": "https://thinkworld-project.github.io/", "topics": [ "vla", "world-models", "learning" ], "tags": [ "vla", "human-video", "motion-tokens", "reinforcement-learning", "cross-embodiment", "real" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-10-28", "takeaway_en": "VLA that reasons in discrete motion tokens (a chain of dynamics) shared across embodiments; predicted futures are scored against what actually happened, so action-free human videos give RL signal. Sim and real tests.", "takeaway_zh": "VLA 用跨本体共享的离散运动 token 链（Chain-of-Dynamics）表达动作思考；预测每条链导致的未来并与真实结果比对来强化，使没有动作标注的人类视频也能提供监督。在仿真与真机上验证人到机器人的迁移。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.37666.html", "labs": [ "nvidia" ], "code_license": null }, { "id": "arxiv-2610.37215", "title": "Retrieve to Act: Motion Primitive Graph Retrieval for Robust VLA Execution", "type": "paper", "arxiv": "2610.37215", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://retrieve2act.github.io/r2a/", "topics": [ "vla", "manipulation" ], "tags": [ "test-time", "retrieval", "robustness", "ood", "gnn", "real", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Test-time add-on for frozen VLAs: a GNN retrieves future motion primitives from a graph of training demos and gates them into the actions. Reports up to +12% in sim and +40% real (GR00T N1.7, π0.5, StableVLA).", "takeaway_zh": "面向冻结 VLA 的测试时插件：把训练示范组织成运动基元图，用 GNN 定位当前执行并检索后续基元，再按维度门控融合到预测动作中；报告在 GR00T N1.7、π0.5 等上仿真最高 +12%、真机 +40%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.37215.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.37381", "title": "PDRVLA: A Vision-Language-Action Model with Physical Dynamics Representations", "type": "paper", "arxiv": "2610.37381", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "vla", "world-models" ], "tags": [ "vla", "physical-dynamics", "future-prediction", "ecot", "public-data", "sim" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-10-28", "takeaway_en": "Light module turns VLM features into 'physical tokens' trained to predict future RGB, depth and flow features (with world-model supervision); no online video generation. Reports 99.3% LIBERO, 93.6% RoboTwin 2.0.", "takeaway_zh": "轻量物理动力学模块从视觉-语言特征中提取“物理 token”，通过预测未来 RGB、深度和光流特征（并辅以冻结视频世界模型监督）来学习，推理时无需在线生成视频；仅用公开数据训练，报告 LIBERO 99.3%、RoboTwin 2.0 93.6%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.37381.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.31423", "title": "FocusVLA: Future-Intent Guided Dynamic View Routing for Vision-Language-Action Models", "type": "paper", "arxiv": "2610.31423", "year": 2026, "venue": "CoRL 2026", "org": "ISCAS", "url": null, "code": null, "project": null, "topics": [ "vla", "manipulation" ], "tags": [ "vla", "multi-view", "view-routing", "intent-prediction", "real", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Finds VLAs over-rely on the wrist camera; predicts future interaction intent from past actions to route between global and wrist views per stage. Reports +10.1 pp over π0.5 on RoboTwin 2.0 and 71.7% → 85.0% real.", "takeaway_zh": "发现现有 VLA 过度依赖腕部相机；根据历史动作预测未来交互意图，按任务阶段在全局与腕部视角间动态路由；报告在 RoboTwin 2.0 上比 π0.5 高 10.1 个百分点，真机成功率由 71.7% 提升到 85.0%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31423.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.31670", "title": "Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success", "type": "paper", "arxiv": "2610.31670", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/yunfeixie233/sensorimotor-alignment", "project": "https://yunfeixie233.github.io/s2/", "topics": [ "vla", "datasets" ], "tags": [ "evaluation", "representation", "offline-metric", "vlm-backbone", "wam" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-29", "takeaway_en": "S² is a training-free offline metric of how well a model's representation aligns with physical motion. Reports it tracks robot task success across model families and can rank VLM backbones before VLA fine-tuning.", "takeaway_zh": "S² 是一种无需训练的离线指标，衡量模型视觉表征与物理运动几何的对齐程度（把柏拉图表征假说扩展到机器人）；报告它与多类模型的真实任务成功率正相关，可在微调成 VLA 之前筛选 VLM 骨干。", "github_stars": 9, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31670.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.29329", "title": "Plan First, Act Later: Streaming Reasoning for Executable Memory in Robot Control", "type": "paper", "arxiv": "2610.29329", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://qinghangliu.github.io/pal-webpage/", "topics": [ "vla", "manipulation" ], "tags": [ "memory", "long-horizon", "hierarchical", "vlm-planner", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-29", "takeaway_en": "PAL has a high-level VLM write future-relevant subtasks into an executable memory while watching, then hand the current one to a low-level VLA. Reports 56.05% vs 44.51% on RoboMME and 60.83% on 4 real tasks.", "takeaway_zh": "PAL 让高层 VLM 在观察过程中把未来需要的子任务提前写入可执行记忆（subtask_list），执行时再挑出当前子任务交给底层 VLA；报告在 RoboMME 上成功率 56.05%（最强基线 44.51%），4 个真实任务总体 60.83%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.29329.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.38661", "title": "Quantize by Doing: Task-Guided Mixed-Precision Quantization of Vision-Language-Action Models", "type": "paper", "arxiv": "2610.38661", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/jiuyixu25/QbD-VLA", "project": null, "topics": [ "vla", "learning" ], "tags": [ "quantization", "efficiency", "deployment", "libero", "mixed-precision", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "Assigns VLA bit widths from short closed-loop task-success tests, not calibration sensitivity: ~4-bit weights on LIBERO across four models. Honest caveat: allocations transfer to a real arm for X-VLA but fail for π0.5.", "takeaway_zh": "用短时闭环任务成功率（而非校准数据敏感度）为 VLA 各组件分配权重/激活位宽，并考虑组件间相互影响；在四个 VLA 与 LIBERO 全部套件上约 4 比特权重仍保持较强表现。作者如实指出：仿真选出的方案在真机上对 X-VLA 无损，对 π0.5 却失败。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2610.38661.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.34186", "title": "HuGe: Scaling Humanoid Generalist Vision-Language-Action Models with Egocentric Experience", "type": "paper", "arxiv": "2610.34186", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/yliu-cs/HuGe", "project": "https://yliu-cs.github.io/HuGe", "topics": [ "vla", "locomotion" ], "tags": [ "humanoid", "egocentric-video", "human-data", "whole-body-control", "unitree-g1", "cross-embodiment", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "Humanoid VLA family pre-trained on 2,000+ h of egocentric human and robot data, predicting latent motion tokens for a whole-body controller; uses pseudo-proprioception and arm re-rendering to bridge the embodiment gap.", "takeaway_zh": "人形机器人通用 VLA 系列：在 2,000 多小时第一视角人类与机器人数据上预训练，输出潜在动作 token 驱动预训练的全身控制器并附手部信号；通过仿真回放构造伪本体感知、重渲染手臂外观缩小人-机差异，报告 HumanoidArena 最优，并在 Unitree G1 上验证人类数据增益。", "github_stars": 14, "page": "https://embodied-ai-radar.com/e/arxiv-2610.34186.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.37917", "title": "What Actually Matters? A Paired Evaluation of Inference Settings for π0.5 on BEHAVIOR-1K", "type": "paper", "arxiv": "2610.37917", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://rubenhammele.github.io/behavior2026", "topics": [ "vla", "datasets" ], "tags": [ "evaluation", "pi0.5", "behavior-1k", "inference-strategies", "household", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "Paired test of previously proposed inference and data-collection tricks on all 100 BEHAVIOR-1K tasks with a π0.5 based on the 2025 challenge winner (Q-score 0.39): the tricks do not transfer uniformly.", "takeaway_zh": "在 BEHAVIOR-1K 全部 100 个任务上，以基于 2025 BEHAVIOR 挑战赛冠军方案微调的 π0.5（公开测试集 Q 分 0.39）为对象，对已有推理与数据采集技巧做配对评测；结论是这些技巧在新环境与任务上的效果并不一致，提醒小规模结论不宜直接外推。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.37917.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.40026", "title": "RoboSeed: Multidimensional Evaluation of Vision-Language-Action and World-Action Models: Capability, Resource Affordability, and Research Evidence", "type": "paper", "arxiv": "2610.40026", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/PrimitiveX/RoboSeed", "project": null, "topics": [ "vla", "world-models", "datasets" ], "tags": [ "evaluation", "benchmark", "vla", "wam", "reproducibility", "compute-cost" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "Rates VLA and world-action models on five axes (capability, training and deployment cost, verified research value, reproducibility), not success rate alone; withholds overall scores when evidence is missing.", "takeaway_zh": "多维度评测 VLA 与世界-动作模型（WAM）：除任务能力外，还衡量下游训练成本、部署成本、经本地验证的研究价值和复现证据，按不同算力预算给出 0–100 画像；证据缺失时明确标注、不给总分，并开源评分代码与机器可读证据。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2610.40026.html", "labs": [], "code_license": "" }, { "id": "arxiv-2605.30877", "title": "Wall-OSS-0.5 Technical Report", "type": "paper", "arxiv": "2605.30877", "year": 2026, "venue": null, "org": "X Square Robot", "url": null, "code": "https://github.com/X-Square-Robot/wall-x", "project": "https://x2robot.com/en/oss", "topics": [ "vla" ], "tags": [ "real", "open-weights", "cross-embodiment", "pretraining" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-02", "takeaway_en": "Wall-OSS-0.5: open 4B VLA on a Qwen2.5-VL-3B backbone, pretrained across 20+ embodiments; reports 60.5% task progress after fine-tuning on 15 real tasks, against 43.0% for π0.5.", "takeaway_zh": "Wall-OSS-0.5：自变量机器人（X Square Robot）开源的 40 亿参数 VLA 模型，基于 Qwen2.5-VL-3B 视觉语言主干，在 20 余种机器人形态上预训练；报告在 15 个真机任务上微调后平均任务进度 60.5%，π0.5 为 43.0%。", "github_stars": 1320, "page": "https://embodied-ai-radar.com/e/arxiv-2605.30877.html", "labs": [ "x-square" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2607.12659", "title": "EagleVLA: Towards Onboard Real-Time Robot Control via Foresight-Aligned Asynchronous Inference", "type": "paper", "arxiv": "2607.12659", "year": 2026, "venue": "CoRL 2026", "org": "PKU SEC Lab", "url": null, "code": "https://github.com/PKU-SEC-Lab/EagleVLA", "project": null, "topics": [ "vla" ], "tags": [ "real", "onboard", "jetson", "async-inference", "spotlight" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-02", "takeaway_en": "EagleVLA: asynchronous VLA inference for low-power onboard chips such as Jetson Orin, tackling perception-execution misalignment and slow reaction; a CoRL 2026 Spotlight, formerly titled Jetson-PI.", "takeaway_zh": "EagleVLA：面向 Jetson Orin 等低功耗机载设备的 VLA 异步推理方法，针对异步执行带来的感知-执行错位与反应慢两个问题；CoRL 2026 Spotlight，曾用名 Jetson-PI。", "github_stars": 45, "page": "https://embodied-ai-radar.com/e/arxiv-2607.12659.html", "labs": [ "pku" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2607.14236", "title": "Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection", "type": "paper", "arxiv": "2607.14236", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/y-wng/lift", "project": "https://lift-policy.github.io/", "topics": [ "vla", "tactile" ], "tags": [ "real", "force", "post-training", "contact", "spotlight" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-02", "takeaway_en": "LIFT: grafts a reactive force-driven action expert onto a pretrained, vision-driven VLA, keeping its output unchanged at initialization, so post-training gains contact reactivity; CoRL 2026 Spotlight.", "takeaway_zh": "LIFT：在以视觉为主的预训练 VLA 旁接入由力觉驱动的反应式动作专家，初始化时保持原策略输出不变，使后训练获得接触反应能力，应对遮挡、深度歧义等接触阶段难题；CoRL 2026 Spotlight。", "github_stars": 16, "page": "https://embodied-ai-radar.com/e/arxiv-2607.14236.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.30111", "title": "Automating the Design of Embodied Agent Architectures", "type": "paper", "arxiv": "2606.30111", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/jianzhou0420/AgentCanvas", "project": "https://jianzhou0420.github.io/agentcanvas", "topics": [ "vla", "navigation" ], "tags": [ "agents", "architecture-search", "memory", "planning" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Tests whether agent architecture search, so far used on text agents, transfers to perceptual embodied agents; AgentCanvas makes perception, memory, planning and action wiring searchable.", "takeaway_zh": "研究原用于文本智能体的智能体架构搜索（AAS）能否迁移到有感知的具身智能体；提出 AgentCanvas，把感知、记忆、规划与动作模块的组合方式变成可搜索的设计空间。", "github_stars": 56, "page": "https://embodied-ai-radar.com/e/arxiv-2606.30111.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.08653", "title": "FiberTune: Preserving Action-Fiber Visual Residuals in Vision-Language-Action Fine-Tuning", "type": "paper", "arxiv": "2606.08653", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/fibertune/FiberTune", "project": "https://fibertune.github.io/", "topics": [ "vla" ], "tags": [ "fine-tuning", "representation", "no-inference-overhead" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "FiberTune: action-supervised VLA fine-tuning can collapse visual structure the action loss barely constrains; a training-time objective keeps those residuals, with no added inference cost.", "takeaway_zh": "FiberTune：动作监督微调会压缩动作损失约束较弱的视觉结构（沿局部动作纤维的残差坍塌）；提出训练期目标函数，对齐冻结的视觉教师以保留这些残差，不增加推理开销。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2606.08653.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.41609", "title": "VLA Policies Do Not Know When Not to Act: Unintended Task Execution and Start Suppression", "type": "paper", "arxiv": "2610.41609", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "safety", "evaluation", "libero", "gr00t", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Given an empty instruction, all six tested VLA checkpoints still moved in every LIBERO-Goal episode; five often finished an unrequested task. A 'do not move' prefix stopped none; an action-similarity gate held five.", "takeaway_zh": "指令为空时，六个 VLA 检查点在 LIBERO-Goal 的每个回合都仍会启动，其中五个常常完成了未被要求的任务。给指令加“无任务就别动”前缀对六个都无效；比较首个动作块与空指令动作块的门控在五个上能让机器人保持不动。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.41609.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.41216", "title": "TinyAstral: Transferring Self-Evolving Harnesses to Tiny Generalist Manipulation Agents", "type": "paper", "arxiv": "2610.41216", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Garfield-kh/TinyAstral", "project": "https://garfield-kh.github.io/TinyAstral/", "topics": [ "vla" ], "tags": [ "harness", "recursive-self-improvement", "distillation", "robotwin" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Evolves a frozen teacher's harness by recursive self-improvement with paired statistical tests, then distills a 4B student into it. Reports RoboTwin 33.4% → 63.6% for the teacher, 62.8% for the student.", "takeaway_zh": "先冻结开放权重教师模型，用递归自我改进演化其 harness（每次修改须通过配对统计检验），再把 4B 学生模型蒸馏进同一 harness。报告 RoboTwin 上教师成功率由 33.4% 升到 63.6%，学生达 62.8%，加速器时间约为教师的五分之一。", "github_stars": 11, "page": "https://embodied-ai-radar.com/e/arxiv-2610.41216.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.42053", "title": "REA: A Routed Embodied Agent Runtime for Asynchronous Brain-Cerebellum Control", "type": "paper", "arxiv": "2610.42053", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://mikeeeeeeeeeeeeee.github.io/REA/", "topics": [ "vla" ], "tags": [ "agent-runtime", "routing", "libero-pro", "humanoid", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Runtime pairing a slow brain with fast policies under one rule: no running policy waits on the brain. Reports 95.9% over 3,000 LIBERO-Pro episodes vs 94.7% for the best single baseline, with 17% less control time.", "takeaway_zh": "让慢速“大脑”（基础模型）与快速“小脑”（VLA、全身控制）协同的统一运行时，核心规则是运行中的策略绝不等待大脑。用路由记忆与边界触发的判定门替代逐次求助；报告 LIBERO-Pro 3000 回合成功率 95.9%（最佳单体基线 94.7%），控制时间减少 17%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.42053.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.12007", "title": "REACT: Rolling Denoising and Dual Decoupling for Reactive Robot Control with VLA Models", "type": "paper", "arxiv": "2610.12007", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/houlong-xiong/REACT", "project": "https://react-vla.github.io", "topics": [ "vla" ], "tags": [ "flow-matching", "action-chunking", "reactive-control" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Instead of regenerating action chunks from scratch, keeps a persistent action buffer with staggered flow timesteps, so flow-based VLAs get more reactive while preserving long-horizon context.", "takeaway_zh": "CoRL 2026 Spotlight。不再每次从头生成动作块，而是维护一个流时间步错开的持久动作缓冲区，让基于流的 VLA 更具反应性，同时保留长时域上下文。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2610.12007.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2608.01402", "title": "Demystifying When and Why VLAs Fail in Contact-Rich Tasks and How to Fix Them", "type": "paper", "arxiv": "2608.01402", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "contact-rich", "failure-analysis", "force" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Studies why VLA models struggle in contact-rich manipulation and separates two failure modes: precision failures, where policies struggle to produce small corrective actions, and force failures.", "takeaway_zh": "CoRL 2026 Spotlight。分析 VLA 模型为何难以完成接触丰富的操作任务，并区分两类失败模式：难以输出接触时所需微小修正动作的精度失败，以及力失败。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.01402.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.40799", "title": "CaPtain: A Pipeline for Post-Training Code-as-Policy Robot Agents", "type": "paper", "arxiv": "2610.40799", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/waltstephen/CaPtain", "project": null, "topics": [ "vla", "learning" ], "tags": [ "code-as-policy", "post-training", "rl", "libero", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Turns a small open VLM into a code-as-policy agent: SFT, then multi-turn RL with a physical critic. Reports Qwen3.5-4B going from 0% to 73.8% on held-out LIBERO; code, data and weights released.", "takeaway_zh": "把小型开放 VLM 训练成“代码即策略”机器人智能体的开源流水线：先 SFT，再做多轮 RL，并用读取仿真特权状态的物理评论家为每次请求分配贡献。报告 Qwen3.5-4B 在留出的 LIBERO 上由 0% 升到 73.8%，并公开代码、数据和权重。", "github_stars": 93, "page": "https://embodied-ai-radar.com/e/arxiv-2610.40799.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.42141", "title": "RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory", "type": "paper", "arxiv": "2610.42141", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/roboarchitect-ai/RoboArchitect", "project": "https://roboarchitect-ai.github.io/", "topics": [ "vla" ], "tags": [ "agent-harness", "frozen-policy", "coding-agent" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "A coding model reads rollouts of a frozen policy and rewrites its harness, with no weight updates. Reports average success rising from 9.7% to 43.3% on unseen scenes and 12.7% to 40.2% on unseen tasks.", "takeaway_zh": "让编码模型观察冻结策略的执行轨迹、诊断失败并反复改写外围 harness，不更新任何权重。报告在未见场景上平均成功率从 9.7% 升到 43.3%，未见任务上从 12.7% 升到 40.2%。", "github_stars": 10, "page": "https://embodied-ai-radar.com/e/arxiv-2610.42141.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.39280", "title": "Cross-Controller Transfer via In-Context Behavior Adaptation", "type": "paper", "arxiv": "2610.39280", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://controller-transfer.github.io", "topics": [ "vla" ], "tags": [ "cross-embodiment", "controllers", "context-encoder", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Pools robot data across six controller types by showing the policy examples of commanded actions and the motions they caused. Reports 33 points better average success than naive pooling in real experiments.", "takeaway_zh": "跨六类控制器共享机器人数据：把“指令动作与实际运动”的交互样例编码为上下文，让同一策略在线适应。真实实验报告平均成功率比直接混合数据高 33 个百分点。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.39280.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.39543", "title": "From Reasoning to Action: Consequence-Grounded Alignment for Vision-Language-Action Models", "type": "paper", "arxiv": "2610.39543", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://onlyxingxingzyx.github.io/CoRAct/", "topics": [ "vla" ], "tags": [ "chain-of-thought", "noise-optimization", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRAct turns a VLA's stated movement and waypoints into checkable signals, then steers action noise toward agreeing with them, so actions align with the reasoning. Tested on RoboCasa, VLABench and real tasks.", "takeaway_zh": "CoRAct 把 VLA 思维链中的运动意图与路点变成可验证信号，再用一致性奖励优化初始动作噪声，让动作真正对齐推理，而不只是以推理为条件。在 RoboCasa、VLABench 与真实任务上测试。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.39543.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.38199", "title": "Embodied at Sea: Imitation Learning for Multi-Task Underwater Manipulation", "type": "paper", "arxiv": "2610.38199", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://lair-research-nyu.github.io/Embodied-at-Sea/", "topics": [ "vla" ], "tags": [ "underwater", "field-robotics", "act", "real" ], "robot_types": [ "underwater" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Trains VLA models on 1,616 underwater episodes and grades 1,260 pool rollouts, best model 93.3% including partial successes. In a field case study at a WWII wreck, single-task ACT beat generalist SmolVLA 47.9% to 7.9%.", "takeaway_zh": "用 1616 条水下数据训练 VLA，并在水池评估 1260 次，最佳模型成功率 93.3%（含部分成功）。在二战沉船实地案例中，单任务 ACT 以 47.9% 对 7.9% 明显胜过通用的 SmolVLA。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.38199.html", "labs": [], "code_license": null }, { "id": "arxiv-2601.12993", "title": "Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization", "type": "paper", "arxiv": "2601.12993", "year": 2026, "venue": "CoRL 2026", "org": "BeingBeyond", "url": null, "code": "https://github.com/BeingBeyond/Being-H", "project": "https://research.beingbeyond.com/being-h05", "topics": [ "vla" ], "tags": [ "cross-embodiment", "human-centric", "humanoid-data" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. Cross-embodiment VLA that treats human interaction traces as a shared language, pre-trained on UniHand-2.0: over 35,000 hours of multimodal data across 30 robot embodiments.", "takeaway_zh": "CoRL 2026 Oral。跨形态 VLA，把人类交互轨迹当作通用“母语”，在 UniHand-2.0 上预训练：超过 3.5 万小时多模态数据，覆盖 30 种机器人形态。", "github_stars": 1157, "page": "https://embodied-ai-radar.com/e/arxiv-2601.12993.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2607.15275", "title": "RoboTTT: Context Scaling for Robot Policies", "type": "paper", "arxiv": "2607.15275", "year": 2026, "venue": "CoRL 2026", "org": "NVIDIA", "url": null, "code": null, "project": "https://research.nvidia.com/labs/gear/robottt/", "topics": [ "vla" ], "tags": [ "long-context", "test-time-training", "one-shot-imitation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. Test-Time-Training policies scale visuomotor context to 8K timesteps, three orders of magnitude longer, without added inference latency, enabling one-shot imitation from human video.", "takeaway_zh": "CoRL 2026 Oral。Test-Time-Training 策略把视觉运动上下文扩展到 8K 时间步，长出三个数量级且不增加推理延迟，由此支持从人类视频单次模仿等新能力。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.15275.html", "labs": [ "nvidia" ], "code_license": null }, { "id": "arxiv-2604.15483", "title": "$π_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities", "type": "paper", "arxiv": "2604.15483", "year": 2026, "venue": "CoRL 2026", "org": "Physical Intelligence", "url": null, "code": null, "project": "https://www.pi.website/blog/pi07", "topics": [ "vla" ], "tags": [ "generalist", "foundation-model", "steerable", "cross-embodiment" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. A steerable generalist foundation model: follows diverse instructions in unseen scenes and reaches zero-shot cross-embodiment tasks such as folding laundry, with no task-specific finetuning.", "takeaway_zh": "CoRL 2026 Oral。可引导的通用基础模型：在未见环境中执行多样指令，跨形态零样本完成叠衣等任务，无需任务专用微调。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.15483.html", "labs": [ "physical-intelligence" ], "code_license": null }, { "id": "arxiv-2609.16864", "title": "TEMPO: Learning Temporal Context for Dynamic Robot Manipulation", "type": "paper", "arxiv": "2609.16864", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/tempo-robot/TEMPO", "project": "https://tempo-robot.github.io/", "topics": [ "vla" ], "tags": [ "dynamic-manipulation", "temporal-context" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. Argues VLAs fail at dynamic tasks because one observation brings motion ambiguity and state aliasing, and adds learned temporal context to resolve both.", "takeaway_zh": "CoRL 2026 Oral。指出 VLA 难以应对动态任务，是因为单帧观测带来运动歧义与状态混淆，并通过学习时序上下文同时解决这两个问题。", "github_stars": 23, "page": "https://embodied-ai-radar.com/e/arxiv-2609.16864.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2603.03596", "title": "MEM: Multi-Scale Embodied Memory for Vision Language Action Models", "type": "paper", "arxiv": "2603.03596", "year": 2026, "venue": "CoRL 2026", "org": "Physical Intelligence", "url": null, "code": null, "project": "https://pi.website/research/memory", "topics": [ "vla" ], "tags": [ "memory", "long-horizon" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Gives VLAs memory at several timescales: abstract long-term state (which recipe stages are done) plus short-term recall of recent events and occluded objects.", "takeaway_zh": "CoRL 2026 Spotlight。为 VLA 提供多时间尺度记忆：长期记住抽象状态（如菜谱进行到哪一步），短期记住近期事件与被遮挡物体。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.03596.html", "labs": [ "physical-intelligence" ], "code_license": null }, { "id": "arxiv-2606.12299", "title": "Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering", "type": "paper", "arxiv": "2606.12299", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "language-steering", "evaluation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Similar prompts can make VLAs behave very differently, so this framework interactively searches for language sequences that raise closed-loop task performance.", "takeaway_zh": "CoRL 2026 Spotlight。语义相近的指令可能让 VLA 行为迥异，该框架通过交互搜索语言序列来提升闭环任务表现。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12299.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.12965", "title": "EmbodiSteer: Steering Embodiment-Agnostic Visuomotor Policies with Joint-Space Guidance for Zero-Shot Cross-Embodiment Deployment", "type": "paper", "arxiv": "2606.12965", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/frankWang67/EmbodiSteer", "project": "https://frankwang67.github.io/EmbodiSteer-Page", "topics": [ "vla" ], "tags": [ "cross-embodiment", "guidance" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Training-free steering that makes Cartesian, embodiment-agnostic policies aware of the deployed robot's body, using joint-space guidance for constraints such as whole-body collision avoidance.", "takeaway_zh": "CoRL 2026 Spotlight。无需训练的引导框架：用关节空间引导让与本体无关的笛卡尔策略感知实际机器人本体，处理全身避碰等约束。", "github_stars": 17, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12965.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2602.17659", "title": "When Vision Overrides Language: Evaluating and Mitigating Counterfactual Failures in VLAs", "type": "paper", "arxiv": "2602.17659", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/yuffish/VLA-CF", "project": "https://vla-cf.github.io/", "topics": [ "vla" ], "tags": [ "counterfactual", "benchmark", "language-following" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. VLAs can ignore language and follow vision shortcuts from dataset bias. Introduces LIBERO-CF, a counterfactual benchmark for language following.", "takeaway_zh": "CoRL 2026 Spotlight。VLA 可能忽略语言、沿用数据偏差形成的视觉捷径。论文提出反事实基准 LIBERO-CF，用于评测语言遵循能力。", "github_stars": 14, "page": "https://embodied-ai-radar.com/e/arxiv-2602.17659.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2609.02546", "title": "ZETA: A Controlled Study of Zero-Shot Cross-Embodiment VLA Transfer for Tabletop Manipulation", "type": "paper", "arxiv": "2609.02546", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "cross-embodiment", "evaluation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. A controlled study of zero-shot VLA transfer that isolates embodiment change from tasks, scenes and protocols, and separates strict zero-shot from looser definitions.", "takeaway_zh": "CoRL 2026 Spotlight。对 VLA 零样本跨形态迁移的对照研究：将本体变化与任务、场景、流程隔离，并把严格零样本与宽松定义区分开。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.02546.html", "labs": [], "code_license": null }, { "id": "arxiv-2506.07530", "title": "BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation", "type": "paper", "arxiv": "2506.07530", "year": 2025, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/ustcwhy/BitVLA", "project": null, "topics": [ "vla" ], "tags": [ "1-bit", "edge-deployment" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. A fully native 1-bit VLA with ternary weights, built on BitNet b1.58 2B4T, designed for edge devices instead of relying on post-hoc compression.", "takeaway_zh": "CoRL 2026 Spotlight。原生 1-bit VLA，权重三值化，基于 BitNet b1.58 2B4T，面向边缘设备设计，而非依赖事后压缩。", "github_stars": 175, "page": "https://embodied-ai-radar.com/e/arxiv-2506.07530.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2607.29687", "title": "Diagnosing Compositional Generalization in Sequential Robot Tasks", "type": "paper", "arxiv": "2607.29687", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/YixiaoWang7/Diagnosing_Compositional_Generalization_Robot", "project": "https://yixiaowang7.github.io/Diagnosing_Compositional_Generalization_Robot_Page/", "topics": [ "vla" ], "tags": [ "compositional-generalization", "evaluation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Splits the generalization gap of sequential instruction-following policies into marginal, compositional and context-action shift, viewed through instruction-space coverage.", "takeaway_zh": "CoRL 2026 Spotlight。从指令空间覆盖出发，把序列任务策略的泛化差距分解为边际偏移、组合偏移与上下文-动作偏移。", "github_stars": 7, "page": "https://embodied-ai-radar.com/e/arxiv-2607.29687.html", "labs": [], "code_license": "" }, { "id": "arxiv-2607.05369", "title": "GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks", "type": "paper", "arxiv": "2607.05369", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/graph-robots/graph-as-policy", "project": "https://graph-robots.github.io/gap", "topics": [ "vla" ], "tags": [ "agentic", "harness", "variational-automation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. A multi-agent self-learning harness aimed at Variational Automation, industrial tasks with more object variation than fixed automation, which need sustained reliability.", "takeaway_zh": "CoRL 2026 Spotlight。面向“可变自动化”的多智能体自学习框架：这类工业任务的物体变化大于固定自动化，且要求持续可靠。", "github_stars": 187, "page": "https://embodied-ai-radar.com/e/arxiv-2607.05369.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.19419", "title": "Playful Agentic Robot Learning", "type": "paper", "arxiv": "2606.19419", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Playful-RATs/rats", "project": "https://playful-rats.github.io/", "topics": [ "vla" ], "tags": [ "agentic", "code-as-policy", "play" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. RATs, teams of embodied coding agents, propose exploratory tasks and learn skills through self-directed play before any downstream task arrives.", "takeaway_zh": "CoRL 2026 Spotlight。具身编程智能体团队 RATs 在下游任务到来之前，自行提出探索任务，并通过自主玩耍学习技能。", "github_stars": 143, "page": "https://embodied-ai-radar.com/e/arxiv-2606.19419.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.41663", "title": "ResMerge: Residual Policy Learning and Merging for Continual Adaptation of Pre-trained Robot Policies", "type": "paper", "arxiv": "2610.41663", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/ResMerge/resmerge-official", "project": "https://resmerge.github.io/", "topics": [ "vla" ], "tags": [ "vla", "continual-learning", "residual-rl" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Adapts a frozen pre-trained policy with residual RL instead of new demos, then merges compatible residuals to cut stored parameters; tested on LIBERO and a real Franka.", "takeaway_zh": "在冻结的预训练策略上用残差强化学习适配新任务（无需新演示），再合并相容的残差以减少参数；在 LIBERO 与真实 Franka 上验证。", "github_stars": 4, "page": "https://embodied-ai-radar.com/e/arxiv-2610.41663.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.41009", "title": "STAR: Steering Action Representations with Proprioception for VLA Inference", "type": "paper", "arxiv": "2610.41009", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/PKU-VaLuE-Lab/STAR", "project": null, "topics": [ "vla" ], "tags": [ "vla", "inference-time" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Training-free tweak for pre-trained VLAs: an EMA over late-layer updates keeps object-aware directions near the action output, mixed less when proprioception shows fast dynamics; reports +4.0 pts success.", "takeaway_zh": "面向预训练 VLA 的免训练方法：对后层残差更新做 EMA，让靠近动作输出的表征保留物体感知方向，并按近期本体感知状态调节混合强度（动态变化快时更保守）；报告平均任务成功率提升 4.0 个百分点。", "github_stars": 5, "page": "https://embodied-ai-radar.com/e/arxiv-2610.41009.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.40373", "title": "HiFOR: Hierarchical Foresight with Action-Guided World Grounding for Vision-Language-Action Model", "type": "paper", "arxiv": "2610.40373", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "vla", "foresight", "world-model" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Jointly predicts short-term states, semantic milestones and goal states across horizons, then lets the action query retrieve only the relevant parts of that imagined future.", "takeaway_zh": "跨时间尺度联合预测短期状态、语义里程碑与目标状态，再由动作查询从中检索相关的未来表征。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.40373.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.35604", "title": "RoboDoctor: Training-Free Recursive Self-Improvement for Robot Policies", "type": "paper", "arxiv": "2610.35604", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/THUSI-Lab/RoboDoctor", "project": "https://robodoctor-rsi.github.io/", "topics": [ "vla" ], "tags": [ "agents", "policy-execution", "self-improvement" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Keeps model weights fixed and adapts during execution: an agent diagnoses, intervenes and stores linked experience records, revising how the policy is used rather than the policy.", "takeaway_zh": "不改模型权重，在执行中适配：智能体负责诊断、干预并保存关联的经验记录，修订的是策略的使用方式而非策略本身。", "github_stars": 19, "page": "https://embodied-ai-radar.com/e/arxiv-2610.35604.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2611.02497", "title": "RoboColosseum: Evaluating Physical Intelligence at Scale In-The-Wild", "type": "paper", "arxiv": "2611.02497", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/RoboColosseum/robocolosseum-client", "project": null, "topics": [ "vla" ], "tags": [ "benchmark", "evaluation", "real", "fine-tuning" ], "robot_types": [], "highlight": true, "rating": 2, "added": "2026-11-03", "takeaway_en": "Blinded pairwise trials at 124 sites compare VLAs and frontier agents. Rankings barely agree across robots (W = 0.24), and open rankings poorly predict results after fine-tuning; no policy finishes a G1 task.", "takeaway_zh": "在 124 个站点做盲测两两对比，横跨四种机器人评测 VLA 与前沿智能体。不同机器人的排名几乎不一致（Kendall W = 0.24），开箱排名也难以预测微调后的表现；G1 人形上没有策略完成任何任务。", "github_stars": 4, "page": "https://embodied-ai-radar.com/e/arxiv-2611.02497.html", "labs": [], "code_license": "" }, { "id": "arxiv-2611.01560", "title": "Democratizing Robot-Use Agents: A Unified Harness with Open Models", "type": "paper", "arxiv": "2611.01560", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Open-RUA/uni-robot-agent", "project": "https://openrua.ai/uni-robot-agent", "topics": [ "vla" ], "tags": [ "agent", "harness", "open-weights", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "uni-robot-agent: an open harness for robot-use agents on open-weight models. It reports a RoboDojo score of 31.39 with an off-the-shelf Qwen3.8 model, above the published GPT-6 Astra-RoboProbe result of 28.97.", "takeaway_zh": "uni-robot-agent：面向开放权重模型的机器人操作智能体（Robot-use Agent）框架，便于继续训练。作者报告现成的 Qwen3.8 模型在 RoboDojo 上得 31.39 分，高于已发表的 GPT-6 Astra-RoboProbe 的 28.97 分。", "github_stars": 323, "page": "https://embodied-ai-radar.com/e/arxiv-2611.01560.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.01112", "title": "Auriga Technical Report: A Task-Agnostic Agent Harness for Zero-Shot Embodied Manipulation", "type": "paper", "arxiv": "2611.01112", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/lose4578/Auriga", "project": "https://auriga.stelledge.com/", "topics": [ "vla" ], "tags": [ "agent", "harness", "benchmark", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "The LLM decides; the harness adds calibrated 3D measurements, trajectory previews and stop-on-divergence checks. Reports first place on RoboDojo with 57.7% success, over double the reference harness.", "takeaway_zh": "任务决策交给大模型，框架提供标定后的三维测量、轨迹预览和偏差即停的执行检查。作者报告在 RoboDojo 榜单排名第一，成功率 57.7%，是同模型参考框架的两倍多。", "github_stars": 34, "page": "https://embodied-ai-radar.com/e/arxiv-2611.01112.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.03103", "title": "VLALagBench: Understanding When and Why VLA Inference Latency Matters", "type": "paper", "arxiv": "2611.03103", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "benchmark", "latency", "evaluation", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Benchmark of VLA latency sensitivity across tasks, object speeds and execution modes: 100 ms can nearly halve success on moving targets. An empirical model then predicts success from limited calibration data.", "takeaway_zh": "评测 VLA 对推理延迟的敏感度，覆盖不同任务、物体速度与同步/异步执行：100 ms 延迟就可能让移动目标任务的成功率几乎减半。作者还给出经验模型，用少量标定数据预测成功率。代码尚待发布。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03103.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.02660", "title": "HeLM: Hierarchical Explicit Language Memory for Non-Markovian Manipulation", "type": "paper", "arxiv": "2611.02660", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/kimspacehuni/HeLM", "project": "https://kimspacehuni.github.io/HeLM/", "topics": [ "vla" ], "tags": [ "memory", "real", "hierarchical" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Gives a VLA working memory by keeping task history as explicit text, updated only when events are detected. Tested on 10 real non-Markovian tasks, including ones that need context across episodes.", "takeaway_zh": "用显式文本保存任务历史、只在检测到事件时更新，为 VLA 增加工作记忆。在 10 个真实的非马尔可夫任务上测试，其中包括需要跨回合保持上下文的任务。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2611.02660.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2601.18692", "title": "A Pragmatic VLA Foundation Model", "type": "paper", "arxiv": "2601.18692", "year": 2026, "venue": null, "org": "Robbyant", "url": null, "code": "https://github.com/Robbyant/lingbot-vla", "project": "https://technology.robbyant.com/lingbot-vla", "topics": [ "vla" ], "tags": [ "real", "bimanual", "open-weights", "open-data", "cross-embodiment" ], "robot_types": [ "arm" ], "highlight": true, "rating": 2, "added": null, "takeaway_en": "LingBot-VLA: an Apache-2.0 foundation model trained on about 20,000 hours of real data from nine dual-arm setups, tested on four platforms with 100 tasks each; code, base model and benchmark data are released.", "takeaway_zh": "LingBot-VLA：Apache-2.0 开源的基础模型，用九种双臂构型约 2 万小时真实数据训练，并在四个平台各 100 项任务上评测；代码、基座模型和评测数据一并开放。", "github_stars": 1883, "page": "https://embodied-ai-radar.com/e/arxiv-2601.18692.html", "labs": [ "robbyant" ], "code_license": "Apache-2.0" }, { "id": "isaac-gr00t-n1-7", "title": "Isaac GR00T N1.7", "type": "code", "arxiv": null, "year": 2026, "venue": null, "org": "NVIDIA", "url": null, "code": "https://github.com/NVIDIA/Isaac-GR00T", "project": "https://huggingface.co/blog/nvidia/gr00t-n1-7", "topics": [ "vla", "locomotion" ], "tags": [ "real", "humanoid", "dexterous", "open-weights", "vlm", "diffusion-policy", "human-demonstrations" ], "robot_types": [ "hand", "humanoid" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Commercially licensed 3B-parameter humanoid VLA: a Cosmos-Reason2 backbone plans and a diffusion transformer outputs motor commands. NVIDIA reports training on about 20,854 hours of human egocentric video.", "takeaway_zh": "可商用的 30 亿参数人形 VLA：Cosmos-Reason2 主干负责推理规划，扩散 Transformer 输出电机指令。NVIDIA 称用约 20,854 小时人类第一视角视频训练。", "github_stars": 8346, "page": "https://embodied-ai-radar.com/e/isaac-gr00t-n1-7.html", "labs": [ "nvidia" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2510.03342", "title": "Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer", "type": "paper", "arxiv": "2510.03342", "year": 2025, "venue": null, "org": "Google DeepMind", "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "real", "vlm", "cross-embodiment", "reasoning" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Tech report on a multi-embodiment VLA plus the ER 1.5 reasoning model: Motion Transfer learns across varied robot data, and actions interleave with natural-language reasoning so the robot can think before acting.", "takeaway_zh": "技术报告：跨本体 VLA 与具身推理模型 ER 1.5。Motion Transfer 让模型从不同机器人的数据中学习，动作与自然语言推理交替进行，使机器人先想后做。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2510.03342.html", "labs": [ "google-deepmind" ], "code_license": null }, { "id": "arxiv-2602.10098", "title": "VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model", "type": "paper", "arxiv": "2602.10098", "year": 2026, "venue": "ECCV 2026", "org": null, "url": null, "code": "https://github.com/ginwind/VLA-JEPA", "project": "https://ginwind.github.io/VLA-JEPA/", "topics": [ "vla", "world-models" ], "tags": [ "jepa", "latent-world-model", "pretraining", "libero", "open-weights" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "JEPA-style VLA pretraining: future frames are only a latent target and the student sees the current observation alone, so it avoids leakage and learns dynamics rather than pixel changes; then fine-tune an action head.", "takeaway_zh": "JEPA 式 VLA 预训练：未来帧只作为潜空间监督目标，学生网络只看当前观测，避免信息泄漏，学动力学而非像素变化；之后再微调动作头。", "github_stars": 629, "page": "https://embodied-ai-radar.com/e/arxiv-2602.10098.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2603.19199", "title": "FASTER: Rethinking Real-Time Flow VLAs", "type": "paper", "arxiv": "2603.19199", "year": 2026, "venue": "NeurIPS 2026", "org": "HKU", "url": null, "code": "https://github.com/hku-sail/FASTER", "project": null, "topics": [ "vla" ], "tags": [ "real-time", "flow-matching", "latency", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Shows reaction time hinges on time to first action, then uses a schedule that spends denoising on near-term actions: one step for the immediate reaction, about 10× faster for π0.5 and X-VLA; demoed on table tennis.", "takeaway_zh": "指出反应速度取决于首动作耗时，并让去噪优先照顾近期动作：即时反应只需一步，π0.5 和 X-VLA 上约快 10 倍；真机演示包括乒乓球。", "github_stars": 165, "page": "https://embodied-ai-radar.com/e/arxiv-2603.19199.html", "labs": [ "hku" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2411.02359", "title": "DeeR-VLA: Dynamic Inference of Multimodal Large Language Models for Efficient Robot Execution", "type": "paper", "arxiv": "2411.02359", "year": 2024, "venue": null, "org": null, "url": null, "code": "https://github.com/yueyang130/DeeR-VLA", "project": null, "topics": [ "vla" ], "tags": [ "efficiency", "early-exit", "calvin" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Multi-exit VLA that stops at the smallest model fit for the moment, with budgets for power, latency and GPU memory; on CALVIN it reports 5.2–6.5× lower LLM compute and 2–6× less memory without hurting performance.", "takeaway_zh": "多出口 VLA：按当前情境停在够用的最小模型，并按功耗、时延和显存预算设定退出条件；在 CALVIN 上报告 LLM 计算量降低 5.2–6.5 倍、显存降低 2–6 倍，性能不降。", "github_stars": 129, "page": "https://embodied-ai-radar.com/e/arxiv-2411.02359.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2407.08693", "title": "Robotic Control via Embodied Chain-of-Thought Reasoning", "type": "paper", "arxiv": "2407.08693", "year": 2024, "venue": "CoRL 2024", "org": null, "url": null, "code": "https://github.com/MichalZawalski/embodied-CoT", "project": "https://embodied-cot.github.io", "topics": [ "vla" ], "tags": [ "reasoning", "chain-of-thought", "openvla" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Trains VLAs to reason through plans, subtasks, motions and visual features before acting; synthetic reasoning data gives OpenVLA a reported 28% success gain on hard generalization tasks with no extra robot data.", "takeaway_zh": "训练 VLA 在动作前依次推理计划、子任务、动作和视觉特征；用合成的推理数据，OpenVLA 在困难泛化任务上成功率提升 28%，无需额外机器人数据。", "github_stars": 420, "page": "https://embodied-ai-radar.com/e/arxiv-2407.08693.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2507.01925", "title": "A Survey on Vision-Language-Action Models: An Action Tokenization Perspective", "type": "paper", "arxiv": "2507.01925", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": "https://github.com/Psi-Robot/Awesome-VLA-Papers", "topics": [ "vla" ], "tags": [ "survey" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Survey that reads every VLA as one pipeline producing ever more actionable tokens, and reviews eight token types from language and code to trajectories, latents, raw actions and reasoning.", "takeaway_zh": "综述：把所有 VLA 看作生成越来越可执行 token 的同一流程，并梳理语言、代码、可供性、轨迹、目标状态、潜表示、原始动作和推理八类 token。", "github_stars": 583, "page": "https://embodied-ai-radar.com/e/arxiv-2507.01925.html", "labs": [], "code_license": "" }, { "id": "arxiv-2509.19012", "title": "Pure Vision Language Action (VLA) Models: A Comprehensive Survey", "type": "paper", "arxiv": "2509.19012", "year": 2025, "venue": null, "org": "Lanzhou University, NUS", "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "survey" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Survey of over three hundred studies with a taxonomy of autoregression, diffusion, reinforcement, hybrid and specialized VLAs, plus the datasets, benchmarks and simulators used to test them.", "takeaway_zh": "综述三百多项研究，把 VLA 分为自回归、扩散、强化学习、混合和专用几类，并介绍常用数据集、评测基准和仿真平台。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2509.19012.html", "labs": [ "nus" ], "code_license": null }, { "id": "arxiv-2611.05155", "title": "Asynchronous Robot Policies", "type": "paper", "arxiv": "2611.05155", "year": 2026, "venue": null, "org": "INSAIT", "url": null, "code": null, "project": "https://arp.insait.ai", "topics": [ "vla" ], "tags": [ "real", "arm", "latency" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Splits a large VLM that slowly updates a semantic memory from a light expert that acts at high rate, trained on staleness and delay; reports running the VLM at a tenth of its nominal rate with no significant loss.", "takeaway_zh": "把低频更新语义记忆的大型 VLM 与高频出动作的轻量专家解耦，并针对陈旧和延迟训练；报告 VLM 降到标称频率十分之一时性能无明显损失。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05155.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.04166", "title": "Say the Same, Act Differently: Diagnosing and Steering Text-Action Divergence in Reasoning VLAs", "type": "paper", "arxiv": "2611.04166", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://say-the-same.github.io/", "topics": [ "vla" ], "tags": [ "safety", "adversarial", "neurips" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Shows visual perturbations can change a reasoning VLA's action while its rationale stays word-for-word identical, tested on Alpamayo and InstructVLA, so monitors must check the action channel, not the text.", "takeaway_zh": "展示视觉扰动可以改变推理型 VLA 的动作，而解释文本一字不变（在 Alpamayo 和 InstructVLA 上测试），因此监控必须直接检查动作通道，不能只看文本。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.04166.html", "labs": [], "code_license": null }, { "id": "arxiv-2507.14049", "title": "EdgeVLA: Efficient Vision-Language-Action Models", "type": "paper", "arxiv": "2507.14049", "year": 2025, "venue": null, "org": "K-Scale Labs", "url": null, "code": "https://github.com/kscalelabs/evla", "project": null, "topics": [ "vla" ], "tags": [ "edge", "efficiency" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Speeds up VLA inference on edge devices by dropping the autoregressive end-effector prediction, reported as a 7x speedup, and by using small language models.", "takeaway_zh": "通过去掉末端位姿的自回归预测（报告提速 7 倍）并使用小型语言模型，加快 VLA 在边缘设备上的推理。", "github_stars": 109, "page": "https://embodied-ai-radar.com/e/arxiv-2507.14049.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2406.04339", "title": "RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation", "type": "paper", "arxiv": "2406.04339", "year": 2024, "venue": "NeurIPS 2024", "org": null, "url": null, "code": null, "project": "https://sites.google.com/view/robomamba-web", "topics": [ "vla" ], "tags": [ "mamba", "efficiency" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "An end-to-end VLA built on Mamba state space models, aiming for both robotic reasoning and cheap fine-tuning and inference.", "takeaway_zh": "基于 Mamba 状态空间模型的端到端 VLA，兼顾机器人推理能力和较低的微调与推理成本。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2406.04339.html", "labs": [], "code_license": null }, { "id": "arxiv-2503.07511", "title": "PointVLA: Injecting the 3D World into Vision-Language-Action Models", "type": "paper", "arxiv": "2503.07511", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "point-cloud", "3d" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Adds point cloud input to pretrained VLAs without retraining: freezes the action expert and injects 3D features only into blocks that a skip-block analysis finds less useful.", "takeaway_zh": "无需重训即可为预训练 VLA 加入点云输入：冻结动作专家，只向跳块分析认定作用较小的模块注入 3D 特征。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2503.07511.html", "labs": [], "code_license": null }, { "id": "arxiv-2412.11974", "title": "Emma-X: An Embodied Multimodal Action Model with Grounded Chain of Thought and Look-ahead Spatial Reasoning", "type": "paper", "arxiv": "2412.11974", "year": 2024, "venue": null, "org": null, "url": null, "code": "https://github.com/declare-lab/Emma-X", "project": null, "topics": [ "vla" ], "tags": [ "reasoning", "chain-of-thought" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A VLA with grounded chain of thought and look-ahead spatial reasoning, trained on a dataset built from BridgeV2 with 60,000 auto-annotated trajectories.", "takeaway_zh": "具备落地链式思考和前瞻空间推理的 VLA，基于 BridgeV2 构建并自动标注的 6 万条轨迹数据集训练。", "github_stars": 85, "page": "https://embodied-ai-radar.com/e/arxiv-2412.11974.html", "labs": [], "code_license": "" }, { "id": "arxiv-2510.24795", "title": "A Survey on Efficient Vision-Language-Action Models", "type": "paper", "arxiv": "2510.24795", "year": 2025, "venue": "TPAMI 2026", "org": null, "url": null, "code": null, "project": "https://evla-survey.github.io/", "topics": [ "vla" ], "tags": [ "survey", "efficiency" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A survey of efficient VLAs across the model, training and data pipeline, with a unified taxonomy of work on real-time inference, training cost and scalable data collection.", "takeaway_zh": "综述高效 VLA，贯穿模型、训练和数据全流程，用统一分类梳理实时推理、训练成本和可扩展数据采集方面的工作。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2510.24795.html", "labs": [], "code_license": null }, { "id": "arxiv-2502.21257", "title": "RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete", "type": "paper", "arxiv": "2502.21257", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "mllm", "affordance", "dataset" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Trains a multimodal language model for task planning, object affordance and trajectory prediction, using ShareRobot, a dataset labelled for all three.", "takeaway_zh": "训练多模态语言模型完成任务规划、物体可供性和轨迹预测，并配套标注了这三类信息的 ShareRobot 数据集。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2502.21257.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.03983", "title": "Efficient Long-Horizon Vision-Language-Action Models via Static-Dynamic Disentanglement", "type": "paper", "arxiv": "2602.03983", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "efficiency", "kv-cache" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "DySta splits visual input into static and dynamic tokens, keeping one copy of the static tokens and reusing their KV cache, to extend VLA context and speed up inference.", "takeaway_zh": "DySta 把视觉输入分成静态和动态 token，静态部分只保留一份并复用 KV 缓存，从而延长 VLA 上下文并加快推理。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.03983.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.04391", "title": "Human-to-Robot Transfer via Bridging Representation and Action Space in VLAs", "type": "paper", "arxiv": "2611.04391", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/jun-brro/h2rb-code", "project": "https://junhyeong-park.me/h2rb/", "topics": [ "vla" ], "tags": [ "real", "human-video", "fine-tuning" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "H2RB adapts a pretrained VLA from unpaired human and robot demonstrations by aligning observations on geometry and sharing action latents; reports object transfer score rising from 8.0 to 51.8 on real tasks.", "takeaway_zh": "H2RB 用非配对的人类与机器人示教微调预训练 VLA：按几何对齐观测，并共享动作潜变量；报告真机任务的物体迁移得分从 8.0 升至 51.8。", "github_stars": 4, "page": "https://embodied-ai-radar.com/e/arxiv-2611.04391.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2611.05058", "title": "Do Agentic Policies Need to Look at Every Step? Geometric Abstraction for Reasoning and Action", "type": "paper", "arxiv": "2611.05058", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Spectraorder/geometric-abstraction", "project": "https://geometric-abstraction.github.io", "topics": [ "vla" ], "tags": [ "real", "agentic", "llm", "3d" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Moves routine spatial reasoning from images to text: a locally refreshed 3D symbolic scene the language model reads directly, with vision only on demand; reports better multi-step completion and far fewer image queries.", "takeaway_zh": "把常规空间推理从图像转到文本：语言模型直接读取局部刷新的三维符号场景，仅在需要时看图；报告多步任务完成度提高，图像查询大幅减少。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05058.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2607.13597", "title": "Semantic Anchoring for Robotic Action Representations", "type": "paper", "arxiv": "2607.13597", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://xy02-05.github.io/SemanticMN", "topics": [ "vla" ], "tags": [ "vla", "representation", "generalization" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Finds that fine-tuning erodes the semantic structure VLAs inherit, and that this tracks task failure. A training-only anchor restores it, reporting up to +21.5% on out-of-distribution tasks.", "takeaway_zh": "发现微调会破坏 VLA 继承的语义结构，且这与任务失败同步。提出仅在训练时使用的语义锚定方法，报告分布外任务提升最高 21.5%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.13597.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.27472", "title": "PRTS: A Primitive Reasoning and Tasking System via Contrastive Representations", "type": "paper", "arxiv": "2604.27472", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla", "learning" ], "tags": [ "vla", "goal-conditioned-rl", "contrastive" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Reframes VLA pretraining as goal-conditioned RL: contrastive learning gives the model a sense of whether a language goal is reachable. Pretrained on 167B tokens; strongest on long-horizon tasks.", "takeaway_zh": "将 VLA 预训练重构为目标条件强化学习：用对比学习让模型判断语言目标是否可达。基于 1670 亿 token 预训练，在长时序任务上优势最明显。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.27472.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.22149", "title": "Meta-Ctrl: Guaranteed Plan Generation by Decoupling Syntactic and Semantic Constraints", "type": "paper", "arxiv": "2608.22149", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://metactrlg.github.io", "topics": [ "vla", "learning" ], "tags": [ "llm-planning", "constrained-decoding" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Constrained decoding for LLM planners: grounded 'meta-tokens' guarantee syntax and preconditions by construction and cut decoder memory from over 107 TB to under 2 GB.", "takeaway_zh": "面向 LLM 规划的约束解码：用接地的“元 token”从结构上保证语法与前置条件，并将解码内存从 107 TB 以上降到 2 GB 以内。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.22149.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.34270", "title": "Bayesian Active Learning for Intent Disambiguation in Interactive Robot Planning", "type": "paper", "arxiv": "2609.34270", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "hri", "clarification", "stl" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Treats clarifying questions as Bayesian active learning over Signal Temporal Logic specs, so the robot asks fewer questions and hands a formal planner a verifiable task.", "takeaway_zh": "把澄清提问视为信号时序逻辑规约上的贝叶斯主动学习，机器人提问更少，并向形式化规划器交付可验证的任务。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.34270.html", "labs": [], "code_license": null }, { "id": "arxiv-2503.12533", "title": "Being-0: A Humanoid Robotic Agent with Vision-Language Models and Modular Skills", "type": "paper", "arxiv": "2503.12533", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/BeingBeyond/being-0", "project": "https://beingbeyond.github.io/Being-0", "topics": [ "vla", "locomotion" ], "tags": [ "humanoid", "agent", "vlm" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Being-0: a humanoid agent that coordinates a foundation model, a vision-language model and modular skills to limit compounding errors on long tasks.", "takeaway_zh": "Being-0：协调基础模型、视觉语言模型与模块化技能的人形智能体，以减少长任务中的误差累积。", "github_stars": 62, "page": "https://embodied-ai-radar.com/e/arxiv-2503.12533.html", "labs": [], "code_license": "" }, { "id": "arxiv-2506.13751", "title": "LeVERB: Humanoid Whole-Body Control with Latent Vision-Language Instruction", "type": "paper", "arxiv": "2506.13751", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": "https://ember-center-berkeley.github.io/LeVERB-Website/", "topics": [ "vla", "locomotion" ], "tags": [ "humanoid", "vla", "whole-body" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Vision-language latent commands for humanoid whole-body control, moving VLAs beyond hand-made low-level action vocabularies and quasi-static tasks.", "takeaway_zh": "用视觉语言隐空间指令驱动人形全身控制，让 VLA 摆脱手工定义的底层动作词表和准静态任务的限制。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2506.13751.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.12263", "title": "$Ψ_0$: An Open Foundation Model Towards Universal Humanoid Loco-Manipulation", "type": "paper", "arxiv": "2603.12263", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "vla", "locomotion" ], "tags": [ "humanoid", "foundation-model", "open-source" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "An open foundation model for humanoid loco-manipulation that argues against co-training on mixed human and humanoid data because of their kinematic differences.", "takeaway_zh": "面向人形移动操作的开放基础模型，主张因运动学差异，不宜直接混合人类与人形数据共同训练。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.12263.html", "labs": [], "code_license": null }, { "id": "arxiv-2507.12440", "title": "EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos", "type": "paper", "arxiv": "2507.12440", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/RchalYang/EgoVLA_Release", "project": "https://rchalyang.github.io/EgoVLA", "topics": [ "vla", "manipulation" ], "tags": [ "human-video", "egocentric", "vla" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Trains VLAs on egocentric human videos, predicting wrist and hand actions, because human data scales beyond robot data and is richer in scenes and tasks.", "takeaway_zh": "用第一视角人类视频训练 VLA，预测手腕与手部动作；人类数据规模更大，场景和任务也更丰富。", "github_stars": 194, "page": "https://embodied-ai-radar.com/e/arxiv-2507.12440.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2611.03163", "title": "FineCraft: Enhancing VLA Models for Fine-Grained Manipulation", "type": "paper", "arxiv": "2611.03163", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/eqcy/FineCraft", "project": null, "topics": [ "vla", "manipulation" ], "tags": [ "vla", "challenge", "fine-manipulation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Fourth-place MetaFine Challenge entry: a task router picks between adapted DINOv2-DiT and π0.5 policies behind one interface, a practical recipe for adapting pretrained policies to precise tasks.", "takeaway_zh": "MetaFine 挑战赛第四名方案：任务路由在统一接口下选择微调后的 DINOv2-DiT 或 π0.5 策略，是把预训练策略适配到精细任务的实用做法。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03163.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.06918", "title": "From Pixels to Actions - Towards Native Vision-Language-Action Policies", "type": "paper", "arxiv": "2611.06918", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/EvolvingLMMs-Lab/NEO", "project": "https://weichenfan.github.io/NEO-VLA-Page/", "topics": [ "vla", "manipulation" ], "tags": [ "vla", "flow-matching", "encoder-free", "architecture" ], "robot_types": [], "highlight": true, "rating": 2, "added": "2026-11-05", "takeaway_en": "NEO-VLA drops the visual encoder: images and language evolve in one backbone and a flow-matching action expert reads several depths. Layer probing shows action signals often peak before the final visual layer.", "takeaway_zh": "NEO-VLA 去掉独立视觉编码器：图像与语言在同一主干中共同演化，流匹配动作专家从多个深度读取特征。逐层探测显示，对动作关键的信号往往在最终视觉层之前达到峰值。", "github_stars": 925, "page": "https://embodied-ai-radar.com/e/arxiv-2611.06918.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2611.06817", "title": "Open-HumanWAM: An Open Recipe for Scaling Human and Robot Experience for Embodied Foundation Models", "type": "paper", "arxiv": "2611.06817", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/DAGroup-PKU/HumanNet", "project": "https://dagroup-pku.github.io/open-humanscale/", "topics": [ "vla", "world-models", "datasets", "learning" ], "tags": [ "egocentric", "human-video", "world-action-model", "pretraining", "open-data" ], "robot_types": [], "highlight": true, "rating": 2, "added": "2026-11-05", "takeaway_en": "Opens a 50,000-hour annotated egocentric video corpus with pipelines and checkpoints. Reports a log-linear law between pretraining hours and robot success, and 84.6% on RoboTwin 2.0 Clean2Random.", "takeaway_zh": "开源 5 万小时带标注的第一视角视频语料及流程和权重。报告预训练时长与机器人成功率之间的对数线性规律，后训练后在 RoboTwin 2.0 Clean2Random 上达到 84.6%。", "github_stars": 343, "page": "https://embodied-ai-radar.com/e/arxiv-2611.06817.html", "labs": [], "code_license": "" }, { "id": "arxiv-2412.14058", "title": "What Matters in Building Vision-Language-Action Models for Generalist Robots", "type": "paper", "arxiv": "2412.14058", "year": 2024, "venue": null, "org": null, "url": null, "code": "https://github.com/Robot-VLAs/RoboVLMs", "project": "https://robovlms.github.io/", "topics": [ "vla" ], "tags": [ "vla", "empirical" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Systematic study of what actually drives VLA manipulation performance, from model design to data; introduces the RoboVLMs family.", "takeaway_zh": "系统研究哪些因素真正决定 VLA 操作性能（模型设计到数据），并提出 RoboVLMs 系列。", "github_stars": 491, "page": "https://embodied-ai-radar.com/e/arxiv-2412.14058.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2602.03310", "title": "RDT2: Exploring the Scaling Limit of UMI Data Towards Zero-Shot Cross-Embodiment Generalization", "type": "paper", "arxiv": "2602.03310", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/thu-ml/RDT2", "project": "https://rdt-robotics.github.io/rdt2/", "topics": [ "vla" ], "tags": [ "vla", "umi", "cross-embodiment" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "RDT2 builds a robot foundation model on a 7B-parameter VLM and tests how far UMI data scales toward zero-shot deployment on new embodiments.", "takeaway_zh": "RDT2 基于 70 亿参数 VLM 构建机器人基础模型，探索 UMI 数据规模能把零微调跨形态部署推到多远。", "github_stars": 810, "page": "https://embodied-ai-radar.com/e/arxiv-2602.03310.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2602.11236", "title": "ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning", "type": "paper", "arxiv": "2602.11236", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/amap-cvlab/ABot-Manipulation", "project": "https://amap-cvlab.github.io/ABot-Manipulation/", "topics": [ "vla" ], "tags": [ "vla", "cross-embodiment", "action-manifold" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "ABot-M0 pairs a data curation pipeline with action manifold learning for one model across hardware, the \"one brain, many forms\" goal.", "takeaway_zh": "ABot-M0 结合数据整理流程与动作流形学习，让一个模型跨不同硬件工作，即“一脑多形”。", "github_stars": 712, "page": "https://embodied-ai-radar.com/e/arxiv-2602.11236.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2605.30280", "title": "Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments", "type": "paper", "arxiv": "2605.30280", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "vla", "cross-embodiment", "unified" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Qwen-VLA studies whether manipulation, navigation and other decision tasks can share one VLA model across tasks, environments and robots.", "takeaway_zh": "Qwen-VLA 研究操作、导航等决策任务能否由同一 VLA 模型统一，跨任务、环境和机器人形态。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2605.30280.html", "labs": [], "code_license": null }, { "id": "arxiv-2510.21571", "title": "Scalable Vision-Language-Action Model Pretraining for Robotic Manipulation with Real-Life Human Activity Videos", "type": "paper", "arxiv": "2510.21571", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/microsoft/VITRA", "project": "https://microsoft.github.io/VITRA/", "topics": [ "vla" ], "tags": [ "vla", "human-video", "pretraining" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "VITRA pretrains VLA models on unscripted in-the-wild human videos, treating the human hand as a dexterous end-effector, with no annotations needed.", "takeaway_zh": "VITRA 用无脚本的真实人类活动视频预训练 VLA，把人手视为灵巧末端执行器，无需人工标注。", "github_stars": 516, "page": "https://embodied-ai-radar.com/e/arxiv-2510.21571.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2505.19789", "title": "What Can RL Bring to VLA Generalization? An Empirical Study", "type": "paper", "arxiv": "2505.19789", "year": 2025, "venue": "NeurIPS 2025", "org": null, "url": null, "code": "https://github.com/gen-robot/RL4VLA", "project": "https://rlvla.github.io", "topics": [ "vla" ], "tags": [ "vla", "rl", "fine-tuning" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Empirical study of whether RL fine-tuning generalises better than supervised fine-tuning, which suffers compounding errors under distribution shift.", "takeaway_zh": "实证研究强化学习微调是否比监督微调泛化更好；后者在分布偏移下易累积误差。", "github_stars": 297, "page": "https://embodied-ai-radar.com/e/arxiv-2505.19789.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2406.18977", "title": "RoboUniView: Visual-Language Model with Unified View Representation for Robotic Manipulation", "type": "paper", "arxiv": "2406.18977", "year": 2024, "venue": null, "org": null, "url": null, "code": "https://github.com/liufanfanlff/RoboUniview", "project": null, "topics": [ "vla" ], "tags": [ "vla", "multi-view", "bev" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "RoboUniView learns a unified view representation so a VLM-based policy depends less on camera specs and mounting positions.", "takeaway_zh": "RoboUniView 学习统一视角表征，降低基于 VLM 的策略对相机参数和安装位置的依赖。", "github_stars": 66, "page": "https://embodied-ai-radar.com/e/arxiv-2406.18977.html", "labs": [], "code_license": "" }, { "id": "arxiv-2311.14379", "title": "Robot Learning in the Era of Foundation Models: A Survey", "type": "paper", "arxiv": "2311.14379", "year": 2023, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "survey", "foundation-models" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Survey of how foundation models are reshaping robot learning, alongside traditional learning methods.", "takeaway_zh": "综述基础模型如何与传统学习方法结合，改变机器人学习。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2311.14379.html", "labs": [], "code_license": null }, { "id": "arxiv-2312.08782", "title": "Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis", "type": "paper", "arxiv": "2312.08782", "year": 2023, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "survey", "meta-analysis", "foundation-models" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Survey and meta-analysis arguing robots are limited by task-specific design, and how foundation models could enable general-purpose ones.", "takeaway_zh": "综述与元分析：指出机器人受限于任务专用设计，并讨论基础模型如何走向通用机器人。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2312.08782.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.06184", "title": "Can Astra Solve RoboMME without Breaking the Bank?", "type": "paper", "arxiv": "2611.06184", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/bingaochen/Astra-on-RoboMME", "project": "https://bingaochen.github.io/Astra-on-RoboMME/", "topics": [ "vla" ], "tags": [ "sim", "memory", "llm-planner", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "CoRL 2026 workshop paper: a planner picks subtasks, a π0.5 policy acts, and a small fine-tuned monitor decides when to call the planner again; reports 79.13% on RoboMME with about 3.6 planner calls per episode.", "takeaway_zh": "CoRL 2026 研讨会论文：大模型规划子任务，π0.5 策略执行，小型微调监视器判断何时再次调用规划器；报告在 RoboMME 上成功率 79.13%，每回合约调用规划器 3.6 次。", "github_stars": 32, "page": "https://embodied-ai-radar.com/e/arxiv-2611.06184.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2611.06768", "title": "PyRUA-REAL: Robot Exploration and Adaptive Learning through RSI in the Real World", "type": "paper", "arxiv": "2611.06768", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/DAGroup-PKU/PyRUA-Lean", "project": "https://dagroup-pku.github.io/PyRUA-Lean/real/", "topics": [ "vla", "manipulation" ], "tags": [ "coding-agents", "self-improvement", "real", "skill-memory" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Coding agents keep real-robot experience as memory and reusable code skills, refining them through physical trials; reports 91.1% success on eight real tasks and 84.4% on 42 RoboDojo tasks.", "takeaway_zh": "让编码智能体把真机交互经验存为记忆和可复用的代码技能，并在实物试验中持续改进；报告八个真实任务成功率 91.1%，RoboDojo 的 42 个任务成功率 84.4%。", "github_stars": 234, "page": "https://embodied-ai-radar.com/e/arxiv-2611.06768.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.05979", "title": "World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis", "type": "paper", "arxiv": "2606.05979", "year": 2026, "venue": "CoRL 2026", "org": "SJTU", "url": null, "code": "https://github.com/SJTU-DENG-Lab/WLA", "project": null, "topics": [ "vla", "world-models" ], "tags": [ "wam", "autoregressive", "subgoal-images", "test-time-scaling", "real", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "World-language-action model: one autoregressive backbone predicts text subtasks, subgoal images and actions; world prediction can be off at inference or on for test-time scaling. Reports 92.94% on RoboTwin 2.0 Clean.", "takeaway_zh": "世界-语言-动作模型：单一自回归主干同时预测文本子任务、子目标图像与动作；推理时可关闭世界预测，也可开启做测试时扩展。报告在 RoboTwin 2.0 Clean 上成功率 92.94%。", "github_stars": 153, "page": "https://embodied-ai-radar.com/e/arxiv-2606.05979.html", "labs": [ "sjtu" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.20092", "title": "EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies", "type": "paper", "arxiv": "2606.20092", "year": 2026, "venue": "CoRL 2026", "org": "InternRobotics", "url": null, "code": "https://github.com/InternRobotics/EventVLA", "project": null, "topics": [ "vla", "manipulation", "datasets" ], "tags": [ "memory", "long-horizon", "keyframes", "bimanual", "benchmark", "real", "sim" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Memory for VLAs that keeps only sparse keyframes the policy itself predicts will matter later. Adds the RoboTwin-MeM benchmark; reports +40% average success over memory-augmented VLAs across 17 sim and 4 real tasks.", "takeaway_zh": "为 VLA 加入稀疏记忆：由策略自己预测哪些关键帧之后会有用并只保存这些帧。同时提出 RoboTwin-MeM 基准；报告在 17 个仿真和 4 个真机任务上平均成功率比带记忆的 VLA 高 40%。", "github_stars": 84, "page": "https://embodied-ai-radar.com/e/arxiv-2606.20092.html", "labs": [ "shanghai-ai-lab" ], "code_license": "" }, { "id": "arxiv-2609.10706", "title": "HuRo: Robotizing Human Videos for Scalable VLA Pretraining", "type": "paper", "arxiv": "2609.10706", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/3587jjh/HuRo", "project": "https://3587jjh.github.io/HuRo", "topics": [ "vla", "datasets" ], "tags": [ "human-video", "robotization", "pretraining", "open-data", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Converts human videos from five sources into robot-aligned observations and actions (about 630K episodes). Pretraining a VLA on more of it raises real-task completion from 51.5% to 80.3% and OOD from 34.9% to 72.2%.", "takeaway_zh": "把五个来源的人类视频转换成与机器人对齐的观测和动作（约 63 万条轨迹）。报告用更多此类数据预训练 VLA，真机任务完成率从 51.5% 升到 80.3%，分布外从 34.9% 升到 72.2%。", "github_stars": 65, "page": "https://embodied-ai-radar.com/e/arxiv-2609.10706.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.17200", "title": "ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining", "type": "paper", "arxiv": "2606.17200", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/ACERobotics-VLA/ACE-Ego-0", "project": null, "topics": [ "vla", "datasets" ], "tags": [ "human-video", "egocentric", "pretraining", "bimanual", "real", "sim" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Joint VLA pretraining on 4.53K h of robot/sim data plus 1.48K h of egocentric human video with pseudo-actions, camera-space actions and a reliability-weighted loss. Reports SOTA on RoboCasa GR1 TableTop and RoboTwin 2.0.", "takeaway_zh": "用 4530 小时机器人/仿真数据加 1480 小时带伪动作的第一视角人类视频联合预训练 VLA，采用相机空间动作与按可靠性加权的损失。报告在 RoboCasa GR1 TableTop 和 RoboTwin 2.0 上达到最优。", "github_stars": 58, "page": "https://embodied-ai-radar.com/e/arxiv-2606.17200.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.12366", "title": "APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies", "type": "paper", "arxiv": "2606.12366", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/xukechun/APT", "project": "https://xukechun.github.io/papers/APT/", "topics": [ "vla" ], "tags": [ "action-expert", "pretraining", "language-generalization", "training-recipe" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Pretrain the action expert first as a language-free vision-action prior, then inject language through gated fusion. Targets VLAs that ignore unseen instructions; works with π-style and GR00T-style designs.", "takeaway_zh": "先把动作专家预训练为不含语言的视觉-动作先验，再通过门控融合注入语言。针对 VLA 对未见指令泛化差的问题，适用于 π 系和 GR00T 系架构。", "github_stars": 48, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12366.html", "labs": [], "code_license": "" }, { "id": "arxiv-2606.19774", "title": "Start Right, Arrive Right: Asynchronous Execution via Initial Noise Selection", "type": "paper", "arxiv": "2606.19774", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/htrbao/paint-action-chunking", "project": "https://paint-action-chunking.github.io", "topics": [ "vla", "manipulation" ], "tags": [ "action-chunking", "asynchronous-execution", "flow-matching", "training-free", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "PAINT keeps action chunks consistent under async execution by picking the initial noise (backward Euler inversion), not by guiding the flow. No training; reports RTC-level task success with better prefix consistency.", "takeaway_zh": "PAINT 通过选择初始噪声（后向欧拉反演）而非引导流场，让异步执行时的动作块首尾一致；无需训练，报告任务成功率与 RTC 相当、前缀一致性更好。", "github_stars": 21, "page": "https://embodied-ai-radar.com/e/arxiv-2606.19774.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.21139", "title": "PoLAR: Factorizing Extent and Mode in Latent Actions for Robot Policy Learning", "type": "paper", "arxiv": "2606.21139", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/joon-stack/PoLAR", "project": "https://joon-stack.github.io/PoLAR", "topics": [ "vla", "learning" ], "tags": [ "latent-actions", "hyperbolic", "pretraining", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Latent actions with polar structure in hyperbolic space: radius encodes how much changes, direction encodes what kind. Reports gains over latent-action baselines and pretrained VLAs.", "takeaway_zh": "在双曲空间中构造极坐标结构的潜动作：半径表示变化多大，方向表示变化类型。报告优于其他潜动作方法和预训练 VLA。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2606.21139.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2611.08373", "title": "SLA: Scaling Spatial-Language-Action Model via Continual Pre-training", "type": "paper", "arxiv": "2611.08373", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/OpenHelix-Team/Spatial-Forcing/tree/master/SLA", "project": "https://embodiedsla.github.io/", "topics": [ "vla" ], "tags": [ "spatial-alignment", "3d", "continual-pretraining", "pi0.5" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Continual pre-training of π0.5 that aligns intermediate visual features with a frozen 3D perception model across many robot datasets. The extra parts are training-only, so inference and weights stay π0.5-compatible.", "takeaway_zh": "在多个机器人数据集上对 π0.5 做持续预训练，把中间视觉特征与冻结的 3D 感知模型对齐。附加组件只在训练时使用，推理结构和权重与 π0.5 保持兼容。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08373.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.07513", "title": "Concertina: Embodying Manipulation and Cognition in One Diffusion Vision-Language Model", "type": "paper", "arxiv": "2611.07513", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/lose4578/Concertina", "project": null, "topics": [ "vla" ], "tags": [ "diffusion-vlm", "continuous-actions", "unified-model", "libero", "calvin" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Puts continuous actions inside one diffusion VLM: each timestep is a latent token, no separate action expert (0.21% of params for action I/O). Reports 98.15% on LIBERO and 85.9% on LIBERO-Plus.", "takeaway_zh": "把连续动作直接放进同一个扩散 VLM：每个控制时间步是一个潜动作 token，没有独立动作专家（动作接口仅占 0.21% 参数）。报告 LIBERO 98.15%、LIBERO-Plus 85.9%。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2611.07513.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.08425", "title": "Failure Prediction and Classification in Vision-Language-Action Models Via Calibrated Latent Probing", "type": "paper", "arxiv": "2611.08425", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://vla-tug.github.io", "topics": [ "vla", "datasets" ], "tags": [ "failure-prediction", "monitoring", "calibration", "probing", "safety" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Lightweight probe on a VLA's latent layers that flags failures early with calibrated miss and false-alarm bounds and names the failure mode. Releases 3,180 annotated manipulation episodes.", "takeaway_zh": "在 VLA 隐层上加轻量探针，提前预警失败并给出校准的漏报与误报上界，还能判断失败类型。并发布 3180 条带标注的操作轨迹。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08425.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.20504", "title": "Graph-Reweighted Behavior Cloning with Human Interventions for Precise Robotic Manipulation", "type": "paper", "arxiv": "2610.20504", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Gamma-Robot/GR-BC", "project": "https://gr-bc.github.io", "topics": [ "vla", "learning" ], "tags": [ "post-training", "behavior-cloning", "human-intervention", "graph-search", "precise-manipulation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Weights each action chunk by how much it shortens the path to success on a reachability graph built over demos, rollouts and interventions. Reports 91% on precise tasks after a few offline rounds; no reward model.", "takeaway_zh": "在示教、rollout 与人工干预构成的可达图上，按每个动作块缩短到成功的步数来加权行为克隆。报告经几轮离线迭代在精细任务上达到 91%，无需奖励模型。", "github_stars": 51, "page": "https://embodied-ai-radar.com/e/arxiv-2610.20504.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2609.37165", "title": "Disentangling Spurious Correlations in Vision-Language-Action Models via Predicting Domain-Invariant Latent Lookahead", "type": "paper", "arxiv": "2609.37165", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://dill-vla.github.io/", "topics": [ "vla" ], "tags": [ "robustness", "spurious-correlations", "representation", "libero-plus", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Supervises VLAs with domain-invariant future latents to cut reliance on visual shortcuts. Reports 69.1% on LIBERO-Plus, 11.4 points above the strongest baseline.", "takeaway_zh": "用与域无关的未来潜变量监督 VLA，减少对视觉捷径的依赖。报告 LIBERO-Plus 平均成功率 69.1%，比最强基线高 11.4 个百分点。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.37165.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.21022", "title": "Catch Me If You Can: Real-Time Feedback Denoising for Responsive VLAs", "type": "paper", "arxiv": "2609.21022", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/jidaxian010/VLA-Feedback-release", "project": "https://vla-feedback.github.io", "topics": [ "vla" ], "tags": [ "closed-loop", "diffusion", "dynamic-tasks", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Keeps the last denoising step of a diffusion VLA as a fast feedback layer, so each action is corrected with the newest image. Reports dynamic sim tasks 27.5% to 85.0% and real tasks 51% to 73%.", "takeaway_zh": "把扩散 VLA 的最后一步去噪保留为快速反馈层，用最新图像修正每个动作。报告动态仿真任务从 27.5% 升到 85.0%，真机任务从 51% 升到 73%。", "github_stars": 7, "page": "https://embodied-ai-radar.com/e/arxiv-2609.21022.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2609.13812", "title": "GeomVLA: Unifying Scene, Motion, and Action in 3D", "type": "paper", "arxiv": "2609.13812", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://ziyin-xiong.github.io/geomvla.io/", "topics": [ "vla" ], "tags": [ "3d", "scene-flow", "geometry", "calvin", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "VLA that lifts VLM features into 3D scene tokens, predicts how scene points will move, and conditions a 3D flow action denoiser on that motion. Reports SOTA on CALVIN without robot-action pretraining.", "takeaway_zh": "把 VLM 特征提升为 3D 场景 token，预测场景点将如何运动，并以此为条件驱动 3D 流匹配动作去噪器。报告在无机器人动作预训练下 CALVIN 达到最优。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.13812.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.21924", "title": "Long-Horizon Manipulation via Trace-Conditioned VLA Planning", "type": "paper", "arxiv": "2604.21924", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://www.liuisabella.com/LoHoManip", "topics": [ "vla" ], "tags": [ "long-horizon", "planning", "visual-trace", "franka", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "A task-manager VLM re-predicts the remaining subtasks and a 2D keypoint trace at every step; the VLA executor just follows the trace. Failed steps stay in the plan, so replanning comes for free. Sim + real Franka.", "takeaway_zh": "由任务管理 VLM 每一步重新预测剩余子任务和一条 2D 关键点轨迹，VLA 执行器只需沿轨迹执行。失败的步骤会留在计划里，因此无需额外逻辑即可自动重规划。仿真与真实 Franka 实验。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.21924.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.10655", "title": "Artificial Foveated Perception for Mitigating Shortcut Learning in Robotic Foundation Models", "type": "paper", "arxiv": "2607.10655", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Apollo-Lab-Yale/artificial-foveated-perception", "project": "https://apollo-lab-yale.github.io/26-CoRL-AFP-website/", "topics": [ "vla" ], "tags": [ "shortcut-learning", "attention", "masks", "fine-tuning", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Fights shortcut learning when fine-tuning VLAs: predicted masks of task-relevant objects and the robot serve as an auxiliary grounding loss; nothing changes at inference. Tested on four foundation models and π0.5.", "takeaway_zh": "针对 VLA 微调中的捷径学习：预测任务相关物体与机器人的掩码，作为辅助对齐损失引导策略关注正确区域，推理时不增加任何开销。在四个基础模型和真机 π0.5 上验证。", "github_stars": 7, "page": "https://embodied-ai-radar.com/e/arxiv-2607.10655.html", "labs": [], "code_license": "" }, { "id": "arxiv-2606.12475", "title": "Learning to Assist: Collaborative VLAs for Implicit Human-Robot Collaboration", "type": "paper", "arxiv": "2606.12475", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://learning-to-assist.github.io/", "topics": [ "vla" ], "tags": [ "human-robot-collaboration", "action-chunking", "steering", "user-study", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Shows VLAs can do implicit human-robot collaboration, but action chunks that cross task transitions make the robot help too early; an inference-time steering fix is tested in a 16-person assembly study.", "takeaway_zh": "表明 VLA 可用于隐式人机协作，但跨越任务切换点的动作块会让机器人过早提供帮助；提出推理时引导方法缓解，并在 16 人装配用户研究中验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12475.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.14752", "title": "X-Tokenizer: A Multimodal Action Tokenizer for Vision-Language-Action Pretraining", "type": "paper", "arxiv": "2606.14752", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/X-Square-Robot/X-Tokenizer", "project": "https://x-square-robot.github.io/X-Tokenizer_projectPage/", "topics": [ "vla" ], "tags": [ "action-tokenizer", "pretraining", "quantization", "robotwin", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Action tokenizer whose first quantization level is trained as a semantic 'action language' while deeper levels keep detail; pretrained on 2.4M trajectories. Reports gains over FAST on grounding and long-horizon tasks.", "takeaway_zh": "动作 tokenizer：残差量化的第一层被训练成表达粗粒度运动意图的语义“动作语言”，更深层保留细节；在 240 万条轨迹上预训练。报告在多模态对齐和长程任务上优于 FAST。", "github_stars": 79, "page": "https://embodied-ai-radar.com/e/arxiv-2606.14752.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.02735", "title": "See Less, Specify More: Visual Evidence Budgets for Generalizable VLAs", "type": "paper", "arxiv": "2606.02735", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://s2.airoa.io", "topics": [ "vla" ], "tags": [ "generalization", "language-relabeling", "visual-budget", "hsr", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Trains the VLA executor with refined subtask language plus an explicit budget on how much visual evidence it may use. Reports mean subtask success up from 54.2% to 79.0% over π0.5 on eight real tasks.", "takeaway_zh": "用更细的子任务语言重标注轨迹，并对执行器可使用的视觉证据施加显式预算来训练 VLA。报告在八个真机任务上平均子任务成功率比 π0.5 从 54.2% 提升到 79.0%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.02735.html", "labs": [], "code_license": null }, { "id": "arxiv-2605.31234", "title": "HARP-VLA: Human-Robot Aligned Representation Learning for Vision-Language-Action Model", "type": "paper", "arxiv": "2605.31234", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/PuzhenYuan/HARP-VLA", "project": "https://puzhenyuan.github.io/HARP-VLA-website/", "topics": [ "vla" ], "tags": [ "human-video", "latent-actions", "representation-alignment", "calvin", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Aligns robot visual features with human ones using paired human-robot demos, then pretrains a latent-action VLA on human and robot videos. Reports 4.481 average length on CALVIN ABC→D.", "takeaway_zh": "利用成对的人-机演示把机器人视觉表征与人类表征对齐，再用人类和机器人视频预训练潜在动作 VLA。报告 CALVIN ABC→D 平均长度 4.481。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2605.31234.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.12352", "title": "CHORUS: Decentralized Multi-Embodiment Collaboration with One VLA Policy", "type": "paper", "arxiv": "2606.12352", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://chorus-model.github.io", "topics": [ "vla" ], "tags": [ "multi-robot", "decentralized", "collaboration", "mobile", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "One VLA backbone runs as an independent copy on each robot, conditioned on its own view and a robot ID prompt, for decentralized team tasks. Reports a 64-point gain over from-scratch decentralized models.", "takeaway_zh": "同一个 VLA 主干在每台机器人上独立运行，只依赖本机观测和机器人身份提示，实现去中心化多机协作。报告比从零训练的去中心化模型提升 64 个百分点。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12352.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.07895", "title": "TBD-VLA: Temporal Block Diffusion Vision Language Action Model", "type": "paper", "arxiv": "2606.07895", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/TBD-VLA/lerobot", "project": "https://tbd-vla.github.io/", "topics": [ "vla" ], "tags": [ "discrete-diffusion", "action-tokens", "fast-inference", "real-time-chunking", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Discrete VLA that generates actions autoregressively across temporal blocks and with masked diffusion inside each block, gaining speed and supporting real-time chunking via temporal in-painting.", "takeaway_zh": "离散 token VLA：块与块之间自回归、块内做掩码离散扩散，兼顾时间连贯性与推理速度，并可通过时间补全支持实时分块执行。", "github_stars": 7, "page": "https://embodied-ai-radar.com/e/arxiv-2606.07895.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2603.10052", "title": "OmniGuide: Universal Spatial Guidance Fields for Enhancing Generalist Robot Policies", "type": "paper", "arxiv": "2603.10052", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://omniguide.github.io/", "topics": [ "vla" ], "tags": [ "guidance", "energy-functions", "3d", "pi0.5", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Turns guidance from 3D foundation models, VLMs or human pose into differentiable 3D attractor/repeller energies that steer VLA action sampling. Reports gains for π0.5 and GR00T N1.6 in sim and real.", "takeaway_zh": "把 3D 基础模型、VLM、人体姿态等指导信号统一表示为 3D 空间中可微的吸引/排斥能量函数，引导 VLA 动作采样。报告提升 π0.5 和 GR00T N1.6 在仿真与真机上的表现。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.10052.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.07723", "title": "VoLo: A Physical Orchestrator for Open-Vocabulary Long-Horizon Manipulation", "type": "paper", "arxiv": "2606.07723", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/NVlabs/VoLoAgent", "project": "https://chicychen.github.io/VoLo/", "topics": [ "vla" ], "tags": [ "agent", "vlm-orchestration", "long-horizon", "benchmark", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "VLM agent that plans, monitors and recovers by treating a VLA or world-action model as an interruptible tool it can stop and steer mid-rollout. Comes with RoboVoLo, a long-horizon open-vocabulary benchmark.", "takeaway_zh": "VLM 智能体把 VLA/世界动作模型当作可随时中断的工具，在执行中途调度和纠正，从而完成规划、监控与恢复。配套发布开放词汇长程操作基准 RoboVoLo。", "github_stars": 78, "page": "https://embodied-ai-radar.com/e/arxiv-2606.07723.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.10180", "title": "Flow Control: Steering Vision-Language-Action Models with Simple Real-Time Inputs", "type": "paper", "arxiv": "2606.10180", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://jcykao.github.io/flowcontrol/", "topics": [ "vla" ], "tags": [ "steering", "teleoperation", "flow-matching", "human-in-the-loop", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Steers a flow-matching VLA in real time from a keyboard by writing the user input into the action expert's initial noise; no retraining. Fine-tuning on steered runs also improves the autonomous policy.", "takeaway_zh": "把键盘等简单用户输入写入流匹配动作专家的初始噪声，即可实时引导 VLA，无需重训；用引导后的轨迹微调还能提升自主策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.10180.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.08974", "title": "CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding", "type": "paper", "arxiv": "2607.08974", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://omron-sinicx.github.io/clap/", "topics": [ "vla" ], "tags": [ "vlm-to-vla", "language-actions", "open-weights", "libero", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Turns a VLM into a VLA without architecture changes by writing a short language action description before the numeric action tokens. 2B model reports 90.8% on LIBERO after 4–5 hours of training; 0.8B–4B weights.", "takeaway_zh": "不改结构直接把 VLM 变成 VLA：在数值动作 token 之前先生成一句自然语言动作描述。2B 模型训练 4–5 小时，报告 LIBERO 90.8%；发布 0.8B–4B 开放权重。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.08974.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.15021", "title": "Steering Autoregressive Vision-Language-Action Policies via Action Token Intervention", "type": "paper", "arxiv": "2606.15021", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://jasontchan.github.io/token-steering/", "topics": [ "vla" ], "tags": [ "steering", "action-tokens", "human-in-the-loop", "assistive", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Steers autoregressive VLAs at inference by injecting low-dimensional user input into the action-token space. Reports success from 10.0% to 72.5% and 16.7% to 93.8% on two household tasks.", "takeaway_zh": "推理时把低维用户输入注入自回归 VLA 的动作 token 空间来引导轨迹，无需训练。报告两个家务任务成功率分别从 10.0% 升到 72.5%、从 16.7% 升到 93.8%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.15021.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.16677", "title": "SentryVLA: Uncertainty-Guided Action Selection and State Monitoring for Vision-Language-Action Robotic Control", "type": "paper", "arxiv": "2604.16677", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://robotic-vision-lab.github.io/sentryvla/", "topics": [ "vla" ], "tags": [ "uncertainty", "conformal-prediction", "failure-detection", "runtime-monitoring", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Runtime wrapper for frozen VLAs: conformalized quantile regression ranks sampled actions, and a Mahalanobis anomaly score on latent states warns about abnormal execution. No retraining.", "takeaway_zh": "冻结 VLA 的运行时外壳：用共形分位数回归为多个候选动作打分择优，并在潜在状态上计算马氏距离异常分，预警异常执行。无需重训。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.16677.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.12105", "title": "DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model", "type": "paper", "arxiv": "2606.12105", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://intuitive-robots.github.io/DAM-VLA/", "topics": [ "vla" ], "tags": [ "asynchronous", "multimodal", "force", "high-frequency", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Lets each modality update at its own sensor rate through latent buffers read by the action head, adding fast signals via gated cross-attention. Reports 95.2% vs 40.95% on seven contact-rich real tasks at 100 Hz.", "takeaway_zh": "各模态按各自传感器频率刷新潜在缓存，供动作头持续读取，高频信号通过门控交叉注意力接入。报告在七个富接触真机任务上成功率 95.2%，对比基线 40.95%，控制频率 100 Hz。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12105.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.00337", "title": "Action Chunk Scheduling for Batched Robot Policy Serving", "type": "paper", "arxiv": "2608.00337", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/GaTech-RL2/armory", "project": "https://gatech-rl2.github.io/actionchunkscheduling", "topics": [ "vla" ], "tags": [ "serving", "scheduling", "fleet", "inference", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Serving one robot policy to a fleet from a remote GPU, posed as scheduling. Naive batching fails when robots consume action chunks at different rates; their scheduler reports up to 18% higher real-world throughput.", "takeaway_zh": "把从远程 GPU 为多台机器人提供策略推理建模为调度问题。当各机器人消耗动作块的速度不同时，简单批处理失效；所提调度算法在真机上报告吞吐量最高提升 18%。", "github_stars": 26, "page": "https://embodied-ai-radar.com/e/arxiv-2608.00337.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2603.19183", "title": "Sparse Autoencoders Reveal Interpretable and Steerable Features in VLA Models", "type": "paper", "arxiv": "2603.19183", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/swannaiden/drvla", "project": "https://drvla.github.io", "topics": [ "vla" ], "tags": [ "interpretability", "sparse-autoencoders", "steering", "libero", "droid" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Trains sparse autoencoders on VLA activations and finds features for motion primitives and concepts that transfer across episodes and can be amplified to steer behaviour. LIBERO + real DROID tests.", "takeaway_zh": "在 VLA 隐层激活上训练稀疏自编码器，发现对应运动基元和语义概念、可跨回合迁移的特征，放大这些特征即可引导行为。在 LIBERO 和真实 DROID 平台验证。", "github_stars": 13, "page": "https://embodied-ai-radar.com/e/arxiv-2603.19183.html", "labs": [], "code_license": "" }, { "id": "arxiv-2606.12978", "title": "Trajectory-Level Redirection Attacks on Vision-Language-Action Models", "type": "paper", "arxiv": "2606.12978", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://vla-redirection-attack.github.io/", "topics": [ "vla" ], "tags": [ "security", "adversarial-prompts", "robustness", "attack", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Shows a prompt that still reads like the intended task can redirect a VLA's rollout to an attacker's target; an on-policy prompt search finds such near-benign prompts. Sim + hardware.", "takeaway_zh": "揭示 VLA 的轨迹级漏洞：看起来仍在描述原任务的指令，可以把机器人执行结果引向攻击者指定的目标；作者用在线提示搜索找到这类近乎无害的提示。仿真与真机验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12978.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.21740", "title": "CounterAlign: Counterfactual Supervision for Vision-Language-Action Models", "type": "paper", "arxiv": "2608.21740", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://counteralign.airoa.io", "topics": [ "vla" ], "tags": [ "offline-rl", "counterfactual", "reward-model", "libero-pro", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Turns expert demos into negative supervision by pairing actions with mismatched instructions, training an instruction-grounded reward for offline RL of VLAs. More robust on LIBERO-PRO and a real AgiBot-G2-type robot.", "takeaway_zh": "把专家动作与不匹配的指令配对生成反事实样本，训练与指令对齐的奖励模型，用于 VLA 离线强化学习，无需额外采集。在 LIBERO-PRO 和真机上提升鲁棒性。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.21740.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.03240", "title": "GeoAlign: Beyond Semantics with State-Guided Spatial Alignment in VLA Models", "type": "paper", "arxiv": "2606.03240", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://chenyizhi123.github.io/geoalign-project/", "topics": [ "vla" ], "tags": [ "geometry", "rgb-d", "spatial-alignment", "gr00t", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Post-trains an RGB geometry branch with RGB-D supervision, then lets state-generated queries pull eight geometry tokens for action prediction. Reports 99.0% on LIBERO and 78.8% on eight real ALOHA tasks.", "takeaway_zh": "先用 RGB-D 监督后训练 RGB 几何分支，再由机器人状态生成查询，提取 8 个几何 token 用于动作预测。报告 LIBERO 99.0%，八个真实 ALOHA 任务 78.8%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.03240.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.21386", "title": "VLA-FAIL: Efficient Task Failure Detection for Finetuned Vision-Language-Action Models", "type": "paper", "arxiv": "2606.21386", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://vla-fail.github.io/vla-fail-2026", "topics": [ "vla" ], "tags": [ "failure-detection", "ood", "action-chunks", "monitoring", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Two cheap failure detectors for fine-tuned VLAs that need no failure data: last-layer Mahalanobis distance and consistency between overlapping action chunks, plus a metric that also scores detection time.", "takeaway_zh": "为微调后的 VLA 提出两种无需失败数据的轻量失败检测器：末层特征马氏距离与相邻动作块一致性，并提出同时衡量检测时间的指标。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.21386.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.01549", "title": "Pri4R: Learning World Dynamics for Vision-Language-Action Models with Privileged 4D Representation", "type": "paper", "arxiv": "2603.01549", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://jiiiisoo.github.io/Pri4R/", "topics": [ "vla" ], "tags": [ "4d", "point-tracks", "auxiliary-loss", "libero", "robocasa" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Adds a training-only head that predicts 3D point tracks from VLA features, teaching world dynamics with no inference cost. Reports +10% on LIBERO-Long and +40% on RoboCasa.", "takeaway_zh": "训练时为 VLA 加一个预测 3D 点轨迹的轻量头，让模型学到场景动力学，推理时去掉、零额外开销。报告 LIBERO-Long 提升 10%，RoboCasa 提升 40%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.01549.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.25038", "title": "$π$, But Make It Fly: Physics-Guided Transfer of VLA Models to Aerial Manipulation", "type": "paper", "arxiv": "2603.25038", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://airvla.github.io", "topics": [ "vla" ], "tags": [ "aerial-manipulation", "drone", "guidance", "gaussian-splatting", "real" ], "robot_types": [ "aerial" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Transfers a pretrained π0 to aerial pick-and-place: payload-aware guidance in flow sampling handles flight dynamics and 3DGS data covers navigation. Reports pick-and-place success up from 23% to 50% in 460 real trials.", "takeaway_zh": "把预训练 π0 迁移到空中抓放：在流采样中注入载荷约束以应对飞行动力学，并用高斯泼溅合成导航数据。在 460 次真机实验中报告抓放成功率从 23% 提升到 50%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.25038.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.38989", "title": "Cue the Flow: Steering Flow-Matching Policies for Open-World Delivery Manipulation", "type": "paper", "arxiv": "2609.38989", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://hatchetproject.github.io/delivery_steer/", "topics": [ "vla" ], "tags": [ "steering", "mobile-manipulation", "grounding", "adapter", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Steers a pretrained flow-matching VLA with a spatial cue from a grounding model: a small adapter predicts an affine transform of the action chunk toward the cued target. Up to ~2x task success for open-world delivery.", "takeaway_zh": "用视觉定位模型给出的空间提示引导预训练流匹配 VLA：轻量适配器预测作用在动作块上的仿射变换，使动作对准目标。开放世界配送任务成功率最高提升近 2 倍。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.38989.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.18953", "title": "Object-Centric Residual RL for Zero-Shot Sim-to-Real VLA Enhancement", "type": "paper", "arxiv": "2606.18953", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://www.microsoft.com/en-us/research/articles/object-centric-residual-rl/", "topics": [ "vla" ], "tags": [ "residual-rl", "sim-to-real", "object-poses", "franka", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Residual RL on top of a frozen VLA using only object poses, so it transfers from sim to real; a sim twin of the VLA is trained by replaying the same teleop demos. Reports real FR3 success from 42% to 76% zero-shot.", "takeaway_zh": "在冻结的 VLA 之上做只依赖物体位姿的残差强化学习，从而可直接从仿真迁移到真机；通过回放同一批遥操作数据训练 VLA 的仿真副本。报告真实 FR3 上成功率从 42% 提升到 76%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.18953.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.02274", "title": "Dexterity-BEV: Aligning 3D World and Actions for Generalizable Robot Policies Learning", "type": "paper", "arxiv": "2606.02274", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://hnuzhy.github.io/projects/Dex-BEV", "topics": [ "vla" ], "tags": [ "3d", "bev", "cross-embodiment", "alignment", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Lifts 2D inputs to 3D with per-pixel vertex maps and expresses camera views and actions in one shared bird's-eye-view frame, aligning data across robots, cameras and datasets.", "takeaway_zh": "用逐像素顶点图把 2D 输入提升到 3D，并把各相机视角与机器人动作统一表示在同一鸟瞰（BEV）坐标系中，对齐不同机器人、相机与数据集的数据。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.02274.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.07107", "title": "Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models", "type": "paper", "arxiv": "2606.07107", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "planning", "action-tokens", "long-horizon", "libero", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Plan-then-execute VLA that first predicts a short sequence of coarse action tokens summarising the intended trajectory, then the executable tokens conditioned on it. Largest gains on long multi-stage tasks.", "takeaway_zh": "先规划后执行的 VLA：先预测概括未来轨迹的少量粗粒度动作 token，再以此为条件生成可执行动作 token；规划与执行共享同一离散动作词表。长程多阶段任务提升最大。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.07107.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.21372", "title": "NAC: Neural Action Codec for Vision-Language-Action Models", "type": "paper", "arxiv": "2606.21372", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "action-tokenizer", "audio-codec", "rvq", "libero", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Action tokenizer adapted from neural audio codecs (multi-scale RVQGAN with a Vocos-style decoder). Reports higher success than binning, FAST and earlier VQ tokenizers on LIBERO-10, RoboMimic and real tasks.", "takeaway_zh": "借鉴神经音频编解码器的动作 tokenizer（多尺度 RVQGAN + Vocos 式解码器）。报告在 LIBERO-10、RoboMimic 与真机任务上成功率高于分箱、FAST 和已有 VQ 方法。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.21372.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.08288", "title": "MotionVLA: Injecting Geometric Motion into Vision-Language-Action Model", "type": "paper", "arxiv": "2606.08288", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "memory", "motion-history", "trajectory-fields", "long-horizon", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Gives a VLA memory as motion rather than frames: a short past video becomes continuous trajectory-field tokens that current visual tokens query. Smoother, more direct long-horizon execution.", "takeaway_zh": "让 VLA 记住“运动”而非“帧”：把一小段历史视频转成时间连续的轨迹场 token，供当前视觉 token 查询。长程操作更平滑、更直接。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.08288.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.16517", "title": "Trust-Calibrated VLA Policy Post-Training with Evolving Imagination", "type": "paper", "arxiv": "2610.16517", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "rl", "world-model", "trust", "libero-pro", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "RL post-training of VLAs with an online-updated world model whose measured reliability decides how much imagined data and how long rollouts to use. Reports 79.7% on perturbed LIBERO-PRO.", "takeaway_zh": "VLA 强化学习后训练：在线更新的世界模型与奖励头作为模拟器，按其可信度动态决定想象数据比例和推演长度。报告扰动版 LIBERO-PRO 平均成功率 79.7%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.16517.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.03693", "title": "CoRE-VLA: Towards Scalable and Robust Vision-Language-Action Modeling via Conditional Routing of Experts", "type": "paper", "arxiv": "2607.03693", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "mixture-of-experts", "missing-sensors", "depth", "robustness", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "VLA with sparse experts: sensor availability gates modality experts, so it degrades gracefully when depth is missing, and task intent routes action experts. Strong on LIBERO, RoboCasa GR1 and real dual-arm tasks.", "takeaway_zh": "稀疏专家 VLA：传感器是否可用决定模态专家的开关，缺少深度时也能平稳退化；任务意图再路由到动作专家。在 LIBERO、RoboCasa GR1 和真实双臂任务上表现强。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.03693.html", "labs": [], "code_license": null }, { "id": "arxiv-2605.25477", "title": "EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models", "type": "paper", "arxiv": "2605.25477", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "rl-finetuning", "sample-efficiency", "precision", "open-source", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "System for stable, sample-efficient RL fine-tuning of pretrained VLAs; reports 30/30 success on every task (e.g. plugging in string lights, pool shots) after ~19 minutes of robot data on average.", "takeaway_zh": "稳定且样本高效的 VLA 强化学习微调系统；报告在插灯串插头、台球击球入袋等所有任务上均 30/30 成功，平均只需约 19 分钟在线机器人数据。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2605.25477.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.28429", "title": "Watch, Recall, Act: Always-On Robots in Concurrent Embodied Streams", "type": "paper", "arxiv": "2609.28429", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "streaming", "memory", "bimanual", "asynchronous", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Always-on dual-arm robot: a π0.5 backbone with three light async modules that watch the live stream, track embodied state and recall its own past actions. Reports 45% vs 28% on the combined task.", "takeaway_zh": "“永远在线”的双臂机器人：在 π0.5 主干上加三个轻量异步模块，分别监看实时流、跟踪本体状态、回忆自己过去的动作，两臂可并行执行。报告综合任务成功率 45%，最强基线 28%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.28429.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.15397", "title": "ActionCodec: What Makes for Good Action Tokenizers", "type": "paper", "arxiv": "2602.15397", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "action-tokenizer", "information-theory", "design-principles", "libero", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Derives design rules for action tokenizers from VLA optimisation (token overlap, low redundancy, independence) and builds one: a 2.2B SmolVLM2 reaches 95.5% on LIBERO with no robot pretraining.", "takeaway_zh": "从 VLA 优化角度总结动作 tokenizer 的设计原则（时间重叠、低冗余、模态互信息、独立性）并据此构建 ActionCodec：2.2B 的 SmolVLM2 在无机器人预训练下 LIBERO 达 95.5%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.15397.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.06575", "title": "Think Proprioceptively: State-Grounded Visual Token Selection for VLA Policies", "type": "paper", "arxiv": "2602.06575", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "proprioception", "token-pruning", "efficiency", "calvin", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Turns robot state into VLM-vocabulary tokens that, with the instruction, choose which image patches the VLM processes. Keeping ~12% of visual tokens beats the full-token baseline on CALVIN with lower latency.", "takeaway_zh": "把机器人状态离散成 VLM 词表 token，与指令一起决定哪些图像块进入 VLM 计算。仅保留约 12% 的视觉 token 就在 CALVIN 上超过全 token 基线，同时降低延迟。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.06575.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.17937", "title": "ThinkingVLA: Interleaved Vision and Language Reasoning for Robotic Manipulation", "type": "paper", "arxiv": "2606.17937", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "reasoning", "chain-of-thought", "image-prediction", "mixture-of-transformers", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Interleaves text and image reasoning in one model: a forward chain predicts the next subgoal image, an inverse chain reasons about the actions to reach it, then actions are generated. Biggest gains on long tasks.", "takeaway_zh": "在同一模型中交替进行文本与图像推理：正向推理预测下一个子目标图像，逆向推理据此分析如何到达，再生成动作。长程任务提升最大。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.17937.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.16160", "title": "FlowVLA: Enabling World Dynamics Understanding through Pluggable 3D Flow Learning", "type": "paper", "arxiv": "2610.16160", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "3d-flow", "auxiliary-loss", "dynamics", "data-efficiency", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Training-only 3D flow heads (point-level and gripper-level) teach a 2D VLA how the scene moves; no extra sensors or inference cost. Improves success, zero-shot robustness and data efficiency.", "takeaway_zh": "仅在训练时加入 3D 流预测头（点级与末端级双重监督），让 2D VLA 学到场景动力学；推理时无需额外传感器或计算。提升成功率、零样本鲁棒性与数据效率。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.16160.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.14852", "title": "Towards Human-like Physical Intelligence: Lifelong Vision-Language-Action Learning for Robotic Manipulation", "type": "paper", "arxiv": "2607.14852", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "lifelong-learning", "lora", "replay", "forgetting", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Lifelong VLA learning with short-term and long-term LoRA adapters mixed by a task-aware gate, plus cheap stochastic replay. Learns new skills while keeping old ones; tested on a real xArm.", "takeaway_zh": "终身学习 VLA：短期与长期两条 LoRA 适配通路由任务感知门控融合，再配合低存储的随机回放，兼顾学新与保旧。在真实 xArm 上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.14852.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.31132", "title": "ELASTIC: Efficiently Learning to Adaptively Scale Test-Time Compute for Generative Control Policies", "type": "paper", "arxiv": "2606.31132", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "test-time-compute", "diffusion-policy", "meta-rl", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns, with RL, a meta-policy that picks per state how many denoising steps (sequential) and candidate samples (parallel) a frozen diffusion/flow policy gets, trading success against compute.", "takeaway_zh": "用强化学习训练一个元策略，按状态决定冻结的扩散/流策略在每步分配多少去噪步数（串行）和多少候选采样（并行），在成功率与计算量之间权衡。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.31132.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.27384", "title": "FlashVLA: Streaming Action Decoding for Fast and Asynchronous VLA Inference", "type": "paper", "arxiv": "2608.27384", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "vla", "latency", "async-execution", "flow-matching", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Keeps a streaming buffer of action chunks at different noise levels with chunk-wise causal attention, so each inference step emits one executable chunk and asynchronous execution stays smooth.", "takeaway_zh": "维护一个包含多个不同噪声水平动作块的流式缓冲区，并使用块级因果注意力，使每次推理产出一个可执行动作块，异步执行时动作保持连贯。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.27384.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.22836", "title": "Cloak: Zero-Shot Cross-Embodiment Manipulation by Masking the End-Effector from the VLA", "type": "paper", "arxiv": "2606.22836", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "cross-embodiment", "wrist-camera", "masking", "zero-shot", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Masks the end-effector in the wrist camera (rendered from known geometry, plus mask augmentation). A VLA trained on one parallel gripper then transfers zero-shot to another gripper, another arm and a five-finger hand.", "takeaway_zh": "在腕部相机画面中遮住末端执行器（依据已知几何实时渲染遮罩，并做遮罩增强）。只用单一平行夹爪数据训练的 VLA 可零样本迁移到其他夹爪、其他机械臂和五指手。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.22836.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.22895", "title": "H-VLA: Hierarchical Vision-Language-Action Model with Key-Action Reasoning and Motion Planning in a Unified Action Space", "type": "paper", "arxiv": "2609.22895", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "vla", "hierarchical", "key-actions", "simplerenv" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Splits a VLA into a key-action model (next subgoal) and a motion model (dense actions toward it) in a shared camera-centric action space across datasets and robots. Reports 91% on SimplerEnv Google Robot visual matching.", "takeaway_zh": "把 VLA 拆为关键动作模型（预测下一个子目标）与运动规划模型（生成通往它的稠密动作），并统一到以相机为中心的动作空间。在 SimplerEnv Google Robot 视觉匹配上报告 91%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.22895.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.27663", "title": "Direct Action-Head Injection of A Grounded 3D Point Unlocks Spatial and Task Generalization", "type": "paper", "arxiv": "2606.27663", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "vla", "grounding", "3d", "libero-pro", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Feeds a grounded 3D target point through a two-layer MLP straight into a VLA's action head. On LIBERO-PRO it lifts GR00T-N1.6 from 31.2 to 77.5 under task perturbation; π0.5 gains similarly.", "takeaway_zh": "把一个已定位的 3D 目标点经两层 MLP 直接注入 VLA 的动作头。在 LIBERO-PRO 任务扰动下，GR00T-N1.6 成功率从 31.2 提升到 77.5，π0.5 也有类似提升。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.27663.html", "labs": [], "code_license": null }, { "id": "arxiv-2605.30484", "title": "ELAN4D: Embodiment-Centric 4D Supervision for Vision-Language-Action Models via Plug-and-Play Adaptation", "type": "paper", "arxiv": "2605.30484", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "vla", "auxiliary-loss", "keypoint-tracks", "libero", "robotwin" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Adds an auxiliary branch that predicts future 3D tracks of robot keypoints, computed for free by forward kinematics, with gradient isolation from the VLM. The branch is dropped at inference.", "takeaway_zh": "增加一个辅助分支，预测机器人关键点未来的 3D 轨迹（由正运动学免费得到），并与视觉语言主干做梯度隔离。推理时丢弃该分支，策略接口不变。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2605.30484.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.12402", "title": "DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?", "type": "paper", "arxiv": "2606.12402", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "test-time-compute", "vlm-planner", "routing", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "A router picks a VLM planner per subtask from scene context (CoT depth, model size, memory). On a Franka DROID setup it matches a stronger model's success at up to 65% lower average latency.", "takeaway_zh": "路由器根据场景上下文为每个子任务选择 VLM 规划器（思维链深度、模型规模、记忆长度）。在 Franka DROID 平台上以最多降低 65% 的平均延迟达到更强模型的成功率。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12402.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.13335", "title": "Information-Theoretic Constraints for Continual Vision-Language-Action Alignment", "type": "paper", "arxiv": "2603.13335", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "continual-learning", "vla", "forgetting", "libero" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Info-VLA fights forgetting in continual VLA learning by keeping cross-modal structure: replay anchors from a frozen teacher for contrastive alignment, plus a vision-language mutual-information term.", "takeaway_zh": "Info-VLA 通过保持跨模态信息结构来缓解 VLA 持续学习中的遗忘：以冻结教师模型构造回放锚点做对比对齐，并加入视觉-语言互信息约束。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.13335.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.13355", "title": "Real-Time Execution with Autoregressive Policies", "type": "paper", "arxiv": "2606.13355", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "autoregressive", "async-execution", "tokenization", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Shows autoregressive policies can run in real time by tuning the tokenization horizon and constrained decoding under strict latency bounds; reports they beat an equivalent flow-matching policy.", "takeaway_zh": "说明通过调整分词时域并在严格延迟上限下做约束解码，自回归策略也能实时执行；报告其表现优于同级别的流匹配策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.13355.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.02745", "title": "SeeTraceAct: Visibility-Aware Latent Planning from Cross-Embodiment Demonstration Videos", "type": "paper", "arxiv": "2606.02745", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "one-shot", "demo-conditioned", "vla", "human-video", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "A VLA conditioned on one demo video of an unseen task predicts visibility-aware future end-effector traces for precise grounding. Releases RoboCasa-DC; reports +9.5 pts real-world success from human demos.", "takeaway_zh": "以未见任务的一段示教视频为条件的 VLA，通过预测考虑可见性的未来末端轨迹实现精确定位。发布 RoboCasa-DC 基准；在以人类示教为条件的真机实验中成功率提升 9.5 个百分点。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.02745.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.14997", "title": "Barrier-Constrained Guidance For Real-time Asynchronous VLA Execution", "type": "paper", "arxiv": "2610.14997", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "async-execution", "training-free", "cbf", "flow-matching" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "FreeBar: training-free plugin that keeps consecutive action chunks of flow-based VLAs consistent with a CBF-style constraint solved in closed form, with little extra inference cost.", "takeaway_zh": "FreeBar：无需训练的推理插件，用闭式求解的类 CBF 一致性约束让流式 VLA 的相邻动作块与已执行动作衔接，额外推理开销很小。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.14997.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.32312", "title": "MAST-VLA: Skill-Conditioned Adaptation with Trajectory-Affordance Priors for Multi-Skill Robot Manipulation", "type": "paper", "arxiv": "2610.32312", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "vla", "lora-moe", "affordance", "calvin" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Adds skill-conditioned LoRA mixture-of-experts adapters to the LLM backbone and turns a predicted affordance heatmap into a spatial prior for the action head. Reports Avg. Len 4.65 on CALVIN ABC→D.", "takeaway_zh": "在大模型主干中加入以技能为条件的 LoRA 混合专家适配器，并把预测的可供性热图转为动作头的空间先验。在 CALVIN ABC→D 上报告平均完成长度 4.65。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.32312.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.24705", "title": "Relational Spatial Alignment for Vision-Language-Action Models", "type": "paper", "arxiv": "2610.24705", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "vla", "3d", "representation-alignment", "spatial-reasoning" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Aligning VLA features point-wise with 3D foundation models causes rank collapse; aligning pairwise relations instead adds 3D awareness without losing expressivity. Adds a new spatial reasoning benchmark.", "takeaway_zh": "将 VLA 特征逐点对齐到 3D 基础模型会导致秩坍塌；改为对齐成对关系，可在不损失表达力的前提下注入 3D 空间感知。并提出新的空间推理基准。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.24705.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.09258", "title": "Back to the Familiar Future: Failure Recovery for VLA Policies via Pre-Imagined Milestone Selection", "type": "paper", "arxiv": "2606.09258", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "failure-recovery", "vla", "foresight", "libero" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "B2FF: before execution the VLA imagines a bank of future milestone images; after a deviation a selector picks a reachable one as a fixed visual goal. Failure-injected LIBERO: 56.3% → 74.0%, no fine-tuning.", "takeaway_zh": "B2FF：执行前让 VLA 想象一组未来里程碑图像；偏离轨迹后，由选择器挑出可恢复的那个作为固定视觉目标。在注入失败的 LIBERO 上成功率从 56.3% 升至 74.0%，无需微调。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.09258.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.13732", "title": "LAPT-Scale: Scaling Embodied Policies with Latent Action Pre-Training on Human Videos", "type": "paper", "arxiv": "2610.13732", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "latent-actions", "human-video", "pretraining", "scaling", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Continuous (not vector-quantized) latent actions learned from 23.3K hours of egocentric human video serve as pre-training labels for a VLA; reports consistent scaling gains.", "takeaway_zh": "从 2.33 万小时第一人称人类视频中学习连续（非向量量化）的潜动作，结合感知、语义与光流对齐信号，作为 VLA 预训练的动作监督；报告随规模持续提升。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.13732.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.26455", "title": "ALIGN: Aligning Condition Estimation and Expression in Advantage-Conditioned Behavior Cloning", "type": "paper", "arxiv": "2610.26455", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "advantage-conditioning", "behavior-cloning", "mixed-quality-data", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Advantage-conditioned BC for VLAs: predicts pairwise conditions directly and injects them into the DiT action expert via AdaRMS. On real retry-heavy data: 90.0% vs 56.7% (bowl stacking), 56.7% vs 33.3% (key hanging).", "takeaway_zh": "面向 VLA 的优势条件行为克隆：直接预测成对条件，并通过 AdaRMS 注入 DiT 动作专家。在含大量重试的真机数据上，叠碗成功率 90.0%（基线 56.7%），挂钥匙 56.7%（基线 33.3%）。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.26455.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.21582", "title": "Scale Up Strategically: Learning Compositional Generalization via Bias-Aware Evaluation and Data Collection for Robotic Manipulation", "type": "paper", "arxiv": "2607.21582", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "compositional-generalization", "shortcut-bias", "data-collection", "evaluation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Measures which instruction factors policies over-rely on: across six foundation policies, color ≥ object ≥ spatial ≥ verb ≥ size. Collecting more data for under-grounded factors wins with half the demos.", "takeaway_zh": "量化策略过度依赖哪些指令要素：六个基础策略普遍呈现颜色 ≥ 物体 ≥ 空间 ≥ 动词 ≥ 尺寸的顺序。把采集预算倾斜给欠落地的要素，只用一半示教即可胜出。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.21582.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.14698", "title": "Lights, Camera, Malfunction: When Illumination Robustness Leaves VLA Models Blind to Color", "type": "paper", "arxiv": "2607.14698", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "adversarial", "robustness", "vla", "lighting", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "FLARE, a physical spotlight attack, drops VLA success to zero. Naive augmentation defenses make models ignore color (≤47.5% on color tasks); ChromaGuard keeps color and reaches 97.5% and 92.5% on a real arm.", "takeaway_zh": "FLARE 物理聚光灯攻击可让 VLA 成功率降为零。朴素的增强防御会让模型忽略颜色（颜色相关任务至多 47.5%）；ChromaGuard 保留色彩信息，在真机上达到 97.5% 与 92.5%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.14698.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.06114", "title": "Where Success Breaks: Failure-Boundary Learning for Robust Vision-Language-Action Models", "type": "paper", "arxiv": "2609.06114", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "vla", "failure-boundary", "digital-twin", "flow-matching", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "DLS finds where VLA success breaks via on-policy digital-twin rollouts, locates the crossing with privileged progress signals, and shapes the flow toward success-producing denoising directions.", "takeaway_zh": "DLS 通过数字孪生中的在策略推演大规模发现 VLA 成功与失败的边界，用特权进度信号定位越界位置，并据此强化导向成功的去噪方向、抑制导向失败的方向。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.06114.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.02486", "title": "Intercepting the Future: Latent-Space Predictive World Model for Dynamic VLA Manipulation", "type": "paper", "arxiv": "2606.02486", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "dynamic-manipulation", "latent-world-model", "openvla", "latency" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "AHEAD wraps a frozen VLA with a small latent world model that forecasts future patch tokens from optical-flow velocity and feeds them to the action decoder. +4.9M params on a 7B OpenVLA; 79-97% on 20 dynamic sim scenes.", "takeaway_zh": "AHEAD 为冻结的 VLA 加上一个小型潜空间世界模型，依据光流速度预测未来图像块 token 并交给动作解码器，以应对运动物体。在 7B OpenVLA 上只增加 490 万参数，20 个动态仿真场景成功率 79–97%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.02486.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.12285", "title": "PLaW-VLA: Predictive Latent World Modeling for Vision-Language-Action Policies", "type": "paper", "arxiv": "2610.12285", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "vla", "latent-world-model", "mixture-of-transformers", "robotwin" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Predicts task-relevant future states in a pretrained prediction-oriented feature space (no pixel reconstruction) and conditions actions on them via causal attention. +11.8 pts over reactive policies on RoboTwin.", "takeaway_zh": "在预训练的面向预测的特征空间中预测与任务相关的未来状态（不做像素重建），并通过结构化因果注意力让动作生成以其为条件。在 RoboTwin 困难长时任务上比反应式策略高 11.8 个百分点。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.12285.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.25981", "title": "FR-VLA: Implicit Force Reasoning VLA for Contact-Rich Manipulation", "type": "paper", "arxiv": "2610.25981", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "vla", "force", "contact-rich", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "FR-VLA uses learnable force query tokens to let vision-language context decide how much measured force should enter the action pathway, rather than feeding force in through a fixed path. Five real tasks, two robots.", "takeaway_zh": "FR-VLA 通过可学习的力查询 token，由视觉语言上下文判断测得的力应在多大程度上进入动作生成通路，而不是走固定的注入路径。在两种机器人的五个接触密集任务上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25981.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.04708", "title": "VISTA: Vision-Grounded and Physics-Validated Adaptation of UMI data for VLA Training", "type": "paper", "arxiv": "2606.04708", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "umi", "vla", "fisheye", "data-filtering", "vqa" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Makes UMI data usable for VLAs: a fisheye wrist-view VQA dataset aligns the VLM to distorted views, and a physics validation pipeline scores trajectories for kinematic limits, collisions and controller bandwidth.", "takeaway_zh": "让 UMI 数据可用于训练 VLA：构建腕部鱼眼视角的 VQA 数据集使 VLM 适应畸变画面，并用物理校验流程对轨迹的运动学极限、碰撞与控制带宽进行评分筛选。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.04708.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.34550", "title": "Gaze Prompts: Temporally Dense Human Attention for Vision-Language-Action Fine-Tuning", "type": "paper", "arxiv": "2609.34550", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "gaze", "vla", "teleoperation", "bimanual", "dataset" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Gaze recorded in VR teleop is drawn as crosshairs on training images; at test time a small predictor supplies the gaze. With π0: 26.3% → 56.0% on six real bimanual tasks. Releases GazeMani (1,200 trajectories).", "takeaway_zh": "把 VR 遥操作中记录的注视点画成十字准星叠加在训练图像上，部署时由轻量预测器估计注视点。基于 π0，六个真实双臂任务平均成功率从 26.3% 升至 56.0%；发布 GazeMani 数据集（1200 条轨迹）。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.34550.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.20698", "title": "SafeDojo: Safe Reinforcement Learning for VLA via Interactive World Model", "type": "paper", "arxiv": "2606.20698", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "safe-rl", "world-model", "vla", "grpo" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Online safe RL for VLAs inside an interactive video world model: a success classifier and a contact-probability safety head are balanced with Lagrangian-constrained GRPO. +8.25 pts safe success on SafeLIBERO Level I.", "takeaway_zh": "在可交互视频世界模型中对 VLA 做在线安全强化学习：成功分类器与预测接触概率的安全头，经拉格朗日约束的 GRPO 平衡。在 SafeLIBERO Level I 上安全成功率提升 8.25 个百分点。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.20698.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.12628", "title": "Beyond Imitation: Reinforcement Learning-Based Sim-Real Co-Training for VLA Models", "type": "paper", "arxiv": "2602.12628", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "sim-real-co-training", "rl", "vla", "openvla", "pi0.5" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "RL-Co: SFT on mixed real and sim demos, then RL in simulation with an auxiliary supervised loss on real data to avoid forgetting. Tested on four real tabletop tasks with OpenVLA and π0.5.", "takeaway_zh": "RL-Co：先在真实与仿真混合示教上做监督微调，再在仿真中强化学习，同时加入真实数据的辅助监督损失以防遗忘。用 OpenVLA 和 π0.5 在四个真实桌面任务上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.12628.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.24448", "title": "Supervise What Survives: Geometry-Guided VLA Adaptation from Synthetic Robot Videos", "type": "paper", "arxiv": "2606.24448", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "synthetic-video", "vla", "representation-alignment", "human-to-robot" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Generated robot videos keep geometry but not control, so GRA uses them only to supervise visual perception (aligned to extracted geometry) and leaves action learning to real demos.", "takeaway_zh": "生成的机器人视频保留了几何轨迹却丢失了控制信号，因此 GRA 只用它们监督视觉感知（与提取的几何对齐），动作学习仍交给真实示教。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.24448.html", "labs": [], "code_license": null }, { "id": "arxiv-2509.22407", "title": "EMMA: Enhancing Robotic Control Robustness through a Generative Visual Model", "type": "paper", "arxiv": "2509.22407", "year": 2025, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "data-augmentation", "video-editing", "vla", "generalization" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "EMMA: a diffusion-transformer video editor (DreamTransfer) changes foreground, background and lighting with multi-view consistency, and AdaMix weights real and generated samples by policy performance.", "takeaway_zh": "EMMA：扩散 Transformer 视频编辑器 DreamTransfer 在保持多视角一致的前提下修改前景、背景与光照，AdaMix 再按策略表现为真实与生成样本自适应加权。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2509.22407.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.14028", "title": "AdvDex: Learning Dexterous Manipulation from Human Demonstrations via Joint-Aligned Actions and Adversarial Learning", "type": "paper", "arxiv": "2608.14028", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "dexterous", "vla", "human-demos", "cross-embodiment", "tactile", "dataset" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "AdvDex trains a dexterous VLA on human and robot demos: OmniShare human data with touch, a joint-aligned action space (wrist pose + 15 finger joints) shared by hands and grippers, and adversarial removal of body cues.", "takeaway_zh": "AdvDex 用人类与机器人示教训练灵巧操作 VLA：含触觉的人类示教数据集 OmniShare、人手/灵巧手/夹爪共用的关节对齐动作空间（腕部位姿 + 15 个手指关节），并以对抗学习去除本体相关特征。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.14028.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.19248", "title": "PACER: Process-Aware VLA Policy Learning from Human Corrections for Robotic Disassembly", "type": "paper", "arxiv": "2610.19248", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "human-corrections", "disassembly", "fine-tuning", "sft" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "PACER fine-tunes a VLA for electronics disassembly on its own rollouts plus human corrections, reweighting action chunks by progress, source and outcome; 87/100 vs 54/100 for plain SFT on motherboard parts.", "takeaway_zh": "PACER 用策略自身的执行轨迹和人工纠正数据微调用于电子产品拆解的 VLA，按进度、来源和结果为动作块加权；在主板部件拆解上成功 87/100，普通 SFT 为 54/100。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.19248.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.23614", "title": "CompVLA: A Variable Compliance Vision-Language-Action Model for Contact-rich Manipulation", "type": "paper", "arxiv": "2609.23614", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "compliance", "impedance-control", "contact-rich" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "CompVLA adds a compliance expert to a VLA that predicts time-varying stiffness and virtual displacement alongside motion, executed with geometric impedance control for contact-rich tasks.", "takeaway_zh": "CompVLA 为 VLA 增加柔顺专家模块，在预测运动的同时输出时变刚度与虚拟位移，并通过几何阻抗控制执行，用于接触密集任务。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.23614.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.26584", "title": "HAF-VLA: Hierarchical Action Flow Framework for Humanoid Vision-Language-Action Control", "type": "paper", "arxiv": "2610.26584", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "humanoid", "loco-manipulation", "flow-matching", "hierarchical" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "HAF-VLA adapts a flow-matching VLA to humanoid loco-manipulation by denoising in stages, locomotion and head first, then waist, then full body, linked by a cross-stage KV cache; six real tasks.", "takeaway_zh": "HAF-VLA 将流匹配 VLA 适配到人形移动操作：分阶段去噪，先生成移动与头部指令，再细化腰部，最后输出全身操作动作，各阶段通过跨阶段 KV 缓存衔接；在六个真实任务上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.26584.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.29172", "title": "CLIFT: Turning Gemini Robotics On-Device into Humanoid Specialists via Non-Invasive Closed-Loop Iterative Fine-Tuning", "type": "paper", "arxiv": "2607.29172", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "humanoid", "fine-tuning", "closed-weight", "flywheel" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Studies adapting closed-weight Gemini Robotics On-Device on a humanoid via a managed fine-tuning API; CLIFT turns deployment reward feedback into SFT data, reaching near-perfect success after two cycles.", "takeaway_zh": "研究通过托管微调 API 在人形机器人上适配闭源权重的 Gemini Robotics On-Device；CLIFT 把部署时的奖励反馈转为监督微调数据，两轮迭代后接近完美成功率。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.29172.html", "labs": [], "code_license": null }, { "id": "arxiv-2511.00917", "title": "MAESTRO: Orchestrating Robotics Modules with Coding Agents for Zero-Shot Generalist Robots", "type": "paper", "arxiv": "2511.00917", "year": 2025, "venue": "CoRL 2026", "org": "University of Pennsylvania", "url": null, "code": "https://github.com/penn-pal-lab/maestro", "project": "https://maestro-robot.github.io", "topics": [ "vla" ], "tags": [ "code-as-policy", "coding-agent", "tool-library", "mobile-manipulation", "zero-shot" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-07", "takeaway_en": "No end-to-end training: a VLM coding agent writes robot programs from a library of perception, planning and control tools in a plan-act-replan loop; reports matching or beating VLAs zero-shot on tabletop tasks.", "takeaway_zh": "不训练端到端策略，而让 VLM 作为编程智能体，调用感知、规划与控制工具库编写机器人程序，按“规划-执行-重规划”循环运行；在桌面任务上零样本达到或超过 VLA 模型，并可完成移动操作。", "github_stars": 12, "page": "https://embodied-ai-radar.com/e/arxiv-2511.00917.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.08449", "title": "VFlash: Fast, Accurate Visual Planning via Flash Primitive Decoding", "type": "paper", "arxiv": "2611.08449", "year": 2026, "venue": null, "org": "NVIDIA", "url": null, "code": null, "project": "https://research.nvidia.com/labs/lpr/vflash/", "topics": [ "vla" ], "tags": [ "visual-planning", "trajectory", "decoding", "inference-acceleration" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-08", "takeaway_en": "VLMs plan with points, boxes and trajectories one coordinate token at a time; VFlash drafts a whole primitive in one pass. Reports lower trajectory error, ~25x faster 3D point forecasting, a WAM gain (62.6% → 70.2%).", "takeaway_zh": "用点、框、轨迹做视觉规划的视觉语言模型需要逐个坐标 token 解码；VFlash 用轻量草稿器一次前向生成整个图元。报告轨迹误差更低、三维点运动预测约快 25 倍，接入世界动作模型后成功率 62.6% → 70.2%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08449.html", "labs": [ "nvidia" ], "code_license": null }, { "id": "arxiv-2611.09892", "title": "On Agentic Manipulation Policies", "type": "paper", "arxiv": "2611.09892", "year": 2026, "venue": null, "org": "Apple", "url": null, "code": "https://github.com/apple-aiml-research/ml-amp", "project": "https://apple-aiml-research.github.io/ml-amp/", "topics": [ "vla", "manipulation" ], "tags": [ "agentic", "harness", "generalist-model", "zero-shot", "memory" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-09", "takeaway_en": "A generalist AI model commands the arm directly through a reference harness (AMP1), with no demos or built-in grasp skills; reports 85.6% zero-shot on RoboLab-120 vs 49.2% for the best VLA, and 104 of 108 real trials.", "takeaway_zh": "让通用 AI 模型通过参考框架 AMP1 直接指挥机械臂，无需示教，也不预置抓取技能；在 RoboLab-120 上零样本成功率 85.6%（最佳 VLA 为 49.2%），真机 108 次试验成功 104 次。", "github_stars": 31, "page": "https://embodied-ai-radar.com/e/arxiv-2611.09892.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2611.08917", "title": "PRISM: Primitive Routing via In-context Skill Mixing for Retentive VLA Adaptation", "type": "paper", "arxiv": "2611.08917", "year": 2026, "venue": "NeurIPS 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "lora", "skill-retention", "continual-learning", "routing", "long-horizon" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-09", "takeaway_en": "Stores each skill as a frozen low-rank action-expert adapter and routes per action chunk via a VLM-conditioned router, so a VLA keeps old skills while learning composite tasks; tested on RoboCasa365 and a PIPER arm.", "takeaway_zh": "把每类技能存为冻结的低秩动作专家适配器，由 VLM 条件路由器按动作块选择组合，使 VLA 学习组合任务时不遗忘旧技能；在 RoboCasa365 和 PIPER 真机上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08917.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.09847", "title": "Computer-Use Agents Are Embodied Generalists", "type": "paper", "arxiv": "2611.09847", "year": 2026, "venue": null, "org": "Carnegie Mellon University", "url": null, "code": "https://github.com/Pranjal2041/cua-embodied", "project": "https://pranjal2041.github.io/cua-embodied", "topics": [ "vla" ], "tags": [ "computer-use-agent", "generalist", "position-paper", "competition" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-09", "takeaway_en": "Renders embodied tasks as on-screen software so an unmodified computer-use agent acts through screenshots, mouse and keys; one design placed 1st, 2nd, 3rd and 6th across four ECCV workshop competitions.", "takeaway_zh": "把具身任务渲染成屏幕上的软件，让未经改动的计算机使用智能体通过截图和键鼠完成；同一设计在四项 ECCV 研讨会竞赛中分获第一、二、三和第六名。", "github_stars": 7, "page": "https://embodied-ai-radar.com/e/arxiv-2611.09847.html", "labs": [ "cmu" ], "code_license": "" }, { "id": "arxiv-2611.09041", "title": "DobotTrack: Target-Grounded Vision-Language-Action Learning for Embodied Person Tracking", "type": "paper", "arxiv": "2611.09041", "year": 2026, "venue": null, "org": "Dobot", "url": null, "code": "https://github.com/Dobot-Team/dobot_track", "project": "https://dobot-team.github.io/dobottrack-web/", "topics": [ "vla", "navigation" ], "tags": [ "person-tracking", "quadruped", "grounding", "dataset" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-09", "takeaway_en": "VLA for language-guided person following with mask and box grounding supervision and target-conditioned history compression; the 0.8B model reports 77.9% on EVT-Bench, plus a 126-episode quadruped tracking dataset.", "takeaway_zh": "面向语言引导行人跟随的 VLA：加入掩码与检测框定位监督，并按目标压缩历史视觉信息；0.8B 模型在 EVT-Bench 上平均成功率 77.9%，另发布 126 段四足机器人跟踪数据。", "github_stars": 13, "page": "https://embodied-ai-radar.com/e/arxiv-2611.09041.html", "labs": [], "code_license": "" }, { "id": "arxiv-2611.10443", "title": "Spatial Representation Distillation and Knowledge Routing for Vision-Language-Action Models", "type": "paper", "arxiv": "2611.10443", "year": 2026, "venue": "NeurIPS 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "vla" ], "tags": [ "3d-aware", "distillation", "lora", "token-routing", "libero" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-10", "takeaway_en": "SPARK-VLA distils a frozen 3D expert into a LoRA visual branch beside the original one, and a small router passes only action-relevant tokens to a 0.5B LLM. No 3D input at inference; ~53M trainable parameters.", "takeaway_zh": "SPARK-VLA 把冻结的 3D 专家蒸馏到与原视觉通路并行的 LoRA 分支，并用轻量路由器只把与动作相关的视觉 token 送入 0.5B 语言模型。推理时无需 3D 输入，可训练参数约 5300 万。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.10443.html", "labs": [], "code_license": null }, { "id": "arxiv-2303.04137", "title": "Diffusion Policy: Visuomotor Policy Learning via Action Diffusion", "type": "paper", "arxiv": "2303.04137", "year": 2023, "venue": null, "org": "Columbia, TRI, MIT", "url": null, "code": "https://github.com/real-stanford/diffusion_policy", "project": null, "topics": [ "manipulation" ], "tags": [ "real", "sim", "arm", "diffusion", "imitation" ], "robot_types": [], "highlight": true, "rating": 3, "added": null, "takeaway_en": "Generating action sequences by denoising handles multimodal demos far better than regression; now a standard IL baseline.", "takeaway_zh": "以去噪方式生成动作序列，比回归更能处理多模态演示，已成为模仿学习标准基线。", "github_stars": 4721, "page": "https://embodied-ai-radar.com/e/arxiv-2303.04137.html", "labs": [ "mit", "columbia", "tri" ], "code_license": "MIT" }, { "id": "arxiv-2304.13705", "title": "Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware", "type": "paper", "arxiv": "2304.13705", "year": 2023, "venue": null, "org": "Stanford", "url": null, "code": "https://github.com/tonyzhaozh/act", "project": null, "topics": [ "manipulation", "hardware" ], "tags": [ "real", "bimanual", "imitation", "low-cost" ], "robot_types": [ "arm" ], "highlight": true, "rating": 2, "added": null, "takeaway_en": "ALOHA + ACT — a ~$20k bimanual teleop rig and an action-chunking Transformer that learns precise tasks from ~50 demos.", "takeaway_zh": "ALOHA + ACT：约 2 万美元的双臂遥操作平台，加上动作分块 Transformer，约 50 条演示即可学会精细任务。", "github_stars": 2335, "page": "https://embodied-ai-radar.com/e/arxiv-2304.13705.html", "labs": [ "stanford" ], "code_license": "MIT" }, { "id": "arxiv-2401.02117", "title": "Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation", "type": "paper", "arxiv": "2401.02117", "year": 2024, "venue": null, "org": "Stanford", "url": null, "code": "https://github.com/MarkFzp/mobile-aloha", "project": null, "topics": [ "manipulation", "hardware" ], "tags": [ "real", "bimanual", "mobile", "imitation", "low-cost" ], "robot_types": [ "arm", "mobile" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Puts ALOHA on a mobile base; co-training with static ALOHA data boosts mobile tasks like cooking and opening cabinets.", "takeaway_zh": "将 ALOHA 装上移动底盘；与静态 ALOHA 数据联合训练显著提升做饭、开柜等移动任务表现。", "github_stars": 4479, "page": "https://embodied-ai-radar.com/e/arxiv-2401.02117.html", "labs": [ "stanford" ], "code_license": "MIT" }, { "id": "arxiv-2403.03954", "title": "3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations", "type": "paper", "arxiv": "2403.03954", "year": 2024, "venue": null, "org": "Shanghai Qi Zhi, Tsinghua", "url": null, "code": "https://github.com/YanjieZe/3D-Diffusion-Policy", "project": null, "topics": [ "manipulation" ], "tags": [ "real", "sim", "point-cloud", "diffusion" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Conditions a diffusion policy on a compact point-cloud encoding; more data-efficient and robust to viewpoint than images.", "takeaway_zh": "用紧凑点云编码作为扩散策略条件，比图像输入更省数据、对视角更鲁棒。", "github_stars": 1471, "page": "https://embodied-ai-radar.com/e/arxiv-2403.03954.html", "labs": [ "tsinghua", "qizhi" ], "code_license": "MIT" }, { "id": "arxiv-2402.10329", "title": "Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots", "type": "paper", "arxiv": "2402.10329", "year": 2024, "venue": null, "org": "Stanford, Columbia, TRI", "url": null, "code": "https://github.com/real-stanford/universal_manipulation_interface", "project": null, "topics": [ "manipulation", "hardware", "datasets" ], "tags": [ "real", "arm", "handheld", "data-collection" ], "robot_types": [ "arm" ], "highlight": true, "rating": 2, "added": null, "takeaway_en": "A handheld gripper with a GoPro collects demos anywhere; policies transfer directly to robot arms.", "takeaway_zh": "手持夹爪加 GoPro 可随处采集演示，训练出的策略可直接迁移到机械臂。", "github_stars": 1636, "page": "https://embodied-ai-radar.com/e/arxiv-2402.10329.html", "labs": [ "stanford", "columbia", "tri" ], "code_license": "MIT" }, { "id": "arxiv-2403.07788", "title": "DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation", "type": "paper", "arxiv": "2403.07788", "year": 2024, "venue": null, "org": "Stanford", "url": null, "code": null, "project": null, "topics": [ "manipulation", "hardware" ], "tags": [ "real", "hand", "data-collection" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A wearable mocap glove + camera rig records human hand motion that is retargeted to train dexterous robot hands.", "takeaway_zh": "可穿戴动捕手套与相机采集人手动作，重定向后用于训练灵巧机器人手。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2403.07788.html", "labs": [ "stanford" ], "code_license": null }, { "id": "arxiv-1808.00177", "title": "Learning Dexterous In-Hand Manipulation", "type": "paper", "arxiv": "1808.00177", "year": 2018, "venue": null, "org": "OpenAI", "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "sim2real", "hand", "rl" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A Shadow Hand reorients a cube using RL trained purely in randomized simulation — a sim-to-real milestone.", "takeaway_zh": "完全在随机化仿真中用强化学习训练，Shadow 灵巧手在真实世界翻转方块，是 sim-to-real 里程碑。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1808.00177.html", "labs": [ "openai" ], "code_license": null }, { "id": "arxiv-1910.07113", "title": "Solving Rubik's Cube with a Robot Hand", "type": "paper", "arxiv": "1910.07113", "year": 2019, "venue": null, "org": "OpenAI", "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "sim2real", "hand", "rl" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Automatic domain randomization lets a sim-trained hand policy manipulate a Rubik's cube on real hardware.", "takeaway_zh": "自动域随机化让仿真训练的灵巧手策略在真实硬件上操作魔方。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1910.07113.html", "labs": [ "openai" ], "code_license": null }, { "id": "arxiv-2211.11744", "title": "Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes", "type": "paper", "arxiv": "2211.11744", "year": 2022, "venue": null, "org": "MIT", "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "sim2real", "hand", "rl", "depth" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Reorients unseen objects in-hand using only a depth camera, with a policy trained in simulation.", "takeaway_zh": "仅凭深度相机即可在手中翻转未见过的物体，策略在仿真中训练。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2211.11744.html", "labs": [ "mit" ], "code_license": null }, { "id": "arxiv-2307.05973", "title": "VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models", "type": "paper", "arxiv": "2307.05973", "year": 2023, "venue": null, "org": "Stanford", "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "real", "arm", "llm", "zero-shot" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "LLMs write code that composes 3D affordance/constraint maps, which a planner turns into trajectories — no robot training.", "takeaway_zh": "大模型编写代码组合 3D 可供性/约束地图，再由规划器生成轨迹，无需机器人训练。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2307.05973.html", "labs": [ "stanford" ], "code_license": null }, { "id": "arxiv-2203.12601", "title": "R3M: A Universal Visual Representation for Robot Manipulation", "type": "paper", "arxiv": "2203.12601", "year": 2022, "venue": null, "org": "Stanford, Meta AI", "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "representation", "egocentric-video" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Pretrains a visual encoder on Ego4D human videos that makes downstream imitation far more data-efficient.", "takeaway_zh": "在 Ego4D 人类视频上预训练视觉编码器，显著提高下游模仿学习的数据效率。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2203.12601.html", "labs": [ "stanford", "meta" ], "code_license": null }, { "id": "arxiv-2610.17350", "title": "Can a Frontier VLM Serve as a Robot Policy for Memory-Dependent Manipulation?", "type": "paper", "arxiv": "2610.17350", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/jackbjed/embodied-astra-robomme", "project": "https://embodied-astra.github.io", "topics": [ "manipulation" ], "tags": [ "sim", "vlm", "memory", "zero-shot", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-19", "takeaway_en": "A frozen frontier VLM, prompted with the whole episode, outputs waypoints directly and reports 92.1% on RoboMME memory tasks with no task training.", "takeaway_zh": "冻结的前沿 VLM 以整段回合为上下文直接输出路径点，在 RoboMME 记忆类任务上报告 92.1% 成功率，且无需任务训练。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2610.17350.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.23849", "title": "Blind Precise Assembly: Proprioceptive Sim-to-Real Peg-in-Hole for Diverse Parts", "type": "paper", "arxiv": "2610.23849", "year": 2026, "venue": null, "org": "MIT", "url": null, "code": null, "project": null, "topics": [ "manipulation", "sim" ], "tags": [ "sim-to-real", "assembly", "proprioception", "contact-rich" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-19", "takeaway_en": "Asks whether joint readings alone suffice for millimetre-precise insertion — trains sim-to-real policies with no camera, force or tactile input.", "takeaway_zh": "探究仅靠关节读数能否完成毫米级精密插装：训练不用相机、力或触觉输入的 sim-to-real 策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.23849.html", "labs": [ "mit" ], "code_license": null }, { "id": "arxiv-2610.17802", "title": "Beyond the End-Effector: Learning Whole-Arm Contact Manipulation from Human Demonstrations", "type": "paper", "arxiv": "2610.17802", "year": 2026, "venue": "CoRL 2026", "org": "USC", "url": null, "code": null, "project": "https://wacl-robot.github.io", "topics": [ "manipulation" ], "tags": [ "whole-arm", "non-prehensile", "human-demos" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-19", "takeaway_en": "WACL learns from human demos to push and hold with the whole arm, choosing where on the arm to make contact while keeping the gripper's constraints.", "takeaway_zh": "WACL 从人类示范中学习用整条手臂推、抵物体，自主选择手臂上的接触位置，同时满足末端执行器的约束。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.17802.html", "labs": [ "usc" ], "code_license": null }, { "id": "arxiv-2610.23479", "title": "HexaShell: A Universal Harness for Robot Coding Agents in Simulation and the Real World", "type": "paper", "arxiv": "2610.23479", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/HexaFuture/HexaShell", "project": null, "topics": [ "manipulation", "vla" ], "tags": [ "real", "bimanual", "vlm-agent", "code-as-policy" ], "robot_types": [ "arm" ], "highlight": false, "rating": 1, "added": "2026-10-19", "takeaway_en": "A coding agent drives a bimanual robot through one command line; after each failure it reads the logs and writes or fixes small reusable tool programs.", "takeaway_zh": "编程智能体通过一条命令行驱动双臂机器人；每次失败后读取日志，编写或修复可复用的小工具程序。", "github_stars": 363, "page": "https://embodied-ai-radar.com/e/arxiv-2610.23479.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2210.02697", "title": "DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation", "type": "dataset", "arxiv": "2210.02697", "year": 2022, "venue": null, "org": "PKU, BIGAI", "url": null, "code": "https://github.com/PKU-EPIC/DexGraspNet", "project": null, "topics": [ "manipulation", "datasets" ], "tags": [ "sim", "dexterous-hand", "grasping", "large-scale" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Over a million physically validated ShadowHand grasps on thousands of objects, synthesized by optimizing a differentiable force-closure energy; a standard training set for dexterous grasp models.", "takeaway_zh": "通过优化可微的力封闭能量合成、并经物理仿真验证的百万级 ShadowHand 抓取，覆盖数千个物体，是灵巧抓取模型的常用训练集。", "github_stars": 482, "page": "https://embodied-ai-radar.com/e/arxiv-2210.02697.html", "labs": [ "pku" ], "code_license": "" }, { "id": "arxiv-2311.16098", "title": "On Bringing Robots Home", "type": "paper", "arxiv": "2311.16098", "year": 2023, "venue": null, "org": "NYU, Meta", "url": null, "code": "https://github.com/notmahi/dobb-e", "project": null, "topics": [ "manipulation" ], "tags": [ "home-robot", "low-cost-data", "imitation", "open-source" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Dobb-E collects home demonstrations with a cheap reacher-grabber \"stick\", pretrains on them, then adapts to a new household task from a few minutes of data.", "takeaway_zh": "Dobb-E 用低成本的手持夹取“棒”采集家庭示范数据并预训练，在新家庭任务上只需几分钟数据即可适配。", "github_stars": 620, "page": "https://embodied-ai-radar.com/e/arxiv-2311.16098.html", "labs": [ "meta", "nyu" ], "code_license": "MIT" }, { "id": "arxiv-2610.23888", "title": "MoDex: Towards Universal Multi-Object Dexterous Grasping", "type": "paper", "arxiv": "2610.23888", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Aurora-Xiong/MoDex", "project": null, "topics": [ "manipulation" ], "tags": [ "dexterous-hand", "grasping", "multi-object", "residual-learning" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-10-20", "takeaway_en": "Grasps several objects at once with a dexterous hand by learning one-step residual corrections to a reference trajectory, with a strategy that adapts to how the objects are laid out.", "takeaway_zh": "让灵巧手一次抓取多个物体：学习对参考轨迹的单步残差修正，并根据物体的空间布局调整抓取策略。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2610.23888.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.26507", "title": "Learn the Task, Not the Schedule: Reducing Spurious Cross-Arm Coupling in Bimanual Imitation Learning", "type": "paper", "arxiv": "2610.26507", "year": 2026, "venue": null, "org": "SJTU", "url": null, "code": "https://github.com/MINT-SJTU/CTR", "project": null, "topics": [ "manipulation", "learning" ], "tags": [ "bimanual", "imitation", "data-augmentation" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-10-20", "takeaway_en": "CTR retimes the two arms in bimanual demos at training time while keeping task-required constraints, so the policy stops using the other arm's progress as a shortcut cue.", "takeaway_zh": "CTR 在训练时对双臂示教中两只手臂的动作时序重新组合，同时保留任务必需的约束，避免策略把另一只手臂的进度当作捷径线索。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2610.26507.html", "labs": [ "sjtu" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2010.14406", "title": "Transporter Networks: Rearranging the Visual World for Robotic Manipulation", "type": "paper", "arxiv": "2010.14406", "year": 2020, "venue": "CoRL 2020", "org": "Google", "url": null, "code": "https://github.com/google-research/ravens", "project": null, "topics": [ "manipulation" ], "tags": [ "sim", "real", "pick-and-place", "rearrangement", "equivariance" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Transporter Nets — pick-and-place as spatial matching of deep features cropped around the pick point; very sample-efficient on rearrangement tasks, released with the Ravens benchmark.", "takeaway_zh": "Transporter 网络：把抓取-放置建模为以抓取点为中心的深度特征的空间匹配，在重排类任务上样本效率很高，并随论文发布 Ravens 基准。", "github_stars": 629, "page": "https://embodied-ai-radar.com/e/arxiv-2010.14406.html", "labs": [ "google-deepmind" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2109.12098", "title": "CLIPort: What and Where Pathways for Robotic Manipulation", "type": "paper", "arxiv": "2109.12098", "year": 2021, "venue": "CoRL 2021", "org": "University of Washington, NVIDIA", "url": null, "code": "https://github.com/cliport/cliport", "project": null, "topics": [ "manipulation", "vla" ], "tags": [ "sim", "real", "language", "clip", "pick-and-place" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "CLIPort — combines CLIP's semantic \"what\" stream with Transporter's spatial \"where\" stream for language-conditioned tabletop manipulation; an early bridge from web vision-language models to robot actions.", "takeaway_zh": "CLIPort：把 CLIP 提供的语义\"是什么\"通路与 Transporter 的空间\"在哪里\"通路结合，实现语言条件的桌面操作；是较早把网络视觉语言模型接入机器人动作的工作。", "github_stars": 548, "page": "https://embodied-ai-radar.com/e/arxiv-2109.12098.html", "labs": [ "nvidia", "uw" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2209.05451", "title": "Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation", "type": "paper", "arxiv": "2209.05451", "year": 2022, "venue": "CoRL 2022", "org": "University of Washington, NVIDIA", "url": null, "code": "https://github.com/peract/peract", "project": null, "topics": [ "manipulation", "vla" ], "tags": [ "sim", "real", "voxel", "rlbench", "language", "keyframe" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "PerAct — a Perceiver Transformer over voxelized RGB-D observations that predicts the next keyframe gripper pose as voxel classification; one language-conditioned policy for many RLBench and real tasks.", "takeaway_zh": "PerAct：在体素化的 RGB-D 观测上运行 Perceiver Transformer，把下一关键帧夹爪位姿当作体素分类来预测；一个语言条件策略即可覆盖众多 RLBench 与真机任务。", "github_stars": 497, "page": "https://embodied-ai-radar.com/e/arxiv-2209.05451.html", "labs": [ "nvidia", "uw" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2306.14896", "title": "RVT: Robotic View Transformer for 3D Object Manipulation", "type": "paper", "arxiv": "2306.14896", "year": 2023, "venue": "CoRL 2023", "org": "NVIDIA", "url": null, "code": "https://github.com/NVlabs/RVT", "project": null, "topics": [ "manipulation" ], "tags": [ "sim", "real", "multi-view", "rlbench", "keyframe" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "RVT — re-renders the point cloud into a few virtual views and uses a multi-view Transformer to predict keyframe poses; reported to train and run much faster than voxel-based PerAct on RLBench. Repo also hosts RVT-2.", "takeaway_zh": "RVT：把点云重新渲染为若干虚拟视角，再用多视角 Transformer 预测关键帧位姿；论文报告在 RLBench 上训练与推理都比基于体素的 PerAct 快得多。代码库同时包含 RVT-2。", "github_stars": 409, "page": "https://embodied-ai-radar.com/e/arxiv-2306.14896.html", "labs": [ "nvidia" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2409.01652", "title": "ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation", "type": "paper", "arxiv": "2409.01652", "year": 2024, "venue": "CoRL 2024", "org": "Stanford, Columbia", "url": null, "code": "https://github.com/huangwl18/ReKep", "project": null, "topics": [ "manipulation", "vla" ], "tags": [ "real", "zero-shot", "keypoints", "vlm", "optimization" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "ReKep — a VLM writes manipulation tasks as Python cost functions over 3D keypoints (relational keypoint constraints), solved by a hierarchical optimizer in real time; zero-shot, no task-specific training.", "takeaway_zh": "ReKep：由视觉语言模型把操作任务写成定义在三维关键点上的 Python 代价函数（关系关键点约束），再由分层优化器实时求解；零样本，无需针对任务训练。", "github_stars": 997, "page": "https://embodied-ai-radar.com/e/arxiv-2409.01652.html", "labs": [ "stanford", "columbia" ], "code_license": "" }, { "id": "arxiv-2103.14127", "title": "Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes", "type": "paper", "arxiv": "2103.14127", "year": 2021, "venue": "ICRA 2021", "org": "NVIDIA", "url": null, "code": "https://github.com/NVlabs/contact_graspnet", "project": null, "topics": [ "manipulation", "perception" ], "tags": [ "grasping", "point-cloud", "6-dof", "clutter" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Contact-GraspNet — predicts 6-DoF parallel-jaw grasps for a whole cluttered scene from one depth view by treating observed points as grasp contacts, which shrinks the problem to 4 DoF per point.", "takeaway_zh": "Contact-GraspNet：从单帧深度图为整个杂乱场景预测 6 自由度平行夹爪抓取；把观测到的点当作抓取接触点，使每个点只需学习 4 个自由度。", "github_stars": 547, "page": "https://embodied-ai-radar.com/e/arxiv-2103.14127.html", "labs": [ "nvidia" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2212.08333", "title": "AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains", "type": "paper", "arxiv": "2212.08333", "year": 2022, "venue": null, "org": "SJTU", "url": null, "code": "https://github.com/graspnet/anygrasp_sdk", "project": null, "topics": [ "manipulation", "perception" ], "tags": [ "grasping", "real", "dynamic", "tracking" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "AnyGrasp — dense 7-DoF parallel-gripper grasp detection trained on real sensor data with analytic labels and center-of-mass awareness, plus grasp tracking across frames for moving objects; released as an SDK.", "takeaway_zh": "AnyGrasp：基于真实传感器数据与解析标注训练、考虑物体质心的稠密 7 自由度平行夹爪抓取检测，并能跨帧跟踪抓取以应对运动物体；以 SDK 形式发布。", "github_stars": 1055, "page": "https://embodied-ai-radar.com/e/arxiv-2212.08333.html", "labs": [ "sjtu" ], "code_license": "" }, { "id": "arxiv-2610.29997", "title": "DONIC: Generalist Dexterous Bimanual Tracking Control from Human Demonstrations", "type": "paper", "arxiv": "2610.29997", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Ropedia/DONIC", "project": "https://ropedia.github.io/DONIC/", "topics": [ "manipulation", "learning", "datasets" ], "tags": [ "sim", "dexterous", "bimanual", "tracking", "ppo", "human-demos" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": "2026-10-23", "takeaway_en": "One two-hand policy (PPO + BC on its own verified successes) tracks retargeted human hand-object motions; ships BiHOI (1,575 sequences, 229 objects). Reports 86.8% on unseen trajectories, 57.3% on new objects.", "takeaway_zh": "用一个双手跟踪策略（PPO 结合对自身成功执行的行为克隆）复现重定向后的人手-物体交互动作，并发布 BiHOI 数据集（1575 段双手序列、229 个物体）。论文报告在未见轨迹上成功率 86.8%、新物体上 57.3%。", "github_stars": 56, "page": "https://embodied-ai-radar.com/e/arxiv-2610.29997.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.29560", "title": "From VLM Planners to Robotic Agents: Self-Evolving Harnesses for History-Dependent Manipulation", "type": "paper", "arxiv": "2610.29560", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/StevenKKXS/SERA", "project": "https://stevenkkxs.github.io/sera-webpage/", "topics": [ "manipulation", "vla" ], "tags": [ "agentic", "harness", "memory", "long-horizon", "vlm-planner", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-24", "takeaway_en": "SERA turns the high-level VLM into an agent with an editable harness that tracks task progress for a fixed VLA executor, and revises the harness from rollouts; reports 51.4% vs 44.4% overall success on RoboMME.", "takeaway_zh": "SERA 把高层 VLM 规划器做成带可编辑执行框架的智能体，为固定的底层 VLA 记录任务进度与执行反馈，并利用环境回放自动改进框架；报告在 RoboMME 上总体成功率 51.4%，未自我演化时为 44.4%。", "github_stars": 7, "page": "https://embodied-ai-radar.com/e/arxiv-2610.29560.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2410.24221", "title": "EgoMimic: Scaling Imitation Learning via Egocentric Video", "type": "paper", "arxiv": "2410.24221", "year": 2024, "venue": null, "org": "Georgia Tech", "url": null, "code": "https://github.com/SimarKareer/EgoMimic", "project": "https://egomimic.github.io/", "topics": [ "manipulation", "learning" ], "tags": [ "real", "bimanual", "human-video", "egocentric", "imitation" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Treats egocentric human video with 3D hand tracking (Project Aria glasses) as robot data and co-trains one policy on both; reports an extra hour of human hand data helps more than an extra hour of robot data.", "takeaway_zh": "把佩戴 Project Aria 眼镜采集、带三维手部跟踪的第一视角人类视频当作机器人数据，与机器人数据共同训练同一个策略；论文报告多 1 小时人手数据比多 1 小时机器人数据更有用。", "github_stars": 230, "page": "https://embodied-ai-radar.com/e/arxiv-2410.24221.html", "labs": [ "georgia-tech" ], "code_license": "MIT" }, { "id": "arxiv-2412.15109", "title": "Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation", "type": "paper", "arxiv": "2412.15109", "year": 2024, "venue": "ICLR 2025", "org": "Shanghai AI Lab / PKU / CUHK", "url": null, "code": "https://github.com/OpenRobotLab/Seer", "project": "https://nimolty.github.io/Seer/", "topics": [ "manipulation", "world-models" ], "tags": [ "real", "sim", "arm", "inverse-dynamics", "foresight", "pretraining" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Seer forecasts the next visual state and feeds it to an inverse dynamics model that outputs actions, trained end to end and pretrained on DROID; reports 4.28 avg. length on CALVIN ABC-D plus LIBERO and real-robot gains.", "takeaway_zh": "Seer 先预测未来视觉状态，再由逆动力学模型据此输出动作，端到端训练并在 DROID 上预训练；报告 CALVIN ABC-D 平均任务长度 4.28，在 LIBERO-LONG 和真机任务上也有提升。", "github_stars": 314, "page": "https://embodied-ai-radar.com/e/arxiv-2412.15109.html", "labs": [ "shanghai-ai-lab", "pku", "cuhk" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2502.20900", "title": "DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping", "type": "paper", "arxiv": "2502.20900", "year": 2025, "venue": "AAAI 2026", "org": "Peking University / PsiBot", "url": null, "code": "https://github.com/Psi-Robot/DexGraspVLA", "project": "https://dexgraspvla.github.io", "topics": [ "manipulation", "vla" ], "tags": [ "real", "dexterous-hand", "grasping", "hierarchical", "diffusion-policy" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A pretrained VLM plans and a diffusion controller grasps with a dexterous hand, working on domain-invariant features from foundation models; reports 90%+ grasp success across thousands of unseen cluttered scenes.", "takeaway_zh": "由预训练 VLM 做高层规划、扩散控制器驱动灵巧手抓取，底层输入为基础模型提取的域不变特征；报告在数千个未见过的杂乱场景中抓取成功率超过 90%。", "github_stars": 580, "page": "https://embodied-ai-radar.com/e/arxiv-2502.20900.html", "labs": [ "pku" ], "code_license": "" }, { "id": "arxiv-2610.31841", "title": "FORCA: A Force-Aware Coding Agent for Contact-Rich Robot Manipulation", "type": "paper", "arxiv": "2610.31841", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://tanoom.github.io/forca/", "topics": [ "manipulation", "tactile" ], "tags": [ "real", "arm", "force-torque", "contact-rich", "llm-agent", "code-as-policy" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "A coding agent that reads wrist force/torque as well as images, writes probing and force-feedback programs, and revises them from force logs; no demos or training. Beats its vision-only variant on 5 real tasks.", "takeaway_zh": "同时读取腕部力/力矩与图像的编程智能体：编写接触探测与力反馈控制程序，并在每次执行后依据力记录诊断失败、修改代码；无需示教或训练。在 5 个真实任务上优于仅视觉版本。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31841.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.24512", "title": "ContactBench 1.0: A Benchmark for Everyday Contact-Rich Robotic Manipulation", "type": "paper", "arxiv": "2610.24512", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://jingyi-xiang.github.io/contactbench", "topics": [ "manipulation", "tactile", "datasets" ], "tags": [ "benchmark", "contact-rich", "real", "sim", "3d-printable", "visuotactile" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "36 contact-rich tasks built around eight everyday skills, with 3D-printable objects that each have a matching sim model. Reports that tested visuo-tactile policies did not clearly beat vision-only ones.", "takeaway_zh": "围绕八种日常接触技能设计的 36 个接触密集型任务，40 多个物体可用消费级 3D 打印机复现，且每个都有对应仿真模型。报告称所测的视触觉策略并未明显优于纯视觉策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.24512.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.28948", "title": "COWBOY: A Scalable Sim-to-Real Framework for Learning Contextual Whole-Body Manipulation", "type": "paper", "arxiv": "2610.28948", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://cowboy-manip.com", "topics": [ "manipulation", "learning" ], "tags": [ "mobile-manipulation", "whole-body", "dexterous-hand", "sim2real", "distillation", "point-cloud", "active-vision" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Mobile dexterous grasping learned in sim: a whole-body controller does reaching and active vision, local RL experts the object, all distilled into one point-cloud policy; reports zero-shot real transfer.", "takeaway_zh": "在仿真中学习移动灵巧抓取：全身控制器负责底盘-手臂协同到达与主动视觉，局部强化学习专家负责物体交互，二者蒸馏成一个基于点云的视觉策略；报告可零样本迁移到真实世界的数百个物体与场景。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.28948.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.34453", "title": "Scaling Dexterous Manipulation via Action Representation Pretraining", "type": "paper", "arxiv": "2610.34453", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://arpg-project.github.io/", "topics": [ "manipulation", "learning" ], "tags": [ "dexterous", "bimanual", "human-data", "pretraining", "action-representation" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "ARPG pretrains an action encoder on human manipulation data, then trains a 130M generative policy in its space. Reports bimanual dexterous success up from 10.1% (π0.5) to 44.7% on DexJoCo.", "takeaway_zh": "ARPG 把动作表征学习与动作生成解耦：先在大规模人类操作数据上预训练动作编码器，再在其表征空间训练 1.3 亿参数的生成策略。报告在 DexJoCo 双手灵巧任务上平均成功率从 π0.5 的 10.1% 提升到 44.7%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.34453.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.33852", "title": "AdaDexTrack: Dynamic Modulation for Adaptive and Generalizable Dexterous Manipulation Tracking", "type": "paper", "arxiv": "2610.33852", "year": 2026, "venue": "CVPR 2026", "org": null, "url": null, "code": "https://github.com/Janebek/AdaDexTrack", "project": "https://janebek.github.io/AdaDexTrack/", "topics": [ "manipulation", "learning" ], "tags": [ "dexterous", "motion-tracking", "language-conditioned", "rl", "distillation", "sim2real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Language-to-dexterous-manipulation as modulated tracking: a distilled generalist tracker plus an RL-trained modulator that keeps adjusting the reference, object latent and position target; zero-shot sim-to-real.", "takeaway_zh": "把语言条件灵巧操作变成“可调制的跟踪”：蒸馏得到的通用跟踪器加 RL 训练的调制器，在执行中持续修正参考轨迹、物体隐表征与位置目标，抑制误差累积；零样本迁移到真机。", "github_stars": 7, "page": "https://embodied-ai-radar.com/e/arxiv-2610.33852.html", "labs": [], "code_license": "BSD-3-Clause" }, { "id": "arxiv-2610.34280", "title": "AdaPolicy: Action-Driven Anticipatory Robot Policies via Next-Chunk Prediction", "type": "paper", "arxiv": "2610.34280", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/teee000/AdaPolicy", "project": "https://teee111.github.io/AdaPolicy/", "topics": [ "manipulation", "learning" ], "tags": [ "imitation-learning", "action-chunking", "auxiliary-loss", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Foresight without predicting future frames: a training-only head forecasts the next action chunk and backpropagates through the policy's latent. No inference cost; reports up to +14 pts on mm-precision real tasks.", "takeaway_zh": "不预测未来图像也能获得“预见性”：仅在训练时用级联头预测下一个动作块，并把损失回传到在线策略的隐状态，推理零额外开销；报告在耳机装配、穿鞋带等毫米级真机任务上最高提升 14 个百分点。", "github_stars": 22, "page": "https://embodied-ai-radar.com/e/arxiv-2610.34280.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.25785", "title": "Long-Horizon Multi-Object Rearrangement via Distilled Tree Search over Continuous Actions", "type": "paper", "arxiv": "2610.25785", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/kallol-saha/DISCO", "project": "https://distilled-search.github.io", "topics": [ "manipulation", "learning" ], "tags": [ "rearrangement", "mcts", "long-horizon", "distillation", "sim", "real" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-10-27", "takeaway_en": "DISCO runs progressive-widening MCTS in GPU-parallel simulation, with a learned policy proposing continuous actions, then distills plans into a policy-value model used without search. Reports wins over BC, RL and search.", "takeaway_zh": "DISCO 在 GPU 并行物理仿真中用渐进展宽 MCTS 搜索长程重排计划，由学习的策略提出连续动作、价值函数打分，并周期性把搜索结果蒸馏回策略-价值模型；部署时无需仿真搜索。报告在仿真与真机重排任务上优于行为克隆、强化学习与搜索基线（CoRL 2026 Oral）。", "github_stars": 10, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25785.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.28880", "title": "Real2Sem2Real: 3D Semantic Augmentations for Robot Learning from a Single Human RGB Demonstration", "type": "paper", "arxiv": "2610.28880", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://real2sem2real.github.io/", "topics": [ "manipulation", "learning" ], "tags": [ "human-video", "data-generation", "gaussian-splatting", "digital-cousins", "vla", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Turns one monocular RGB human demo into thousands of robot demos across object instances: Gaussian-splat pose tracking, aligned 'digital cousins', replay in IsaacSim or MuJoCo. Reports VLAs matching teleop-trained ones.", "takeaway_zh": "把一段单目 RGB 人类演示扩展为同类别不同物体上的数千条机器人演示：用 DINOv3 蒸馏的高斯溅射跟踪物体位姿，对齐由照片生成的“数字表亲”，在 IsaacSim 或 MuJoCo 中生成数据（约 2 小时 2000+ 条）；报告所训练的 VLA 在真机上达到或超过遥操作数据训练的策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.28880.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.37652", "title": "Dexterous In-Hand Object Pose Reaching with Learned Safety Filters", "type": "paper", "arxiv": "2610.37652", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://ian-chuang.github.io/dexsafe/", "topics": [ "manipulation", "learning", "sim" ], "tags": [ "dexterous", "in-hand", "leap-hand", "sim2real", "safety", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Sim-only RL recipe for in-hand object pose reaching on a LEAP hand, zero-shot to hardware; reports 95.2% of 4,800 unseen goals reached and a tunable residual safety filter that cuts drops by 75%.", "takeaway_zh": "在仿真中无需人类数据训练 LEAP 灵巧手的手内物体位姿到达策略，零样本迁移到真机；报告在 4800 个未见目标上 95.2% 到达，可调节的残差安全滤波器使掉落减少 75%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.37652.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.36539", "title": "CARD: Contact-Anchored Retargeting for Dexterous Manipulation", "type": "paper", "arxiv": "2610.36539", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://card-retargeting.github.io/", "topics": [ "manipulation", "tactile" ], "tags": [ "dexterous", "retargeting", "contact", "tactile-glove", "human-video", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Retargets human hand demos (video, mocap, tactile gloves) by keeping contact-force targets, not just kinematics; a tactile tracking controller replays them. Reports contact kept on 43–78% of frames vs <10%.", "takeaway_zh": "把人手示教（视频、动捕、触觉手套）重定向到灵巧手时显式保留接触力目标而不只是运动学，并用触觉跟踪控制器回放；报告 43–78% 的接触帧保持接触，纯运动学方法不到 10%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36539.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.33863", "title": "RoboLongR: Causally Consistent Rewards Teach Robots to Progressively Master Long-Horizon Manipulation", "type": "paper", "arxiv": "2610.33863", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://chenzixuan99.github.io/RoboLongR-public/", "topics": [ "manipulation", "learning" ], "tags": [ "long-horizon", "reward-learning", "offline-to-online", "rlbench", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Learns history-aware progress rewards for long-horizon manipulation so missing prerequisites are not rewarded, then refines policies from BC to online RL. Reports 31.8% on 18 held-out RLBench tasks (+9.2 pts).", "takeaway_zh": "为长时程操作学习依赖执行历史的进度奖励，避免跳过前置步骤也得高分，再从行为克隆过渡到在线强化学习微调策略；报告在 18 个未参与预训练的 RLBench 任务上平均 31.8%（高出最强基线 9.2 个百分点），真机 57.5%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.33863.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.36685", "title": "RoboSTD: Zero-Shot Single-to-Dual Transfer via Sagittal Mirroring for Bimanual Learning", "type": "paper", "arxiv": "2610.36685", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://huahungy.github.io/RoboSTD/", "topics": [ "manipulation", "learning" ], "tags": [ "bimanual", "data-augmentation", "mirroring", "aloha", "llm", "real", "sim" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Builds pseudo-bimanual demos from single-arm data by sagittal-plane mirroring plus LLM-written coordination constraints. On AgileX ALOHA it reports 2.11× cups collected vs. a single-arm baseline.", "takeaway_zh": "将单臂示范沿矢状面镜像（观测、本体感觉与动作），再由 LLM 生成时空协调约束，构造伪双臂示范；在 AgileX ALOHA（RoboTwin 仿真与真机）上报告收集杯子数达到单臂基线的 2.11 倍。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36685.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.36250", "title": "cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation", "type": "paper", "arxiv": "2610.36250", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://cuarms.github.io/", "topics": [ "manipulation", "control" ], "tags": [ "multi-arm", "motion-planning", "gpu", "task-scheduling", "real", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "GPU-parallel conflict-based multi-arm planner: reusable per-robot roadmaps, lazy conflict/deadlock resolution and a scheduler proposing task skeletons. Reports real-time first solutions that keep improving.", "takeaway_zh": "为 GPU 并行而设计的多臂冲突搜索规划器：每台机器人使用可复用的多模态路线图，惰性解决冲突与死锁，并由高层调度器提出任务骨架；报告可实时给出初始解并持续改进运动质量与调度。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36250.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.29716", "title": "Reconstruction-Augmented Visuomotor Pre-Training of 3D-Grounded Bimanual Dexterity from RGB-Only Human Videos", "type": "paper", "arxiv": "2610.29716", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://spatial-dex.github.io/", "topics": [ "manipulation", "learning" ], "tags": [ "dexterous", "bimanual", "human-video", "3d-reconstruction", "pretraining", "real", "sim" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": "2026-10-29", "takeaway_en": "SpatialDex pre-trains a VLM-based dexterous policy on scene geometry and hand motion reconstructed from 1M RGB-only human clips. Reports +38.3 pts over prior methods on 6 real bimanual tasks.", "takeaway_zh": "SpatialDex 从 100 万段纯 RGB 人类视频中重建场景几何与手部运动，并以几何作为输入和预测目标预训练基于 VLM 的灵巧策略；报告在 9 个仿真任务上比现有方法高 16.5 分、6 个真实双臂灵巧任务上高 38.3 分。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.29716.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.39429", "title": "ScheduleGen: Demonstration Generation via Skill Scheduling for Multi-Arm Imitation Learning", "type": "paper", "arxiv": "2610.39429", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://schedulegen.github.io/", "topics": [ "manipulation", "datasets", "learning" ], "tags": [ "data-generation", "bimanual", "multi-arm", "task-and-motion-planning", "imitation-learning", "sim-to-real", "sim", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "MimicGen-style demo synthesis that respects multi-arm constraints: plans and schedules skills across arms. Reports 79.6% generation success on 28 sim variants and 50-80% real success via zero-shot sim-to-real.", "takeaway_zh": "面向双臂/多臂机器人的示范自动生成：在少量源示范基础上联合做规划与技能调度，显式处理多臂间约束，避免现有生成方法产生的碰撞与失败。28 个仿真任务变体上数据生成成功率 79.6%，训练出的策略经零样本 sim-to-real 在 8 个真实变体上成功率 50–80%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.39429.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.41028", "title": "Contact Imitation Interface: Transferring Dexterous Skills by Showing How Contact Unfolds", "type": "paper", "arxiv": "2610.41028", "year": 2026, "venue": null, "org": "National University of Singapore", "url": null, "code": "https://github.com/ariszxxu/CIMI", "project": "https://nus-lins-lab.github.io/CIMIWeb/", "topics": [ "manipulation" ], "tags": [ "dexterous", "human-demonstrations", "retargeting", "contact", "open-hardware", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Robot-free dexterous demos with shared contact geometry, fingertip RGB and contact-first retargeting. Reports at least 50 points higher final-stage success than baselines on LEAP and Wuji hands; hardware is open.", "takeaway_zh": "免机器人灵巧演示框架：共享接触几何、指尖 RGB 视角、接触对齐优先的重定向，加上预测接触面轨迹的层次策略。报告在 LEAP 和 Wuji 手的六组手-任务上，最终阶段成功率比基线至少高 50 个百分点；硬件与软件公开。", "github_stars": 6, "page": "https://embodied-ai-radar.com/e/arxiv-2610.41028.html", "labs": [ "nus" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.41625", "title": "RTPolicy: Real-Time (32.9 Hz) Multi-View Visuomotor Policy for Robotic Manipulation on Edge Devices", "type": "paper", "arxiv": "2610.41625", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/D-Robotics-AI-Lab/RTPolicy", "project": "https://d-robotics-ai-lab.github.io/RTPolicy.project", "topics": [ "manipulation" ], "tags": [ "edge", "real-time", "flow-matching", "robotwin", "bimanual", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Multi-view policy compressing camera views into a compact token for a flow-matching action head. Reports 52.26% on RoboTwin 2.0 (50 tasks; π0 46.4%) and 32.9 Hz inference on an RDK S600 edge device in INT16.", "takeaway_zh": "把多视角视觉融合压缩为紧凑 token，再条件化流匹配动作生成的轻量策略。报告 RoboTwin 2.0 五十任务平均 52.26%（π0 为 46.4%），真实双臂平台 72.0%，在 RDK S600 边缘设备上以 INT16 推理达 32.9 Hz。", "github_stars": 9, "page": "https://embodied-ai-radar.com/e/arxiv-2610.41625.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.40559", "title": "A Tutorial on Shape Servoing of Deformable Objects-Part I: Fundamentals", "type": "paper", "arxiv": "2610.40559", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/nachocz/tutorial_on_shape_servoing_of_deformable_objects", "project": "https://nachocz.github.io/tutorial_on_shape_servoing_of_deformable_objects/", "topics": [ "manipulation" ], "tags": [ "deformable", "tutorial", "control" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Tutorial on data-driven, Jacobian-based shape control of deformable objects: problem setup, shape representation, online Jacobian estimation, a local stability condition, and why some shapes are unreachable.", "takeaway_zh": "数据驱动、基于雅可比的柔性物体形状控制教程：问题建模、形状表示、形变雅可比在线估计、局部稳定条件，以及为何部分目标形状不可达。附代码。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2610.40559.html", "labs": [], "code_license": "" }, { "id": "arxiv-2602.16710", "title": "EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data", "type": "paper", "arxiv": "2602.16710", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "dexterous", "egocentric", "human-to-robot" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. Trains a VLA on over 20,854 hours of action-labeled egocentric human video to test whether large-scale human data can support fine, high-DoF dexterous manipulation.", "takeaway_zh": "CoRL 2026 Oral。用超过 20,854 小时带动作标注的第一视角人类视频训练 VLA，检验大规模人类数据能否支撑高自由度精细灵巧操作。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.16710.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.18117", "title": "OpenDexGrasp: Open-vocabulary Task-Oriented Dexterous Grasping", "type": "paper", "arxiv": "2609.18117", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://opendexgrasp.github.io/", "topics": [ "manipulation" ], "tags": [ "dexterous", "grasping", "task-oriented", "open-vocabulary" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. Generates executable dexterous grasps from free-form language, inferring the function a task implies from multi-view images and geometry, supported by the OpenDexVerse data.", "takeaway_zh": "CoRL 2026 Oral。根据自由语言生成可执行的灵巧抓取：结合多视角图像与几何推断任务所需的功能，由 OpenDexVerse 数据支撑。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.18117.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.26428", "title": "Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?", "type": "paper", "arxiv": "2606.26428", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/kushal2000/play2perfect", "project": "https://play2perfect.github.io", "topics": [ "manipulation" ], "tags": [ "dexterous", "assembly", "pretraining", "rl" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. Asks what dexterous play pretraining actually needs before precise assembly, a contact-rich, sparse-reward task that is hard for both imitation and direct RL.", "takeaway_zh": "CoRL 2026 Oral。探究灵巧“玩耍”预训练究竟需要什么，才能胜任精密装配这类富接触、稀疏奖励、模仿与直接 RL 都难以解决的任务。", "github_stars": 140, "page": "https://embodied-ai-radar.com/e/arxiv-2606.26428.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2604.06589", "title": "BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes", "type": "paper", "arxiv": "2604.06589", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://frenkielm.github.io/BiDexGrasp.github.io/", "topics": [ "manipulation" ], "tags": [ "bimanual", "dexterous-grasping", "dataset" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. A large-scale bimanual dexterous grasp dataset, built with a two-stage synthesis pipeline, together with a learning-based grasp generation framework.", "takeaway_zh": "CoRL 2026 Spotlight。大规模双手灵巧抓取数据集（由两阶段合成流程生成），并配套基于学习的抓取生成框架。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.06589.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.17016", "title": "Efficient and Reliable Teleoperation through Real-to-Sim-to-Real Shared Autonomy", "type": "paper", "arxiv": "2603.17016", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/shuosha/Residual_Copilot", "project": "https://residual-copilot.github.io/", "topics": [ "manipulation" ], "tags": [ "teleoperation", "shared-autonomy", "real-to-sim-to-real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Augments contact-rich teleoperation with corrective behaviors learned in simulation, using a simple kNN surrogate of the human operator.", "takeaway_zh": "CoRL 2026 Spotlight。为富接触遥操作叠加在仿真中学到的纠正行为，并用简单的 kNN 模型替代人类操作员进行训练。", "github_stars": 28, "page": "https://embodied-ai-radar.com/e/arxiv-2603.17016.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.16316", "title": "Constraint-Decomposed Impedance Control from Generative Policy for Articulated Object Manipulation", "type": "paper", "arxiv": "2610.16316", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/erickun0125/constraint-decomposed-impedance-control", "project": "https://erickun0125.github.io/constraint-decomposed-impedance/", "topics": [ "manipulation" ], "tags": [ "impedance-control", "articulated-objects", "generative-policy" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Addresses impedance gain selection for articulated tasks such as doors, bolts and drawers, where neural policies may violate kinematic constraints.", "takeaway_zh": "CoRL 2026 Spotlight。针对开门、拧螺栓、拉抽屉等铰接任务解决阻抗增益选择问题，这类任务中神经策略可能违反运动学约束。", "github_stars": 4, "page": "https://embodied-ai-radar.com/e/arxiv-2610.16316.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.12956", "title": "SERF: Spatiotemporal Environment and Robot Feature Map for Long-Horizon Mobile Manipulation", "type": "paper", "arxiv": "2606.12956", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/ExistentialRobotics/SERF-VLA", "project": "https://existentialrobotics.org/serf/", "topics": [ "manipulation" ], "tags": [ "mobile-manipulation", "neural-map", "long-horizon" ], "robot_types": [ "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Conditions a mobile manipulation policy on a spatiotemporal map of neural points that represents environment and robot body in one latent space, updated online.", "takeaway_zh": "CoRL 2026 Spotlight。用时空神经点地图作为移动操作策略的条件：环境与机器人本体处于同一隐空间，并在线更新。", "github_stars": 23, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12956.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.03647", "title": "Pixel-UMI: Learning Environment-Aware Whole-Body Manipulation from Sensorized Human Demonstrations", "type": "paper", "arxiv": "2611.03647", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://pixel-robot.com/pixel-umi", "topics": [ "manipulation", "hardware" ], "tags": [ "mobile-manipulation", "human-demonstrations", "retargeting", "whole-body", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Sensorized human demos to whole-body policies: scene-aware retargeting, masking of mismatched views in training, then DAgger corrections. Reports 87.0% task progress on five tasks.", "takeaway_zh": "把传感化的人类示范变成全身策略：结合重建的场景几何做重定向，训练时用重定向误差遮蔽不匹配的视角，再用 DAgger 人工纠正补足接触。作者报告五个任务平均进度 87.0%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03647.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.03294", "title": "ForceLDM: A Force-aware Latent Dynamics Model for Contact-Rich Manipulation", "type": "paper", "arxiv": "2611.03294", "year": 2026, "venue": "NeurIPS 2026", "org": null, "url": null, "code": "https://github.com/tpy001/ForceLDM-code", "project": "https://tpy001.github.io/ForceLDM/", "topics": [ "manipulation", "tactile", "world-models" ], "tags": [ "force", "contact-rich", "knowledge-distillation", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "A teacher sees future force/torque and optical flow; its features are distilled into a student using only current observations, so the policy anticipates contact. Tested on five real contact-rich tasks.", "takeaway_zh": "教师网络看到未来的力/力矩与光流，再把动力学特征蒸馏给只用当前观测的学生网络，使策略在部署时能预判接触。在五个真实接触丰富任务上测试。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03294.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2611.03584", "title": "TAMPAgent: Agentic Belief-Space Planning for Mobile Manipulation", "type": "paper", "arxiv": "2611.03584", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation", "navigation" ], "tags": [ "agent", "tamp", "belief-space", "mobile-manipulation", "real" ], "robot_types": [ "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "An LLM agent writes partial task plans over perception, planning and control tools; classical search completes them and geometric constraints are bound through the tools. Run in closed loop on Rainbow Y1 and Unitree G1.", "takeaway_zh": "大模型智能体基于感知、规划、控制工具生成部分任务规划，再由经典搜索补全，并通过工具满足几何约束；在 Rainbow Y1 与 Unitree G1 上闭环运行，交替观察与操作。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03584.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.03059", "title": "BeyondContact: A Reinforcement Learning Framework for Airflow-Based Non-Contact Manipulation", "type": "paper", "arxiv": "2611.03059", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/iit-DLSLab/BeyondContact", "project": null, "topics": [ "manipulation", "sim", "learning" ], "tags": [ "isaac-lab", "airflow", "non-contact", "sim2real", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Airflow extension for Isaac Lab with experimentally identified models, plus a light blower beside the gripper. Reports zero-shot real push-and-blow at 80% over 20 trials and 2.6° heading error with air alone.", "takeaway_zh": "为 Isaac Lab 增加基于实测模型的气流扩展，并在夹爪旁加装轻型鼓风机。报告零调试真机部署：推吹混合策略 20 次成功率 80%，纯气流转向平均误差 2.6°。", "github_stars": 4, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03059.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.36533", "title": "RoboAuto: Autonomous Robot Program Search through Multi-Level Physical Understanding", "type": "paper", "arxiv": "2610.36533", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://roboauto-ai.github.io/", "topics": [ "manipulation", "vla" ], "tags": [ "code-as-policy", "agentic", "planning" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Coding-agent search over robot programs that adds physical understanding at object, scene and interaction levels: a knowledge base, scene grounding, and self-designed experiments that test competing hypotheses.", "takeaway_zh": "让编码智能体搜索机器人程序时具备物体、场景和交互三层物理理解：知识库、场景落地，以及用自行设计的实验检验相互竞争的假设。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36533.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.00499", "title": "Knowing Where to Look: Interactive Object Search via Spatial-Semantic Fusion", "type": "paper", "arxiv": "2611.00499", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://skymanaditya1.github.io/objsearch-website/", "topics": [ "manipulation", "navigation" ], "tags": [ "object-search", "mobile-manipulation", "humanoid", "real" ], "robot_types": [ "arm", "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Fuses per-object VLM plausibility and geometric occupancy into one 3D voxel belief that decides where to look, go or open next; on a bimanual mobile humanoid it beats geometry-only search and LLM planning.", "takeaway_zh": "把逐物体的 VLM 合理性评分与几何占据证据融合为统一的三维体素信念，决定下一步看哪、去哪、开哪个柜子；在双臂移动人形上优于纯几何搜索和 LLM 规划。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.00499.html", "labs": [], "code_license": null }, { "id": "arxiv-2410.10803", "title": "Generalizable Humanoid Manipulation with 3D Diffusion Policies", "type": "paper", "arxiv": "2410.10803", "year": 2024, "venue": null, "org": "Stanford", "url": null, "code": "https://github.com/YanjieZe/Improved-3D-Diffusion-Policy", "project": "https://humanoid-manipulation.github.io/", "topics": [ "manipulation", "locomotion" ], "tags": [ "humanoid", "diffusion-policy", "teleoperation", "3d", "real" ], "robot_types": [ "humanoid" ], "highlight": true, "rating": 2, "added": "2026-11-04", "takeaway_en": "iDP3: whole-upper-body teleop, a 25-DoF humanoid with 3D LiDAR, and an improved 3D Diffusion Policy for noisy human data, aiming at skills beyond a single scene.", "takeaway_zh": "iDP3：上半身全身遥操作、带三维激光雷达的 25 自由度人形，加上改进的三维扩散策略，使技能不再局限于单一场景。", "github_stars": 559, "page": "https://embodied-ai-radar.com/e/arxiv-2410.10803.html", "labs": [ "stanford" ], "code_license": "MIT" }, { "id": "arxiv-2506.04147", "title": "SLAC: Safe and Efficient Real-Robot Reinforcement Learning via Unsupervised Simulation Pre-Training", "type": "paper", "arxiv": "2506.04147", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/JiahengHu/SLAC", "project": "https://robo-rl.github.io/", "topics": [ "manipulation", "learning", "sim" ], "tags": [ "real-world-rl", "mobile-manipulation", "latent-action", "sim-to-real" ], "robot_types": [ "arm", "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Pretrains a task-agnostic latent action space in a low-fidelity simulator so real-world RL on high-DoF mobile manipulators becomes safe and efficient.", "takeaway_zh": "在低保真仿真器中预训练与任务无关的潜在动作空间，让高自由度移动操作机器人的真实世界强化学习安全高效。", "github_stars": 20, "page": "https://embodied-ai-radar.com/e/arxiv-2506.04147.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.03815", "title": "AIRoA-0: Long-Horizon, Precise, Cross-Embodiment Manipulation from Handheld Data", "type": "paper", "arxiv": "2611.03815", "year": 2026, "venue": null, "org": "AIRoA", "url": null, "code": "https://github.com/airoa-org/AIRoA-0", "project": "https://a-0.airoa.io", "topics": [ "manipulation", "vla", "datasets" ], "tags": [ "real", "bimanual", "handheld", "open-weights" ], "robot_types": [ "arm" ], "highlight": true, "rating": 2, "added": "2026-11-04", "takeaway_en": "Learned from 16,151 hours of handheld gripper demos with no robot data; reports 78% average success over ten tasks versus 27% for the best other VLA, and releases the dataset, weights and post-training recipe.", "takeaway_zh": "从 16,151 小时手持夹爪演示中学习，全程不采机器人数据；报告十项任务平均成功率 78%，其他 VLA 最好为 27%，并开放数据集、权重和后训练方案。", "github_stars": 21, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03815.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2611.05163", "title": "Synthesizing Dexterous Robot Demonstrations from Human Videos via Virtual Try-On", "type": "paper", "arxiv": "2611.05163", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/ryan-seungyong-lee/RoboTryOn", "project": "https://ryan-seungyong-lee.github.io/RoboTryOn", "topics": [ "manipulation" ], "tags": [ "real", "dexterous", "humanoid", "human-video" ], "robot_types": [ "hand", "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Treats human-to-robot transfer as virtual try-on: keeps the motion and swaps the person for a target robot in video; reports novel-task success rising from 2% to 60% with no real demos of those tasks.", "takeaway_zh": "把人到机器人的迁移看作虚拟试穿：保留动作，把视频中的人换成目标机器人；报告在没有这些任务真机演示时，新任务成功率从 2% 升到 60%。", "github_stars": 9, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05163.html", "labs": [], "code_license": "" }, { "id": "arxiv-2611.04080", "title": "FEIJOA: Task-Driven Grounding and Execution for Mobile Manipulation", "type": "paper", "arxiv": "2611.04080", "year": 2026, "venue": null, "org": "MIT", "url": null, "code": null, "project": "https://williamshen-nz.github.io/feijoa/", "topics": [ "manipulation" ], "tags": [ "real", "mobile-manipulation", "agent" ], "robot_types": [ "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A language-model agent resolves instructions against a posed RGB-D scan at test time instead of a prebuilt representation; 35 of 50 real Spot trials succeed, and most failures come from physical skills.", "takeaway_zh": "语言模型智能体在测试时对带位姿的 RGB-D 扫描做指令落地，不依赖预建表示；Spot 真机 50 次试验成功 35 次，失败大多来自物理技能而非推理。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.04080.html", "labs": [ "mit" ], "code_license": null }, { "id": "arxiv-2611.05119", "title": "DexHOT: Learning Hand-Object Configuration Transfer for Dexterous Manipulation", "type": "paper", "arxiv": "2611.05119", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://dex-hot.github.io/", "topics": [ "manipulation" ], "tags": [ "real", "sim", "dexterous" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Sets dexterous goals as joint hand-object configurations and learns one transfer primitive from synthetic pairs, with no human motion; reports 69.4% on unseen objects in simulation and 61.5% over 200 real transfers.", "takeaway_zh": "把灵巧操作目标定义为手与物体的联合构型，从合成构型对学习统一的转移基元，无需人类动作；报告仿真未见物体成功率 69.4%，200 次真机转移成功率 61.5%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05119.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.03779", "title": "Robot-Matched Soft Fingers for UMI Data Collection: Lessons from 100 Hours of Pick and Place", "type": "paper", "arxiv": "2611.03779", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/lute-corp/soft-yubi", "project": "https://lute-corp.github.io/soft-yubi-site/", "topics": [ "manipulation" ], "tags": [ "real", "umi", "soft-gripper", "data-collection", "dataset" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A battery-powered handheld UMI whose soft fingers match the robot and show force by deforming, plus a 99.7-hour dataset; reports a top camera adds 13 points of success with only about half the data including it.", "takeaway_zh": "电池供电的手持 UMI，软指与机器人夹爪匹配，并以形变反映受力，附 99.7 小时数据集；报告即使仅约一半数据含顶部相机，成功率也提高 13 个百分点。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03779.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2611.03825", "title": "A Tutorial on Shape Servoing of Deformable Objects-Part II: Advanced Concepts", "type": "paper", "arxiv": "2611.03825", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/nachocz/tutorial_on_shape_servoing_of_deformable_objects", "project": "https://nachocz.github.io/tutorial_on_shape_servoing_of_deformable_objects/", "topics": [ "manipulation" ], "tags": [ "tutorial", "deformable", "control" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Second part of a tutorial on data-driven, Jacobian-based shape control: which shape changes are reachable, how feature choice shapes behaviour, and the three ways the control loop fails.", "takeaway_zh": "基于数据与雅可比的形状控制教程第二部分：哪些形变可达、特征选择如何影响行为，以及控制回路失效的三种方式。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03825.html", "labs": [], "code_license": "" }, { "id": "arxiv-2606.14188", "title": "Robustness without Wrinkles: Parallel Simulation and Robust MPC for Certified Deformable Manipulation", "type": "paper", "arxiv": "2606.14188", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation", "control", "sim" ], "tags": [ "deformable", "robust-mpc", "conformal-prediction" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "CORD-SLS pairs a GPU-parallel differentiable simulator with robust MPC and conformal bounds for rope and cloth, reporting millisecond planning with safety margins on hardware.", "takeaway_zh": "CORD-SLS 将 GPU 并行可微仿真与鲁棒 MPC、共形预测误差界结合，用于绳索和布料操作，报告在实机上以毫秒级规划并保持安全裕度。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.14188.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.24552", "title": "Enabling Robust Cloth Manipulation via Inference-Time Simulator-in-the-Loop Refinement", "type": "paper", "arxiv": "2606.24552", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://silr-cloth.github.io/", "topics": [ "manipulation", "sim" ], "tags": [ "deformable", "mppi", "real-to-sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Refines cloth actions online from one RGB image: a real-to-sim module estimates the cloth state, then simulator rollouts with prior-guided MPPI correct the policy's trajectory.", "takeaway_zh": "仅凭单张 RGB 在线修正布料动作：先由 real-to-sim 模块估计布料状态，再用仿真器并行推演和先验引导的 MPPI 修正策略轨迹。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.24552.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.24674", "title": "Noise-Invariant Spatial Encoding for 3D Manipulation Policies under Depth Corruption", "type": "paper", "arxiv": "2610.24674", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation", "perception" ], "tags": [ "3d-policy", "depth-noise", "robustness" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "NISE targets the sharp failure of 3D policies under realistic depth noise, carrying position uncertainty into attention; tested on transparent and reflective objects with commodity RGB-D.", "takeaway_zh": "NISE 针对 3D 策略在真实深度噪声下的急剧失效，把位置不确定性带入注意力计算；在透明和反光物体上用普通 RGB-D 相机测试。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.24674.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.09314", "title": "KPGrasp: Scalable Keypoint Flow Matching for Dexterous Grasp Generation", "type": "paper", "arxiv": "2606.09314", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Yuansen-Huang/KPGrasp", "project": "https://yuansen-huang.github.io/KPGrasp-page/", "topics": [ "manipulation" ], "tags": [ "dexterous", "grasping", "flow-matching" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Generates dexterous grasps as 3D hand keypoints with a plain Transformer flow model, with no contact losses or refinement; reports 76.3% success on Dexonomy and 0.032 s per grasp.", "takeaway_zh": "以 3D 手部关键点表示灵巧抓取，用普通 Transformer 流模型生成，无需接触损失或后处理优化；报告 Dexonomy 成功率 76.3%，每次抓取 0.032 秒。", "github_stars": 6, "page": "https://embodied-ai-radar.com/e/arxiv-2606.09314.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2602.08557", "title": "Combining Constrained Sampling and Reinforcement Learning for Manipulation", "type": "paper", "arxiv": "2602.08557", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/LIS-TU-Berlin/csrl", "project": "https://www.user.tu-berlin.de/mtoussai/26-CSRL/", "topics": [ "manipulation", "learning" ], "tags": [ "rl", "contact-rich", "reset-strategy" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Helps RL explore contact-rich tasks by resetting episodes with a contact-aware constrained state sampler, covering diverse contact modes; trains universal in-hand and whole-body policies.", "takeaway_zh": "用感知接触的约束状态采样器重置回合，覆盖多样接触模式，帮助 RL 探索接触丰富任务；可训练通用的手内和全身操作策略。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2602.08557.html", "labs": [], "code_license": "" }, { "id": "arxiv-2503.13441", "title": "Humanoid Policy ~ Human Policy", "type": "paper", "arxiv": "2503.13441", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/RogerQi/human-policy", "project": "https://human-as-robot.github.io/", "topics": [ "manipulation", "vla" ], "tags": [ "humanoid", "human-data", "cross-embodiment" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Uses egocentric human demonstrations as cross-embodiment data for humanoid manipulation policies, narrowing the human-robot gap in data and model design.", "takeaway_zh": "把第一视角人类示教作为人形操作策略的跨形态数据，从数据和模型两方面缩小人与机器人的差距。", "github_stars": 258, "page": "https://embodied-ai-radar.com/e/arxiv-2503.13441.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2506.15666", "title": "Vision in Action: Learning Active Perception from Human Demonstrations", "type": "paper", "arxiv": "2506.15666", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation", "hardware" ], "tags": [ "active-perception", "bimanual", "teleoperation" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Active perception learned from human demonstrations: a 6-DoF robotic neck and a VR interface let bimanual robots search, track and focus.", "takeaway_zh": "从人类示教学习主动感知：6 自由度机械颈加 VR 界面，让双臂机器人能搜索、跟踪和聚焦目标。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2506.15666.html", "labs": [], "code_license": null }, { "id": "arxiv-2507.23523", "title": "H-RDT: Human Manipulation Enhanced Bimanual Robotic Manipulation", "type": "paper", "arxiv": "2507.23523", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation", "vla" ], "tags": [ "human-data", "bimanual", "diffusion" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "H-RDT pretrains a diffusion transformer on human manipulation data to work around the morphology mismatch of cross-robot datasets.", "takeaway_zh": "H-RDT 用人类操作数据预训练扩散 Transformer，规避跨机器人数据集的形态差异问题。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2507.23523.html", "labs": [], "code_license": null }, { "id": "arxiv-2511.07418", "title": "Lightning Grasp: High Performance Procedural Grasp Synthesis with Contact Fields", "type": "paper", "arxiv": "2511.07418", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/zhaohengyin/lightning-grasp", "project": null, "topics": [ "manipulation" ], "tags": [ "dexterous", "grasp-synthesis" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Procedural dexterous grasp synthesis reporting orders-of-magnitude speedups, without carefully tuned energy functions, including for irregular tool-like objects.", "takeaway_zh": "过程式灵巧抓取合成，报告提速数个量级，无需精调能量函数，并适用于不规则的工具类物体。", "github_stars": 229, "page": "https://embodied-ai-radar.com/e/arxiv-2511.07418.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2611.04477", "title": "Spectrally Routed Diffusion Policy for Multi-Task Robotic Manipulation", "type": "paper", "arxiv": "2611.04477", "year": 2026, "venue": "RA-L", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "diffusion-policy", "multi-task", "bimanual" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "SR-DP splits a trained diffusion denoiser by SVD into rank-one experts with task routing, so joint training stops hurting single tasks; reports beating per-task baselines on six RoboTwin tasks.", "takeaway_zh": "SR-DP 用 SVD 把训练好的扩散去噪器拆成秩一专家并按任务路由，缓解多任务联合训练的相互干扰；报告在六个 RoboTwin 任务上超过逐任务基线。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.04477.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.05754", "title": "GRASP: Gradient-Guided Sampling for Contact-Rich Physics-Based Motion Retargeting", "type": "paper", "arxiv": "2611.05754", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/ComputationalRobotics/GRASP_code", "project": "https://computationalrobotics.seas.harvard.edu/GRASP/", "topics": [ "manipulation", "locomotion" ], "tags": [ "retargeting", "dexterous", "humanoid", "sampling", "mpc", "real" ], "robot_types": [ "arm", "hand", "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Retargets human demos under contact without a differentiable simulator: gradients come from archived sampling rollouts. Reports 92.8% success on 1,352 bimanual OakInk2 demos with no task-specific contact rewards.", "takeaway_zh": "无需可微仿真器即可在接触动力学下重定向人类示教：从存档的采样轨迹中恢复梯度。报告在 1352 条双手 OakInk2 示教上成功率 92.8%，且不用任务专属接触奖励。", "github_stars": 22, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05754.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2306.17817", "title": "Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation", "type": "paper", "arxiv": "2306.17817", "year": 2023, "venue": null, "org": null, "url": null, "code": "https://github.com/zhouxian/act3d-chained-diffuser", "project": "https://act3d.github.io/", "topics": [ "manipulation" ], "tags": [ "3d", "transformer", "multi-task", "rlbench" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Act3D samples 3D feature fields at adaptive resolutions to get high spatial precision without a costly high-resolution voxel grid.", "takeaway_zh": "Act3D 用自适应分辨率采样三维特征场，无需昂贵的高分辨率体素也能获得高空间精度。", "github_stars": 186, "page": "https://embodied-ai-radar.com/e/arxiv-2306.17817.html", "labs": [], "code_license": "" }, { "id": "arxiv-2402.10885", "title": "3D Diffuser Actor: Policy Diffusion with 3D Scene Representations", "type": "paper", "arxiv": "2402.10885", "year": 2024, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "diffusion-policy", "3d", "multi-task" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "3D Diffuser Actor conditions diffusion policies on 3D scene features aggregated from one or more camera views.", "takeaway_zh": "3D Diffuser Actor 用单视角或多视角聚合的三维场景特征来条件化扩散策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2402.10885.html", "labs": [], "code_license": null }, { "id": "arxiv-2403.03890", "title": "Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic Manipulation", "type": "paper", "arxiv": "2403.03890", "year": 2024, "venue": "CVPR 2024", "org": null, "url": null, "code": "https://github.com/dyson-ai/hdp", "project": "https://yusufma03.github.io/projects/hdp/", "topics": [ "manipulation" ], "tags": [ "diffusion-policy", "hierarchical", "kinematics" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "HDP splits a policy into a high-level next-best-pose planner and a low-level goal-conditioned diffusion policy that generates motion to reach it.", "takeaway_zh": "HDP 把策略分为高层“下一最佳位姿”规划与低层目标条件扩散策略，由后者生成到达该位姿的运动。", "github_stars": 240, "page": "https://embodied-ai-radar.com/e/arxiv-2403.03890.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2405.07503", "title": "Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation", "type": "paper", "arxiv": "2405.07503", "year": 2024, "venue": null, "org": null, "url": null, "code": "https://github.com/Aaditya-Prasad/consistency-policy", "project": "https://consistency-policy.github.io/", "topics": [ "manipulation" ], "tags": [ "diffusion-policy", "consistency", "fast-inference" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Distils a diffusion teacher into a consistency model so visuomotor policies run fast on robots without high-end GPUs.", "takeaway_zh": "把扩散教师模型蒸馏为一致性模型，让视觉运动策略在没有高端 GPU 的机器人上快速运行。", "github_stars": 207, "page": "https://embodied-ai-radar.com/e/arxiv-2405.07503.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2406.01586", "title": "ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation", "type": "paper", "arxiv": "2406.01586", "year": 2024, "venue": null, "org": null, "url": null, "code": "https://github.com/ManiCM-fast/ManiCM", "project": "https://manicm-fast.github.io/", "topics": [ "manipulation" ], "tags": [ "diffusion-policy", "consistency", "3d", "real-time" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "ManiCM applies consistency models to 3D diffusion policies, removing most denoising steps for real-time inference.", "takeaway_zh": "ManiCM 把一致性模型用于三维扩散策略，去掉大部分去噪步骤以实现实时推理。", "github_stars": 126, "page": "https://embodied-ai-radar.com/e/arxiv-2406.01586.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2406.04806", "title": "Streaming Diffusion Policy: Fast Policy Synthesis with Variable Noise Diffusion Models", "type": "paper", "arxiv": "2406.04806", "year": 2024, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "diffusion-policy", "fast-inference" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Streaming Diffusion Policy speeds up action synthesis using variable-noise diffusion, aimed at tasks that need fast reactive policies.", "takeaway_zh": "Streaming Diffusion Policy 用可变噪声扩散加速动作生成，面向需要快速反应的任务。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2406.04806.html", "labs": [], "code_license": null }, { "id": "arxiv-2407.01531", "title": "Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning", "type": "paper", "arxiv": "2407.01531", "year": 2024, "venue": "CoRL 2024", "org": null, "url": null, "code": "https://github.com/AnthonyHuo/SDP", "project": "https://forrest-110.github.io/sparse_diffusion_policy/", "topics": [ "manipulation" ], "tags": [ "diffusion-policy", "multi-task", "continual-learning" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Sparse Diffusion Policy reuses sparse expert modules across tasks instead of one universal policy, for multitask and continual learning.", "takeaway_zh": "Sparse Diffusion Policy 在任务间复用稀疏专家模块而非单一通用策略，用于多任务和持续学习。", "github_stars": 88, "page": "https://embodied-ai-radar.com/e/arxiv-2407.01531.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2407.05996", "title": "Multimodal Diffusion Transformer: Learning Versatile Behavior from Multimodal Goals", "type": "paper", "arxiv": "2407.05996", "year": 2024, "venue": "RSS 2024", "org": null, "url": null, "code": "https://github.com/intuitive-robots/mdt_policy", "project": "https://intuitive-robots.github.io/mdt_policy/", "topics": [ "manipulation" ], "tags": [ "diffusion-policy", "transformer", "multimodal-goals" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "MDT is a diffusion transformer that learns versatile behaviour from multimodal goals with few language annotations, using two self-supervised objectives.", "takeaway_zh": "MDT 是扩散 Transformer，借助两个自监督目标，在语言标注很少时从多模态目标学习多样行为。", "github_stars": 171, "page": "https://embodied-ai-radar.com/e/arxiv-2407.05996.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2307.01849", "title": "Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised Learning", "type": "paper", "arxiv": "2307.01849", "year": 2023, "venue": null, "org": null, "url": null, "code": "https://github.com/LostXine/crossway_diffusion", "project": null, "topics": [ "manipulation" ], "tags": [ "diffusion-policy", "self-supervised" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Adds a self-supervised reconstruction objective to diffusion policy training to improve imitation learning.", "takeaway_zh": "为扩散策略训练加入自监督重建目标，以改进模仿学习。", "github_stars": 72, "page": "https://embodied-ai-radar.com/e/arxiv-2307.01849.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2501.18564", "title": "SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation", "type": "paper", "arxiv": "2501.18564", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/sam2act/sam2act", "project": "https://sam2act.github.io", "topics": [ "manipulation" ], "tags": [ "memory", "sam", "3d", "transformer" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "SAM2Act builds on a visual foundation model and adds a memory architecture for multitask manipulation and tasks that need spatial memory.", "takeaway_zh": "SAM2Act 基于视觉基础模型并加入记忆架构，用于多任务操作和需要空间记忆的任务。", "github_stars": 251, "page": "https://embodied-ai-radar.com/e/arxiv-2501.18564.html", "labs": [], "code_license": "" }, { "id": "arxiv-2407.11298", "title": "ThinkGrasp: A Vision-Language System for Strategic Part Grasping in Clutter", "type": "paper", "arxiv": "2407.11298", "year": 2024, "venue": "CoRL 2024", "org": null, "url": null, "code": "https://github.com/H-Freax/ThinkGrasp", "project": "https://h-freax.github.io/thinkgrasp_page/", "topics": [ "manipulation" ], "tags": [ "grasping", "clutter", "llm", "vlm" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "ThinkGrasp uses GPT-4o reasoning to pick which object and part to grasp in heavy clutter, a plug-and-play vision-language system.", "takeaway_zh": "ThinkGrasp 借助 GPT-4o 推理，在严重杂乱中决定抓哪个物体的哪个部位，是即插即用的视觉-语言系统。", "github_stars": 121, "page": "https://embodied-ai-radar.com/e/arxiv-2407.11298.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2307.14535", "title": "Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition", "type": "paper", "arxiv": "2307.14535", "year": 2023, "venue": null, "org": null, "url": null, "code": "https://github.com/real-stanford/scalingup", "project": "https://www.cs.columbia.edu/~huy/scalingup/", "topics": [ "manipulation" ], "tags": [ "llm", "data-generation", "diffusion-policy" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Uses an LLM plus sampling-based planners to generate language-labelled robot data, then distils it into one language-conditioned visuomotor policy.", "takeaway_zh": "用大模型加采样规划器生成带语言标注的机器人数据，再蒸馏为一个语言条件视觉运动策略。", "github_stars": 412, "page": "https://embodied-ai-radar.com/e/arxiv-2307.14535.html", "labs": [], "code_license": "" }, { "id": "arxiv-2310.10639", "title": "Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models", "type": "paper", "arxiv": "2310.10639", "year": 2023, "venue": null, "org": null, "url": null, "code": "https://github.com/kvablack/susie", "project": "https://rail-berkeley.github.io/susie", "topics": [ "manipulation" ], "tags": [ "diffusion", "subgoals", "zero-shot" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "SuSIE uses a pretrained image-editing diffusion model as a high-level planner that proposes intermediate subgoal images.", "takeaway_zh": "SuSIE 用预训练图像编辑扩散模型作高层规划器，提出中间子目标图像。", "github_stars": 164, "page": "https://embodied-ai-radar.com/e/arxiv-2310.10639.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2406.18915", "title": "Manipulate-Anything: Automating Real-World Robots using Vision-Language Models", "type": "paper", "arxiv": "2406.18915", "year": 2024, "venue": null, "org": null, "url": null, "code": "https://github.com/Robot-MA/manipulate-anything", "project": "https://robot-ma.github.io/", "topics": [ "manipulation" ], "tags": [ "vlm", "data-generation", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Manipulate-Anything uses vision-language models to generate real-world robot demonstrations automatically.", "takeaway_zh": "Manipulate-Anything 用视觉-语言模型自动生成真实世界机器人示教数据。", "github_stars": 59, "page": "https://embodied-ai-radar.com/e/arxiv-2406.18915.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2410.00371", "title": "AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation", "type": "paper", "arxiv": "2410.00371", "year": 2024, "venue": null, "org": null, "url": null, "code": "https://github.com/NVlabs/AHA", "project": "https://aha-vlm.github.io/", "topics": [ "manipulation" ], "tags": [ "vlm", "failure-detection" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "AHA is a vision-language model for detecting and reasoning about manipulation failures, a gap in earlier VLM work.", "takeaway_zh": "AHA 是用于检测和推理操作失败的视觉-语言模型，填补此前 VLM 研究的空白。", "github_stars": 79, "page": "https://embodied-ai-radar.com/e/arxiv-2410.00371.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2311.17842", "title": "Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning", "type": "paper", "arxiv": "2311.17842", "year": 2023, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "vlm", "planning", "gpt-4v" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Early study of GPT-4V for physically grounded vision-language task planning on robots.", "takeaway_zh": "较早研究 GPT-4V 用于机器人基于物理场景的视觉-语言任务规划。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2311.17842.html", "labs": [], "code_license": null }, { "id": "arxiv-2309.02561", "title": "Physically Grounded Vision-Language Models for Robotic Manipulation", "type": "paper", "arxiv": "2309.02561", "year": 2023, "venue": "ICRA 2024", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "vlm", "physical-properties" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Teaches vision-language models about physical object properties so they can reason better for manipulation.", "takeaway_zh": "让视觉-语言模型理解物体的物理属性，从而更好地为操作任务推理。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2309.02561.html", "labs": [], "code_license": null }, { "id": "arxiv-2408.11537", "title": "A Survey of Embodied Learning for Object-Centric Robotic Manipulation", "type": "paper", "arxiv": "2408.11537", "year": 2024, "venue": null, "org": null, "url": null, "code": "https://github.com/RayYoh/OCRM_survey", "project": null, "topics": [ "manipulation" ], "tags": [ "survey", "object-centric" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Survey of embodied learning for object-centric robotic manipulation.", "takeaway_zh": "综述面向物体的机器人操作中的具身学习方法。", "github_stars": 258, "page": "https://embodied-ai-radar.com/e/arxiv-2408.11537.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.05667", "title": "Correct Robots Before They Make Mistakes: Proactive Human-in-the-Loop Intervention via Preference Learning", "type": "paper", "arxiv": "2611.05667", "year": 2026, "venue": null, "org": "KIT", "url": null, "code": null, "project": "https://intuitive-robots.github.io/PHIL-page/", "topics": [ "manipulation" ], "tags": [ "real", "human-in-the-loop", "xr", "diffusion-policy" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "PHIL shows an operator the policy's predicted motion in XR so they can step in before an error, then trains on rejected vs corrected chunks; reports 70.3% vs 44.0% for HG-DAgger with a diffusion policy.", "takeaway_zh": "PHIL 在 XR 中预览策略预测的动作，让操作者在出错前介入，再用被否决与修正后的动作片段做偏好训练；报告相比 HG-DAgger，扩散策略成功率从 44.0% 升至 70.3%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05667.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.38357", "title": "CSO: Refining Robotic Policies via Skill Distribution Alignment and Skill-Grained Optimization", "type": "paper", "arxiv": "2610.38357", "year": 2026, "venue": "NeurIPS 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "real", "sim", "skills", "post-training" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Two-stage post-training for VQ-style skill policies: align the skill prior with successful online rollouts, then optimise per skill with the encoder frozen; reports LIBERO success rising from 80.1% to 97.5%.", "takeaway_zh": "面向 VQ 类技能策略的两阶段后训练：先让技能先验对齐线上成功轨迹，再冻结编码器按技能优化；报告 LIBERO 成功率从 80.1% 升至 97.5%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.38357.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.05979", "title": "Learning Object-Centric Motion Priors from Human for Robotic Dexterous Manipulation", "type": "paper", "arxiv": "2611.05979", "year": 2026, "venue": "AAAI 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "real", "sim", "dexterous", "human-data", "rl" ], "robot_types": [ "hand" ], "highlight": false, "rating": 1, "added": "2026-11-05", "takeaway_en": "Learns to predict future hand-object states from human interaction datasets and uses the predicted object states as a general RL reward, reducing reward engineering; deployed on different hands (AAAI oral).", "takeaway_zh": "从人手交互数据学习预测未来手物状态，并把预测的物体状态作为通用强化学习奖励，减少逐任务奖励设计；已部署到不同灵巧手（AAAI 口头报告）。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05979.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.06118", "title": "Online Adaptation to Latent Physical Properties for Learning from Demonstration", "type": "paper", "arxiv": "2611.06118", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://isri-aist.github.io/latent-dynamics-project-page/", "topics": [ "manipulation", "learning" ], "tags": [ "imitation-learning", "online-adaptation", "real", "center-of-mass" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Updates a latent estimate of hidden object properties during execution to condition an imitation policy; helps with center-of-mass changes, though it reports only about 30% success on unseen locations in sim.", "takeaway_zh": "执行中在线更新物体隐藏物性的潜变量，并以此调节模仿策略；对质心变化有帮助，但报告仿真中未见过的质心位置成功率仅约 30%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.06118.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.03973", "title": "VLS: Steering Pretrained Robot Policies via Vision-Language Models", "type": "paper", "arxiv": "2602.03973", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Vision-Language-Steering/code", "project": "https://vision-language-steering.github.io/webpage/", "topics": [ "manipulation", "vla" ], "tags": [ "steering", "inference-time", "diffusion-policy", "vlm", "reward", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Adapts a frozen diffusion or flow policy at test time: a VLM writes differentiable rewards that steer denoising when obstacles or clutter shift the scene. Reports +31% on CALVIN and +13% on LIBERO-PRO; no retraining.", "takeaway_zh": "在测试时适配冻结的扩散或流匹配策略：当障碍或杂物改变场景时，由 VLM 生成可微奖励来引导去噪。报告 CALVIN 提升 31%、LIBERO-PRO 提升 13%，无需重新训练。", "github_stars": 82, "page": "https://embodied-ai-radar.com/e/arxiv-2602.03973.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2609.09119", "title": "DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination", "type": "paper", "arxiv": "2609.09119", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/AureleoPKU/DeCAL", "project": "https://aureleopku.github.io/DeCAL", "topics": [ "manipulation", "vla", "tactile" ], "tags": [ "dexterous", "visuotactile", "mixture-of-transformers", "latent-imagination", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Dexterous visuotactile VLA on a mixture-of-transformers: contact-aware gating decides when touch matters; the model jointly imagines future visual and tactile latents. Reports 71% average success on contact-rich tasks.", "takeaway_zh": "基于混合 Transformer 的灵巧视触觉 VLA：接触感知门控决定何时引入触觉，并联合想象未来的视觉与触觉潜变量。报告在富接触任务上平均成功率 71%。", "github_stars": 29, "page": "https://embodied-ai-radar.com/e/arxiv-2609.09119.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.03476", "title": "MobiAgent: Dual-Loop Recursive Policy Self-Improvement for Long-Horizon Mobile Manipulation", "type": "paper", "arxiv": "2610.03476", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/kaiknower/MobiAgent", "project": "https://kaiknower.github.io/mobiagent", "topics": [ "manipulation", "vla" ], "tags": [ "mobile-manipulation", "long-horizon", "agent", "skill-library", "self-improvement", "real" ], "robot_types": [ "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Mobile-manipulation agent with two loops: a VLM plans and composes flow-matching skills at run time, and an outer loop mines rollouts to find and fine-tune skills. Reports +22.5 pts over π0.5 on BEHAVIOR-1K.", "takeaway_zh": "双循环移动操作智能体：内循环由 VLM 规划并组合流匹配技能，外循环从部署数据中自动发现并微调技能。报告在 BEHAVIOR-1K 上比 π0.5 高 22.5 个百分点。", "github_stars": 17, "page": "https://embodied-ai-radar.com/e/arxiv-2610.03476.html", "labs": [], "code_license": "" }, { "id": "arxiv-2608.13422", "title": "Attention from Action, for Action: Emergent Visual Bottlenecks for Policy Learning", "type": "paper", "arxiv": "2608.13422", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/zheyu-zhuang/seeker", "project": null, "topics": [ "manipulation" ], "tags": [ "attention", "region-of-interest", "dinov3", "data-efficiency", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Seeker learns where to look from action supervision alone, giving progress-aware crops on frozen DINOv3 features. Reports real-robot success 48.3% to 76.7%, and 20% to 60% under lighting and background shifts.", "takeaway_zh": "Seeker 仅凭动作监督学习“看哪里”，在冻结的 DINOv3 特征上给出随任务进展变化的裁剪区域。报告真机成功率从 48.3% 升到 76.7%，光照与背景变化下从 20% 升到 60%。", "github_stars": 6, "page": "https://embodied-ai-radar.com/e/arxiv-2608.13422.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.07229", "title": "Spatial-UMI-Dream: Scaling World Action Models with Metric Geometry for UMI Manipulation", "type": "paper", "arxiv": "2611.07229", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Yewandou7/spatial_umi_dream", "project": "https://weiduoyuan.com/spatial-umi-dream/", "topics": [ "manipulation", "world-models", "datasets" ], "tags": [ "umi", "wam", "rgb-d", "metric-depth", "benchmark", "real", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "5B world action model pretrained on 605 h of RGB-D UMI data that predicts RGB, metric depth and actions. Adds RoboTwin-UMI; reports 85% average real success, +24.2 points over the best baseline.", "takeaway_zh": "在 605 小时 RGB-D UMI 数据上预训练的 5B 世界动作模型，同时预测 RGB、度量深度和动作。并提出 RoboTwin-UMI 基准；报告真机平均成功率 85%，比最强基线高 24.2 个百分点。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2611.07229.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2611.08372", "title": "MOMASPLAT: Dynamic Novel-View Demonstration Augmentation for Mobile Manipulation", "type": "paper", "arxiv": "2611.08372", "year": 2026, "venue": null, "org": "UT Austin", "url": null, "code": null, "project": "https://robin-lab.cs.utexas.edu/momasplat", "topics": [ "manipulation" ], "tags": [ "mobile-manipulation", "data-augmentation", "gaussian-splatting", "novel-view", "real" ], "robot_types": [ "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Augments mobile-manipulation demos with new base poses, re-rendering the moving object too, via Gaussian splatting and action retargeting. Reports more than doubling the best baseline's success on unseen base poses.", "takeaway_zh": "用高斯泼溅与动作重定向为移动操作示教合成新的底盘位姿视角，连被操作的运动物体也一起重渲染。报告在未见底盘位姿下成功率是最强基线的两倍以上。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08372.html", "labs": [ "ut-austin" ], "code_license": null }, { "id": "arxiv-2611.08458", "title": "Latent RL Priors for Data-Efficient Dexterous Manipulation", "type": "paper", "arxiv": "2611.08458", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://yixiaowang7.github.io/latent-rl-prior-page/", "topics": [ "manipulation", "learning" ], "tags": [ "dexterous", "rl", "sim2real", "flow-matching", "data-efficiency", "bimanual" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Distills sim RL rollouts into a frozen flow-matching action decoder, then learns only a visual interface to its latent from a few demos. Reports beating fine-tuned π0.5 and GR00T N1.7 with 5-10x fewer demos.", "takeaway_zh": "把仿真中强化学习的经验蒸馏进冻结的流匹配动作解码器，之后只需用少量示教学习从图像到其潜变量的视觉接口。报告用少 5–10 倍的示教超过微调后的 π0.5 与 GR00T N1.7。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08458.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.27736", "title": "AWLS: Advantage-Weighted Latent Steering for One-Step Generative Robotic Manipulation Policies", "type": "paper", "arxiv": "2610.27736", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://awls-corl2026.github.io/", "topics": [ "manipulation", "learning" ], "tags": [ "offline-rl", "one-step-policy", "noise-steering", "mixed-quality-data", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Improves a frozen one-step generative policy offline by learning a residual on its input noise, trained with advantage-weighted reconstruction instead of critic gradients, so the base behavior does not drift.", "takeaway_zh": "离线改进冻结的一步生成策略：学习对输入噪声的残差修正，用优势加权的动作重建来训练而不是用评论家梯度，从而避免偏离原有行为。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.27736.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.19518", "title": "HiFun: a Hierarchical Framework for Efficient Functional Dexterous Manipulation Learning", "type": "paper", "arxiv": "2610.19518", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://hly-123.github.io/HiFun/", "topics": [ "manipulation", "learning" ], "tags": [ "dexterous", "real-world-rl", "hierarchical", "human-intervention", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Hierarchical real-world RL for functional dexterous tasks: a hand skill handles contact, an arm-hand policy coordinates and triggers it. Reports 98.3% average success on six tasks within one hour of online training.", "takeaway_zh": "面向功能性灵巧操作的分层真机强化学习：手部技能负责精确接触，臂-手协调策略负责手臂运动与技能触发。报告在六个任务上一小时在线训练即达 98.3% 平均成功率。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.19518.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.17281", "title": "Phase-Conditioned Affordance Fields for SE(3)-Equivariant Visuomotor Policies", "type": "paper", "arxiv": "2610.17281", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://paf-policy.github.io/", "topics": [ "manipulation" ], "tags": [ "equivariance", "se3", "affordance", "point-cloud", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Adds a learned, phase-conditioned affordance field to SE(3)-equivariant policies, anchoring observations and actions on the active region instead of the gripper frame. Improves MimicGen and multi-object real tasks.", "takeaway_zh": "为 SE(3) 等变策略加入随阶段变化的可学习可供性场，以当前作用区域而非夹爪坐标系作为观测与动作的锚点。在 MimicGen 与多物体真机任务上都有提升。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.17281.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.36107", "title": "Scouting the Dynamics Gap: Test-Time Policy Adaptation via Action-Outcome Feedback", "type": "paper", "arxiv": "2609.36107", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/liy1shu/SCOUT", "project": "https://liy1shu.github.io/SCOUT/", "topics": [ "manipulation", "learning" ], "tags": [ "test-time-adaptation", "meta-learning", "dynamics", "sim2real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Meta-learned policy and forward model share a belief latent; at deployment the latent is updated from action-outcome mismatches, so the policy adapts to unknown dynamics on the fly.", "takeaway_zh": "元学习的策略与前向动力学模型共享信念潜变量；部署时根据动作结果的偏差更新该潜变量，使策略能即时适应未知动力学。", "github_stars": 6, "page": "https://embodied-ai-radar.com/e/arxiv-2609.36107.html", "labs": [], "code_license": "" }, { "id": "arxiv-2609.28431", "title": "LiMA: Bridging Long-term Imagination to Real-time Dexterous Manipulation via Asynchronous Diffusion", "type": "paper", "arxiv": "2609.28431", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://ccdcs.github.io/LiMA_repo/", "topics": [ "manipulation", "world-models" ], "tags": [ "dexterous", "bimanual", "dual-system", "asynchronous", "schrodinger-bridge" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Slow system plans sparse long-horizon intent, fast system refines dense motion, linked by a latent Schrödinger bridge. Reports 45.8% lower latency than Cosmos-Policy and 70.8% success on six bimanual tasks.", "takeaway_zh": "慢系统生成稀疏的长时程意图，快系统细化高频动作，二者以潜空间薛定谔桥耦合。报告延迟比 Cosmos-Policy 低 45.8%，六个双臂灵巧任务成功率 70.8%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.28431.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.24996", "title": "Learning Beyond What Humans Can Demonstrate", "type": "paper", "arxiv": "2609.24996", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://guardrail-policy.github.io/", "topics": [ "manipulation" ], "tags": [ "teleoperation", "guardrails", "llm-code", "data-collection", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "For tasks people cannot teleoperate well, an LLM writes state-based guardrails that filter teleop and policy commands. Reports data-collection success rising from 0-10% to 70-90%.", "takeaway_zh": "针对人难以遥操作完成的任务，由 LLM 编写基于状态的护栏来过滤遥操作与策略指令。报告数据采集成功率从 0–10% 提升到 70–90%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.24996.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.16586", "title": "ProxiDex: Learning Dynamics-Guided Proximity Policy for Dexterous Manipulation", "type": "paper", "arxiv": "2609.16586", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://proxidex.github.io/", "topics": [ "manipulation" ], "tags": [ "dexterous", "proximity", "point-cloud", "teleoperation", "dynamics" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Uses hand-object proximity from reconstructed point clouds as a hardware-agnostic contact signal, for VR teleop feedback and a policy with learned proximity dynamics. More robust under occlusion.", "takeaway_zh": "以重建点云得到的手-物距离作为与硬件无关的接触信号，用于 VR 遥操作反馈，并训练带接近度动力学的策略；在遮挡下更稳健。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.16586.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.10137", "title": "Assembling Two Parts in One Hand", "type": "paper", "arxiv": "2609.10137", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://ltbgbird.github.io/in-hand-assembly-page/", "topics": [ "manipulation" ], "tags": [ "dexterous", "in-hand", "assembly", "rl", "sim2real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "RL for mating two parts within one dexterous hand (capping a marker, a syringe, a bottle) with no second arm. Trained in sim, transfers zero-shot with a single camera.", "takeaway_zh": "用强化学习在单只灵巧手中完成两个零件的装配（如给马克笔盖帽、注射器、瓶子），不用第二只手臂；仿真训练，单相机零样本迁移到真机。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.10137.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.08743", "title": "FOCI Policy: Focus on Object-Centric Interactions for Relational Manipulation Policies", "type": "paper", "arxiv": "2609.08743", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/fitz0401/foci_policy", "project": "https://fitz0401.github.io/foci-page/", "topics": [ "manipulation" ], "tags": [ "object-centric", "relational", "se3", "data-efficiency", "rlbench" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Represents skills as short interaction segments of relative SE(3) motion between task objects, independent of scene layout and robot. Needs much less data than prior policies on RLBench and COLOSSEUM.", "takeaway_zh": "把技能表示为任务物体之间相对 SE(3) 运动的短交互片段，与场景布局和机器人无关。在 RLBench 与 COLOSSEUM 上所需数据明显少于以往方法。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2609.08743.html", "labs": [], "code_license": "" }, { "id": "arxiv-2607.07101", "title": "GeoProp: Grounding Robot State in Vision for Generalist Manipulation", "type": "paper", "arxiv": "2607.07101", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://alibaba-damo-academy.github.io/GeoProp/", "topics": [ "manipulation" ], "tags": [ "proprioception", "adapter", "diffusion-policy", "robotwin", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Plug-in adapter that projects the robot state onto the image to build a grounded state token and FiLM-modulate nearby features. Reports +8.7% for Diffusion Policy in sim and 10.6% average real-world gain.", "takeaway_zh": "即插即用适配器：把机器人状态投影到图像平面，采样局部视觉特征构成“落地”的状态 token，并用 FiLM 调制对应特征。报告仿真中 Diffusion Policy 提升 8.7%，真机平均提升 10.6%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.07101.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.03280", "title": "How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference", "type": "paper", "arxiv": "2603.03280", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/ToruOwO/how-to-peel", "project": "https://toruowo.github.io/peel", "topics": [ "manipulation" ], "tags": [ "tool-use", "force", "preference-learning", "food", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Knife peeling as a test case for tasks with subjective quality: force-aware imitation first, then preference-based fine-tuning with a learned reward. Reports 100% success on cucumbers, apples and potatoes.", "takeaway_zh": "以用刀削皮为例研究“质量难以量化”的精细操作：先做力感知模仿学习，再用学习到的奖励模型按人类偏好微调。报告在黄瓜、苹果、土豆上成功率 100%。", "github_stars": 25, "page": "https://embodied-ai-radar.com/e/arxiv-2603.03280.html", "labs": [], "code_license": "" }, { "id": "arxiv-2604.20348", "title": "Bimanual Robot Manipulation via Multi-Agent In-Context Learning", "type": "paper", "arxiv": "2604.20348", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://alesspalma.github.io/bicicle", "topics": [ "manipulation" ], "tags": [ "bimanual", "in-context-learning", "llm", "training-free", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Lets text-only LLMs do few-shot bimanual manipulation without fine-tuning by treating the arms as leader and follower agents. Reports 70.5% average success on 13 TWIN tasks.", "takeaway_zh": "把双臂视为“主-从”两个智能体，让纯文本 LLM 无需微调即可通过上下文学习完成少样本双臂操作。报告在 TWIN 的 13 个任务上平均成功率 70.5%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.20348.html", "labs": [], "code_license": null }, { "id": "arxiv-2601.19406", "title": "Sim-and-Human Co-training for Data-Efficient and Scene-Generalizable Bimanual Manipulation", "type": "paper", "arxiv": "2601.19406", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/kaipengfang/SimHum", "project": "https://kaipengfang.github.io/sim-and-human/", "topics": [ "manipulation" ], "tags": [ "co-training", "sim-data", "human-video", "bimanual", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Co-trains a bimanual policy on sim data (for robot-valid actions) and human videos (for real visuals), then fine-tunes on 80 real episodes per task. Reports 62.5% on held-out scenes, 53.7 points above real-only.", "takeaway_zh": "用仿真数据（提供机器人可执行动作）和人类视频（提供真实视觉）联合预训练双臂策略，再在每任务 80 条真机数据上微调。报告在未见场景上成功率 62.5%，比纯真机数据高 53.7 个百分点。", "github_stars": 36, "page": "https://embodied-ai-radar.com/e/arxiv-2601.19406.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2607.27890", "title": "Static In, Dynamic Out: Counterfactual Action Augmentation for Moving Object Manipulation", "type": "paper", "arxiv": "2607.27890", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://sido-staticindynamicout.github.io/", "topics": [ "manipulation" ], "tags": [ "dynamic-objects", "data-augmentation", "counterfactual", "conveyor", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Lets a policy trained only on static objects handle moving ones: demos are augmented by moving the object to a counterfactual future pose and warping the action chunk; a pose predictor supplies the target at test time.", "takeaway_zh": "让只用静止物体演示训练的策略也能抓取运动物体：把物体平移到反事实的未来位姿并相应变形动作块来扩增数据，部署时由位姿预测器给出目标位置。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.27890.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.26423", "title": "CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation", "type": "paper", "arxiv": "2606.26423", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/costream-simple/costream", "project": "https://costream-simple.github.io", "topics": [ "manipulation" ], "tags": [ "composition", "assembly", "foundation-models", "force", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Composes simple behaviours (semantic constraints from foundation models, keypoints from imagined video, tactile/force corrections) into one SE(3) pose command per step. 8 real tasks, incl. seating a GPU.", "takeaway_zh": "把语义约束（基础模型）、想象视频中的关键点预测、触觉/力实时修正等简单行为在 SE(3) 上组合成每一步的位姿指令。8 个真机任务，包括把显卡插入 PCIe 槽。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2606.26423.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2607.26047", "title": "S2A2: Audio-Visual Imitation Learning for Manipulation Tasks Using Acoustic Spatial Information", "type": "paper", "arxiv": "2607.26047", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://azuma413.github.io/projects/s2a2", "topics": [ "manipulation" ], "tags": [ "audio", "multimodal", "imitation-learning", "sound-localization", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "New manipulation tasks where the target must be found by sound, plus an imitation framework that adds acoustic spatial and spectral features to ACT, Diffusion Policy, VQ-BeT and π0. Sim + real.", "takeaway_zh": "提出需要靠声音确定操作目标的新任务，以及把声源空间信息与频谱特征接入 ACT、Diffusion Policy、VQ-BeT 和 π0 的多模态模仿学习框架。仿真与真机验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.26047.html", "labs": [], "code_license": null }, { "id": "arxiv-2412.08468", "title": "Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic Guided Grasp Generation", "type": "paper", "arxiv": "2412.08468", "year": 2024, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://multi-graspllm.github.io", "topics": [ "manipulation" ], "tags": [ "grasp-generation", "dexterous-hands", "llm", "dataset", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Multi-GraspSet, a large multi-hand grasp dataset with automatic contact annotations, and an LLM that generates language-guided grasps for different robot hands in one model.", "takeaway_zh": "发布带自动接触标注的大规模多手型抓取数据集 Multi-GraspSet，并用一个基于大模型的统一框架，根据语言指令为不同机器人手生成抓取。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2412.08468.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.04554", "title": "HUGS: Guiding Unified Dexterous Grasp Synthesis Across Modes and Scales via Learned Human Priors", "type": "paper", "arxiv": "2607.04554", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/hugs-dex/HUGS-Main", "project": "https://hugs-dex.github.io/", "topics": [ "manipulation" ], "tags": [ "dexterous-grasping", "human-prior", "grasp-synthesis", "bimanual", "real" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns an object-conditioned human grasp prior from 1.8K human grasps to pick contact modes and wrist starts for force-closure optimisation; synthesises 3.2M grasps from pinches to bimanual, 2–30 cm objects.", "takeaway_zh": "从 1800 次人类抓取中学习物体条件的人类先验，用来选择接触模式和手腕初值以指导力封闭优化；合成 320 万个抓取，覆盖两指捏取到双手抓取、2–30 cm 物体。", "github_stars": 14, "page": "https://embodied-ai-radar.com/e/arxiv-2607.04554.html", "labs": [], "code_license": "" }, { "id": "arxiv-2604.21331", "title": "FingerViP: Learning Real-World Dexterous Manipulation with Fingertip Visual Perception", "type": "paper", "arxiv": "2604.21331", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/zhzhen0626/FingerViP", "project": "https://fingervip.github.io/", "topics": [ "manipulation" ], "tags": [ "dexterous", "fingertip-cameras", "diffusion-policy", "multi-view", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Puts miniature cameras in each fingertip of a multi-fingered hand and trains a whole-body diffusion policy on third-view plus fingertip views. Reports 56.6% relative gain over external and wrist cameras.", "takeaway_zh": "在多指手的每个指尖装入微型摄像头，用第三视角加多指尖视角训练全身扩散策略。报告比外部+腕部相机方案平均成功率相对提升 56.6%。", "github_stars": 6, "page": "https://embodied-ai-radar.com/e/arxiv-2604.21331.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.30749", "title": "From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation", "type": "paper", "arxiv": "2606.30749", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://yingyuan0414.github.io/grasp2dexterity/", "topics": [ "manipulation" ], "tags": [ "dexterous", "grasp-pretraining", "tool-use", "hierarchical", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Pretrains a low-level dexterous controller on 355K trajectories built from grasp datasets, then fine-tunes for articulated tool use. Introduces the DexCraft benchmark; reports +33.3 points over DP3 on a real robot.", "takeaway_zh": "用抓取数据集构造的 35.5 万条轨迹预训练灵巧手底层控制器，再微调到需要操作工具活动部件的任务；提出 DexCraft 基准。报告真机完整任务成功率比 DP3 高 33.3 个百分点。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.30749.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.04269", "title": "Instant-Fold: In-Context Imitation Learning for Deformable Object Manipulation", "type": "paper", "arxiv": "2606.04269", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/kelthuzadyl/Instant-Fold", "project": "https://instant-fold.github.io", "topics": [ "manipulation" ], "tags": [ "deformable", "cloth-folding", "in-context-learning", "sim-to-real", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "In-context imitation for folding: from a single human demo, a flow-matching transformer infers and executes the shown folding mode without gradient updates. Trained only in sim, transfers zero-shot to real.", "takeaway_zh": "面向折叠的上下文模仿学习：只看一条人类演示，流匹配 Transformer 即可推断并执行其中的折叠方式，无需梯度更新。完全在仿真训练，零样本迁移到真机。", "github_stars": 14, "page": "https://embodied-ai-radar.com/e/arxiv-2606.04269.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.01301", "title": "Continual Learning for 6-DoF Grasp Synthesis via Experience and Demonstrations", "type": "paper", "arxiv": "2610.01301", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://giuschio.github.io/cl_grasping/", "topics": [ "manipulation" ], "tags": [ "grasping", "continual-learning", "memory", "6-dof", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "6-DoF grasping that keeps learning on the job through memory in an embedding space: outcomes rescore future grasps and user demos are recalled in new scenes. Reports over 90% on hard categories after 50 tries.", "takeaway_zh": "通过嵌入空间中的记忆在部署中持续学习的 6-DoF 抓取：抓取结果更新后续评分，用户示范可在新场景中被召回。报告在困难类别上仅 50 次尝试后成功率超过 90%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.01301.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.00915", "title": "DiffMorphGrasp: A Diffusion Framework Conditioned on Hand Morphology for Cross-Embodiment Grasp Generation", "type": "paper", "arxiv": "2602.00915", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://georgewuzy.github.io/DiffMorphGrasp-website", "topics": [ "manipulation" ], "tags": [ "dexterous-grasping", "cross-embodiment", "diffusion", "hand-morphology", "benchmark" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Diffusion grasp generator conditioned on a graph of the hand's kinematics, so grasps transfer zero-shot to unseen hand designs; comes with a cross-morphology grasp benchmark.", "takeaway_zh": "以手部运动学结构图为条件的扩散抓取生成模型，可零样本迁移到未见过的机器人手型；同时提出跨手型抓取评测基准。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.00915.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.22102", "title": "Wiggle and Go! System Identification for Zero-Shot Dynamic Rope Manipulation", "type": "paper", "arxiv": "2604.22102", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://wiggleandgo.github.io/", "topics": [ "manipulation" ], "tags": [ "deformable", "rope", "system-identification", "dynamic", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "A short safe wiggle reveals rope parameters that condition a trajectory optimiser for zero-shot dynamic throws. Reports 3.55 cm average error on real 3D target striking vs 15.29 cm for uninformed baselines.", "takeaway_zh": "先做一次短暂安全的“抖绳”动作来辨识绳子参数，再以此驱动轨迹优化，零样本完成动态甩绳。报告真机 3D 目标击打平均误差 3.55 cm，无辨识基线为 15.29 cm。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.22102.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.03529", "title": "Current as Touch: Proprioceptive Contact Feedback for Compliant Dexterous Manipulation", "type": "paper", "arxiv": "2607.03529", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://cat.chenyangma.com", "topics": [ "manipulation" ], "tags": [ "dexterous", "motor-current", "compliance", "proprioception", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Uses motor current as a touch signal on low-cost dexterous hands: a model predicts a compliant PD target position whose error yields the right grasp force, with no tactile or force sensors.", "takeaway_zh": "把电机电流当作触觉：在低成本灵巧手上预测一个“柔顺参考位置”，让 PD 控制器的位置误差产生合适的抓握力，无需触觉或力传感器。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.03529.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.07413", "title": "Going with the Flow: Koopman Behavioral Models as Pseudo Planners for Visuo-Motor Dexterity", "type": "paper", "arxiv": "2602.07413", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/GT-STAR-Lab/K-UBM", "project": "https://k-ubm.github.io/", "topics": [ "manipulation" ], "tags": [ "dexterous", "koopman", "dynamical-systems", "replanning", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Models a dexterous skill as a latent linear (Koopman) system in which visual flow and robot motion co-evolve; it replans when observed and predicted flow diverge. Matches strong baselines with much faster inference.", "takeaway_zh": "把灵巧技能建模为潜在线性（Koopman）系统，视觉特征流与机器人动作流共同演化；当观测与预测的视觉流偏离时自动重规划。性能与强基线相当，推理快得多。", "github_stars": 5, "page": "https://embodied-ai-radar.com/e/arxiv-2602.07413.html", "labs": [], "code_license": "" }, { "id": "arxiv-2602.14032", "title": "RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation", "type": "paper", "arxiv": "2602.14032", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Open-X-Humanoid/RoboAug", "project": "https://x-roboaug.github.io/", "topics": [ "manipulation" ], "tags": [ "data-augmentation", "generative-models", "generalization", "attention", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Generative augmentation from a single box-annotated image, plus a region-contrastive loss that steers attention to task regions. Beats augmentation baselines on UR5e, AgileX and Tian Gong 2.0 under scene shifts.", "takeaway_zh": "只需一张带框标注的图片即可做生成式语义增强，并用区域对比损失把注意力引向任务相关区域。在 UR5e、AgileX 与天工 2.0 上，面对背景、干扰物和光照变化时优于其他增强方法。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2602.14032.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2607.11874", "title": "A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation", "type": "paper", "arxiv": "2607.11874", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/yunhaif/regrind", "project": "https://yunhaifeng.com/REGRIND", "topics": [ "manipulation" ], "tags": [ "dexterous", "retargeting", "residual-rl", "tool-use", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "From one human demo: retarget hand-object motion to a robot reference, train residual RL in sim to track object keypoints, transfer zero-shot with careful system ID. Scissors and screwdriver use on two hands.", "takeaway_zh": "从一条人类演示出发：把手-物运动重定向为机器人参考，在仿真中用残差强化学习跟踪物体关键点，并通过细致的系统辨识零样本迁移。在两种多指手上完成用剪刀、拧螺丝刀等任务。", "github_stars": 121, "page": "https://embodied-ai-radar.com/e/arxiv-2607.11874.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2607.11481", "title": "Towards Human-level Dexterous Teleoperation", "type": "paper", "arxiv": "2607.11481", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://bigai-dex.github.io/blog/teledexter/", "topics": [ "manipulation" ], "tags": [ "dexterous", "teleoperation", "in-hand", "rl", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Teleoperation controller that turns operator intent into learned hand-object contact execution, trained with single-stage RL on human motions. Reports 75% average success on seven in-hand and tool-use tasks.", "takeaway_zh": "遥操作控制器把操作者意图转化为学习到的手-物接触执行，基于人类动作用单阶段强化学习训练，零样本迁移真机。报告七个手内操作与工具使用任务平均成功率 75%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.11481.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.19182", "title": "ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning", "type": "paper", "arxiv": "2608.19182", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://adept-dexterity.github.io/", "topics": [ "manipulation" ], "tags": [ "dexterous", "rl-pretraining", "sim-to-real", "visuotactile", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Pretrains a dexterous RL policy on generic object reposing, then post-trains downstream tasks with BC distillation, critic warm-up and conservative updates. Zero-shot sim-to-real on 23-DoF and 29-DoF hand-arm systems.", "takeaway_zh": "先在通用物体重定位任务上用强化学习预训练灵巧策略，再用行为克隆蒸馏、critic 预热和保守更新后训练下游任务。在 23 与 29 自由度手-臂系统上零样本仿真到真机。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.19182.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.28813", "title": "Human2Any: Human-to-Robot Transfer via Constraint-Aware Compositional Planning", "type": "paper", "arxiv": "2606.28813", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://human2any.github.io/", "topics": [ "manipulation" ], "tags": [ "human-video", "object-centric", "motion-planning", "cross-embodiment", "real" ], "robot_types": [ "arm", "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns object-object interaction priors from human videos and composes them with robot feasibility reasoning and motion planning, so one prior serves a Franka and an RBY-1 mobile humanoid without robot data.", "takeaway_zh": "从人类视频学习以物体间交互运动表示的先验，再与机器人可行性推理和运动规划组合，同一先验无需机器人数据即可用于 Franka 与 RBY-1 移动人形机器人。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.28813.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.15828", "title": "Dex4D: Task-Agnostic Point Track Policy for Zero-Shot Dexterous Manipulation", "type": "paper", "arxiv": "2602.15828", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Dex4D/Dex4D-Simulation", "project": "https://dex4d.github.io/", "topics": [ "manipulation" ], "tags": [ "dexterous", "point-tracks", "sim-to-real", "zero-shot", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Task-agnostic 'any pose to any pose' dexterous policy trained in sim on thousands of objects and conditioned on 3D point tracks; prompted with tracks from generated videos, it runs real tasks zero-shot.", "takeaway_zh": "在仿真中用数千种物体训练与任务无关的“任意位姿到任意位姿”灵巧策略，以 3D 点轨迹为条件；用生成视频提取的点轨迹作提示，即可零样本完成真实任务。", "github_stars": 34, "page": "https://embodied-ai-radar.com/e/arxiv-2602.15828.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2604.08983", "title": "AssemLM: A Spatial Reasoning Multimodal Large Language Model for Robotic Assembly", "type": "paper", "arxiv": "2604.08983", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/TeleHuman/AssemLM", "project": "https://assemlmhome.github.io/", "topics": [ "manipulation" ], "tags": [ "assembly", "3d-reasoning", "multimodal-llm", "benchmark", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Multimodal LLM that reads assembly manuals, point clouds and text to predict 6D assembly poses, plus AssemBench with 900K+ samples. Reports state-of-the-art 6D pose reasoning and real multi-step assembly.", "takeaway_zh": "读取装配说明书、点云与文字指令来预测 6D 装配位姿的多模态大模型，并发布含 90 万余样本的 AssemBench。报告 6D 位姿推理达到最优，并完成真实多步装配。", "github_stars": 56, "page": "https://embodied-ai-radar.com/e/arxiv-2604.08983.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2607.11884", "title": "Mixture of Frames Policy: Multi-Frame Action Denoising for Bimanual Mobile Manipulation", "type": "paper", "arxiv": "2607.11884", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "diffusion-policy", "coordinate-frames", "bimanual", "mobile-manipulation", "real" ], "robot_types": [ "arm", "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Diffusion policy that denoises one action in several coordinate frames at once with frame-specialised denoisers and fuses the results. Beats oracle single-frame choice on nine bimanual tasks; two real mobile tasks.", "takeaway_zh": "扩散策略在多个坐标系中同步去噪同一动作：各坐标系有专门的去噪器，再把噪声预测融合回规范坐标系。在九个双臂任务上优于“最优单坐标系”选择，并在两个真实移动操作任务上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.11884.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.08003", "title": "Reactive Task-Oriented Robot-Human Handovers via Generative Hypothesis Selection", "type": "paper", "arxiv": "2610.08003", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "handover", "human-robot-interaction", "vlm", "image-generation", "user-study" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Task-aware robot-to-human handovers: a VLM generates images of how a person would hold the object for the task, matched live to the person's hand. 83.3% of 12 users felt it understood the task better.", "takeaway_zh": "面向任务的机器人递物：用 VLM 生成人在该任务下如何握持物体的图像假设，并与实时观测的人手姿态匹配。12 名参与者中 83.3% 认为它比已有方法更懂任务。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.08003.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.07532", "title": "Semantics-to-Contact: A Stagewise Framework for Robust Contact-Rich Manipulation", "type": "paper", "arxiv": "2611.07532", "year": 2026, "venue": "NeurIPS 2026", "org": null, "url": null, "code": null, "project": "https://edem-ai.github.io/S2C/", "topics": [ "manipulation" ], "tags": [ "contact-rich", "residual-rl", "human-in-the-loop", "vlm", "force", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "A VLM first localizes the task region, then real-world human-in-the-loop residual RL with force feedback learns the contact part. Object-centric background augmentation adds robustness; six real tasks.", "takeaway_zh": "分两阶段：先由视觉语言模型定位任务相关区域，再在真实世界用带力反馈、人在回路的残差强化学习学习接触细节。以物体为中心的背景增强使其在六个真机任务上保持稳健。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.07532.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.23445", "title": "BiRoAD: Learning Shared and Role-Adaptive Representations for Bimanual Manipulation", "type": "paper", "arxiv": "2609.23445", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "bimanual", "representation", "symmetry" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Splits bimanual policy features into a swap-symmetric part (coordination that holds when arms trade roles) and a swap-antisymmetric part (role-specific behaviour), recomposed as residuals on each arm's features.", "takeaway_zh": "把双臂策略特征分解为交换对称分量（两臂互换角色时不变的协调结构）与交换反对称分量（随角色变化的行为差异），再以残差形式加回各臂的表示。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.23445.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.30632", "title": "GROW$^2$: Grounding Which and Where for Robot Tool Use", "type": "paper", "arxiv": "2606.30632", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "tool-use", "affordance", "vlm", "zero-shot", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Creative tool use (cut a cake with a plate): a VLM picks the object to use as a tool and the relevant parts, then vision foundation models ground those parts as 3D regions from one RGB-D image. No end-to-end training.", "takeaway_zh": "创造性用工具（没有刀就用盘子切蛋糕）：VLM 选出可充当工具的物体及其相关部位，再由视觉基础模型从单张 RGB-D 图像把这些部位定位为 3D 区域。无需端到端训练。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.30632.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.13394", "title": "GeoHAT: Geometry-Adaptive Hybrid Action Transformer for Mobile Manipulation", "type": "paper", "arxiv": "2606.13394", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "mobile-manipulation", "whole-body", "diffusion-policy", "3d" ], "robot_types": [ "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Diffusion policy for whole-body mobile manipulation: a Fourier encoder turns per-pixel 3D points into tokens gated in by depth validity, and a hybrid decoder gives arm and base separate action subspaces.", "takeaway_zh": "面向全身移动操作的扩散策略：傅里叶编码器把逐像素 3D 坐标变成几何 token，并按深度可靠性门控注入视觉基础模型特征；混合解码器为机械臂与底盘分配独立的动作子空间。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.13394.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.23973", "title": "REAP: Learning General Rearrangement Heuristics from Motion-Planner Feedback", "type": "paper", "arxiv": "2610.23973", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "rearrangement", "task-planning", "gnn", "motion-planning" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns a relational GNN heuristic over a simple symbolic model, with a motion planner revealing hidden geometric preconditions during training. Its top-ranked action is feasible in 94% of states (22% random).", "takeaway_zh": "在简单符号模型上学习关系图神经网络启发式，训练时由运动规划器揭示隐藏的几何前提。其排名第一的动作在 94% 的状态下几何可行（随机选择为 22%）。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.23973.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.02408", "title": "F2F-AP: Flow-to-Future Asynchronous Policy for Real-time Dynamic Manipulation", "type": "paper", "arxiv": "2604.02408", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "async-execution", "dynamic-manipulation", "object-flow", "latency" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Predicts object flow to synthesize future observations, aligned to real future frames by a contrastive loss, so an asynchronous policy can act ahead of its own latency on moving objects.", "takeaway_zh": "利用预测的物体流合成未来观测，并以对比学习目标使其特征与真实未来帧对齐，让异步策略能提前补偿自身延迟，处理运动中的物体。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.02408.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.15587", "title": "Perfect Demo Makes Poor Teacher: Learning Robust Alignment from Critical Motion Segments", "type": "paper", "arxiv": "2606.15587", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "imitation-learning", "demonstrations", "insertion", "vla" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Fluent expert demos rush through alignment and recovery. Slowing down there and resampling those segments helps; STAIR adds a compact short-horizon motion feature between the VLM and the action expert.", "takeaway_zh": "熟练的示教会把对准与纠错一带而过，导致策略在关键处缺少监督。在这些片段放慢、重采样有帮助；STAIR 进一步在 VLM 与动作专家之间加入紧凑的短时运动特征。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.15587.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.24208", "title": "Grounding Generative Policies in Physics: Optimization-Guided Diffusion for Robot Control", "type": "paper", "arxiv": "2606.24208", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "diffusion", "constraints", "grasping", "cross-embodiment" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Replaces the sampling noise in diffusion's backward process with an optimized correction, so reachability, collision or trackability constraints hold at sampling time without retraining.", "takeaway_zh": "把扩散反向过程中的采样扰动替换为经优化得到的修正，从而在采样时满足可达性、避碰或可跟踪性约束，无需重新训练扩散模型。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.24208.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.26588", "title": "Inference-Time Robot Behavior Steering through Physically-Aware Reconfiguration of Task-Structure", "type": "paper", "arxiv": "2606.26588", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "policy-steering", "automata", "temporal-logic", "vlm", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "ReStruct splits a policy into a state-machine skeleton and residual controller; a VLM turns a language preference into a temporal-logic automaton that reshapes the skeleton. 92.5% preference success on a real robot.", "takeaway_zh": "ReStruct 把视觉运动策略分解为状态机骨架和残差控制器；VLM 把语言偏好编译成时序逻辑自动机来重构骨架。真机上偏好遵循成功率 92.5%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.26588.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.23943", "title": "ESCAPE: Egocentric Sensing for Collision Avoidance and Policy Execution", "type": "paper", "arxiv": "2610.23943", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "collision-avoidance", "on-body-sensing", "flow-matching", "sim2real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Distributed depth sensors on the robot form an obstacle-aware point cloud; a flow-matching policy trained on 100K+ simulated safety-critical trajectories plans collision-free motion and transfers zero-shot to hardware.", "takeaway_zh": "机器人身上的分布式深度传感器汇成障碍感知点云；在 10 万多条仿真安全关键轨迹上训练的流匹配策略据此生成无碰撞运动，零样本迁移到真机。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.23943.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.02171", "title": "GoalDex: Learning Image-Goal-Conditioned Policies for Dexterous In-Hand Manipulation", "type": "paper", "arxiv": "2611.02171", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "dexterous", "in-hand-reorientation", "image-goal", "sim2real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "End-to-end in-hand reorientation from RGB and proprioception, with no object pose estimation: distilled from privileged-state teachers, and the target is given as an image. One policy transfers to five real objects.", "takeaway_zh": "仅凭 RGB 与本体感觉、无需物体位姿估计的端到端手内重定向：由特权状态教师蒸馏而来，目标姿态直接用图像给出。单一策略直接迁移到五个真实物体。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.02171.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.12498", "title": "ArtManip: Category-Level Articulated In-Hand Manipulation", "type": "paper", "arxiv": "2609.12498", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "dexterous", "articulated-objects", "in-hand", "procedural", "rl" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Category-level in-hand manipulation of articulated objects: procedural objects and functional grasps, then two-stage training with articulation physics randomization, reward curriculum and latent distillation.", "takeaway_zh": "类别级的铰接物体手内操作：程序化生成物体与功能性抓取，再用两阶段训练（铰接物理随机化、奖励课程、潜表示蒸馏）学习策略，可泛化到未见实例。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.12498.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.19188", "title": "PartialBiGrasp: Inferring Hidden Local Geometry for Bimanual Grasping from Partial Views", "type": "paper", "arxiv": "2608.19188", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "bimanual", "grasping", "partial-point-cloud", "occupancy" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Dual-arm grasps for large objects from partial point clouds: a convolutional occupancy network reasons about local thickness and clearance, producing force-closure grasp pairs refined by sampling-based optimization.", "takeaway_zh": "从局部点云为大型物体生成双臂抓取：卷积占据网络推断局部厚度与夹爪间隙等几何，生成满足力封闭的抓取对，再用基于采样的优化修正几何不完整带来的歧义。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.19188.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.25410", "title": "T(R,O) Grasp++: Cross-Embodiment Dexterous Grasping in Cluttered Environments at Scale", "type": "paper", "arxiv": "2610.25410", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "dexterous-grasping", "cross-embodiment", "clutter", "diffusion", "benchmark" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "A simulation benchmark of about 0.5B grasp labels over eight dexterous hands and ~50K objects, plus a diffusion model for cross-embodiment grasps in clutter: 88% average success, 0.3 s inference.", "takeaway_zh": "构建约 5 亿条抓取标注、覆盖 8 种灵巧手和约 5 万个物体的仿真基准，并提出跨本体的杂乱场景灵巧抓取扩散模型：平均成功率 88%，推理 0.3 秒。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25410.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.23090", "title": "Flow as Flow: Modeling Robot Velocity Fields as Probability Velocity Fields for Flow-Based Object Manipulation", "type": "paper", "arxiv": "2606.23090", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "object-flow", "flow-matching", "cross-embodiment", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Models robot flow as dense velocity fields in a flow-matching formulation rather than sparse keypoint displacements, about 24x faster than standard flow matching; tested in 13 real tasks with 260 trials per method.", "takeaway_zh": "在流匹配框架中把机器人流建模为稠密速度场，而非稀疏关键点位移，生成速度比标准流匹配快约 24 倍；在 13 个真实任务上每种方法测试 260 次。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.23090.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.10918", "title": "ObstaDiff: Generalizable Diffusion Policy Learning via Obstacle-aware Representations", "type": "paper", "arxiv": "2609.10918", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "diffusion-policy", "clutter", "obstacle-avoidance", "agriculture", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Diffusion policy with an obstacle-aware encoder that splits the scene into target, obstacle and background. 61 real greenhouse trials per method: 75.41% success and 8.20% collision rate.", "takeaway_zh": "带障碍感知编码器的扩散策略，把场景分解为目标、障碍和背景。每种方法在温室真机测试 61 次：成功率 75.41%，碰撞率 8.20%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.10918.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.14535", "title": "Spatially Conditioned Diffusion Policy: Learning Precise and Robust Manipulation with a Single RGB Camera", "type": "paper", "arxiv": "2606.14535", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "diffusion-policy", "single-camera", "visual-attention", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Single-camera diffusion policy: points along intermediate end-effector trajectories in the denoising loop sample multi-scale image features, acting as attention anchors. Comparable to multi-camera baselines in sim.", "takeaway_zh": "单相机扩散策略：在去噪循环中沿中间末端轨迹的点采样多尺度图像特征，作为视觉注意力锚点。仿真中表现与多相机基线相当。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.14535.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.17208", "title": "DexNinja: Learning Robust Dexterous Cutting Policy with a Real-to-Sim-to-Real Data Engine", "type": "paper", "arxiv": "2610.17208", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "dexterous", "cutting", "deformable", "real2sim2real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Dexterous food cutting from a handful of real demos: reconstruct objects, randomize physical parameters within category limits and generate augmented demos in sim, then transfer back to the real hand.", "takeaway_zh": "只用少量真实示教学习灵巧切割食物：重建物体实例，在类别约束下随机化物理参数，在仿真中生成扩增的灵巧示教，再迁移回真实灵巧手。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.17208.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.10809", "title": "WARPED: Wrist-Aligned Rendering for Robot Policy Learning from Egocentric Human Demonstrations", "type": "paper", "arxiv": "2604.10809", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "human-video", "egocentric", "gaussian-splatting", "wrist-camera" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Turns monocular egocentric human videos into robot training data: tracks hand and object, retargets to a gripper, and renders photorealistic wrist-camera views with Gaussian Splatting.", "takeaway_zh": "把单目第一人称人类视频转为机器人训练数据：跟踪手与物体，重定向到机械夹爪，并用高斯泼溅渲染逼真的腕部相机视角。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.10809.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.00033", "title": "Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration", "type": "paper", "arxiv": "2607.00033", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "dexterous", "bimanual", "human-demos", "rl", "benchmark" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "CHORD guides dexterous RL by comparing human and robot motions in object-centric contact wrench space. Also a 4,739-task bimanual benchmark built from MoCap and videos; 82.12% average on 1,831 tasks.", "takeaway_zh": "CHORD 在以物体为中心的接触力旋量空间中比较人与机器人的动作来引导灵巧操作强化学习，并构建来自动捕与视频的 4739 个双手任务基准；在其中 1831 个任务上平均成功率 82.12%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.00033.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.13438", "title": "ContactGuard: Pre-Contact Execution Monitoring with Action-Conditioned Latent World Models", "type": "paper", "arxiv": "2608.13438", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "failure-prediction", "latent-world-model", "contact-rich", "monitoring" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Before contact, a latent world model rolls the policy's planned action chunk forward in multi-view embedding space, and a small probe aborts if the predicted post-contact state looks like failure.", "takeaway_zh": "在接触发生前，用潜空间世界模型在多视角嵌入空间中推演策略计划的动作块，若预测的接触后状态呈现失败迹象，轻量探针即中止执行。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.13438.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.17843", "title": "Physics-Aware Object-Centric SE(3) Forecasting for Real-Time Proactive Dynamic Grasping", "type": "paper", "arxiv": "2610.17843", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "dynamic-grasping", "forecasting", "se3", "catching" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "For catching and intercepting moving objects, forecasts the object's future SE(3) trajectory from short pose histories (15 ms) and picks a reachable contact state; reports real success rising from 15.3% to 67.3%.", "takeaway_zh": "面向抓取运动物体的主动式动态抓取：由短时位姿历史预测物体未来 SE(3) 轨迹（15 毫秒），再选择可达的接触状态；报告真实环境成功率从 15.3% 提升到 67.3%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.17843.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.11934", "title": "Robot-DIFT: Correspondence-Sensitive Diffusion Features for Contact-Rich Robot Manipulation", "type": "paper", "arxiv": "2602.11934", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "visual-representation", "diffusion-features", "contact-rich", "distillation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Robot-DIFT distills a noisy diffusion feature extractor into a deterministic single-pass backbone with a feature pyramid, so policies see fine correspondence cues for the last millimeters of contact-rich tasks.", "takeaway_zh": "Robot-DIFT 将依赖噪声的扩散特征提取器蒸馏为确定性的单次前向骨干网络并配合特征金字塔，使策略获得精细的对应关系线索，改善接触密集任务最后几毫米的精度。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.11934.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.14372", "title": "DexFLEX: Contact-Aware Foundation Controller for Command-Guided Dexterity", "type": "paper", "arxiv": "2610.14372", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "manipulation" ], "tags": [ "dexterous", "tactile", "shared-control", "teleoperation" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "DexFLEX treats fingertip motion commands from teleop or a policy as intent: it proposes short motion chunks, predicts their contact outcome and picks the best; real teleop success rises from 29.2% to 78.3%.", "takeaway_zh": "DexFLEX 把来自遥操作或策略的指尖运动指令视为意图：生成若干短时动作块、预测其接触结果并择优执行；真实遥操作成功率从 29.2% 提升到 78.3%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.14372.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.08341", "title": "AnyDexRT: Calibration-Efficient Dexterous Hand Retargeting with Few-Shot Human Guidance", "type": "paper", "arxiv": "2607.08341", "year": 2026, "venue": "CoRL 2026", "org": "Shanghai Jiao Tong University", "url": null, "code": "https://github.com/chenxi-wang/anydexrt", "project": "https://chenxi-wang.github.io/projects/anydexrt", "topics": [ "manipulation" ], "tags": [ "dexterous", "retargeting", "teleoperation", "few-shot" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-07", "takeaway_en": "Low-calibration hand retargeting for teleop: self-supervised fingertip correspondence plus a few human-guided anchors, and a contact classifier to refine pinch poses; tested on several dexterous hands.", "takeaway_zh": "面向遥操作的灵巧手动作重定向，几乎无需标定：自监督学习指尖对应关系，再用少量人工引导的锚点约束映射，并以接触分类器细化捏取姿态；在多种灵巧手上验证。", "github_stars": 7, "page": "https://embodied-ai-radar.com/e/arxiv-2607.08341.html", "labs": [ "sjtu" ], "code_license": "" }, { "id": "arxiv-2611.09864", "title": "Grasp-TAGS: Task-Aware Grasp Selection via Critic Distillation for Prehensile Loco-Manipulation", "type": "paper", "arxiv": "2611.09864", "year": 2026, "venue": null, "org": "University of Minnesota", "url": null, "code": null, "project": "https://rpm-lab-umn.github.io/Grasp-TAGS-web-page/", "topics": [ "manipulation" ], "tags": [ "grasp-selection", "loco-manipulation", "critic-distillation", "rl" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-09", "takeaway_en": "Chooses the initial grasp for large-object rearrangement by distilling a multi-grasp PPO critic into a small utility network that ranks grasps before contact; reports 89.8% vs 79.9% for the best fixed-grasp policy.", "takeaway_zh": "为大物体重排选择初始抓取：把多抓取 PPO 策略的价值函数蒸馏成轻量效用网络，在接触前为候选抓取排序；成功率 89.8%，最佳固定抓取策略为 79.9%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.09864.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.09130", "title": "Auditing Grasp Feasibility Predictors with Planner-Verified Counterfactuals", "type": "paper", "arxiv": "2611.09130", "year": 2026, "venue": "WACV 2027", "org": null, "url": null, "code": "https://github.com/codewithsajid/graspfc-cf", "project": null, "topics": [ "manipulation", "datasets" ], "tags": [ "grasp-feasibility", "counterfactual", "evaluation", "tamp" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-09", "takeaway_en": "Drops an obstacle into a fixed grasp's approach path and lets a planner relabel it: eight feasibility predictors fall from 0.92–0.996 AUROC to chance, so they ignore collision geometry. Labels and pipeline released.", "takeaway_zh": "固定抓取位姿、在接近路径中插入障碍物并由规划器重新标注：八种可行性预测器的 AUROC 从 0.92–0.996 跌至随机水平，说明它们并未利用碰撞几何。发布标签与流程。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2611.09130.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2611.10410", "title": "SimDuoReal: Object-Centric Bimanual Policy for Emergent Dexterity", "type": "paper", "arxiv": "2611.10410", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://simduoreal.github.io", "topics": [ "manipulation", "learning" ], "tags": [ "bimanual", "dexterous", "rl", "sim-to-real", "handover", "distillation" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": "2026-11-10", "takeaway_en": "One shared object-pose reward, varied training scenes: RL experts discover regrasping, in-hand adjustment and handover on their own, then distil into one policy. Reports 61 of 80 real trials with unseen objects.", "takeaway_zh": "只用统一的物体位姿奖励并变换训练环境，强化学习专家自行学会重抓、手内调整与双手交接，再蒸馏成单一策略。报告在未见物体上真实实验 80 次成功 61 次。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.10410.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.11166", "title": "DexForesight: Grounding Dexterous Policies with Implicit Spatial Foresight", "type": "paper", "arxiv": "2611.11166", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://dexforesight.github.io/", "topics": [ "manipulation", "world-models" ], "tags": [ "dexterous", "world-model", "representation-alignment", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-10", "takeaway_en": "Trains a world model of 3D hand-point and scene motion, then aligns policy latents to it; at test time only the RGB policy runs, with no depth or future frames. Reports 49.4% → 60.5% on 11 DexJoCo tasks.", "takeaway_zh": "先训练预测 3D 手部点与场景变化的世界模型，再把策略潜表示对齐到它；部署时只保留 RGB 策略，无需深度或未来预测。在 11 个 DexJoCo 任务上成功率从 49.4% 提升到 60.5%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.11166.html", "labs": [], "code_license": null }, { "id": "arxiv-2005.14679", "title": "DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation", "type": "paper", "arxiv": "2005.14679", "year": 2020, "venue": null, "org": "Meta AI", "url": null, "code": "https://github.com/facebookresearch/digit-interface", "project": null, "topics": [ "tactile", "hardware" ], "tags": [ "real", "sensor", "vision-based-tactile", "low-cost" ], "robot_types": [ "hand" ], "highlight": true, "rating": 3, "added": null, "takeaway_en": "An inexpensive, compact GelSight-style fingertip sensor that made vision-based touch widely accessible.", "takeaway_zh": "低成本、紧凑的 GelSight 式指尖传感器，让视触觉传感器得以普及。", "github_stars": 94, "page": "https://embodied-ai-radar.com/e/arxiv-2005.14679.html", "labs": [ "meta" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2012.08456", "title": "TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensors", "type": "paper", "arxiv": "2012.08456", "year": 2020, "venue": null, "org": "Meta AI", "url": null, "code": "https://github.com/facebookresearch/tacto", "project": null, "topics": [ "tactile", "sim" ], "tags": [ "sim", "vision-based-tactile" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Renders DIGIT/GelSight-style tactile images in simulation so touch-based policies can be trained at scale.", "takeaway_zh": "在仿真中渲染 DIGIT/GelSight 式触觉图像，便于大规模训练基于触觉的策略。", "github_stars": 471, "page": "https://embodied-ai-radar.com/e/arxiv-2012.08456.html", "labs": [ "meta" ], "code_license": "MIT" }, { "id": "arxiv-2410.24090", "title": "Sparsh: Self-supervised touch representations for vision-based tactile sensing", "type": "paper", "arxiv": "2410.24090", "year": 2024, "venue": null, "org": "Meta AI", "url": null, "code": "https://github.com/facebookresearch/sparsh", "project": null, "topics": [ "tactile" ], "tags": [ "representation", "vision-based-tactile", "open-weights" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Self-supervised encoders pretrained on many tactile images that work across different vision-based touch sensors.", "takeaway_zh": "在大量触觉图像上自监督预训练的编码器，可跨不同视触觉传感器使用。", "github_stars": 225, "page": "https://embodied-ai-radar.com/e/arxiv-2410.24090.html", "labs": [ "meta" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2401.18084", "title": "Binding Touch to Everything: Learning Unified Multimodal Tactile Representations", "type": "paper", "arxiv": "2401.18084", "year": 2024, "venue": null, "org": "Yale, UMich", "url": null, "code": null, "project": null, "topics": [ "tactile" ], "tags": [ "representation", "multimodal" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "UniTouch aligns tactile signals with vision, language and audio embeddings, enabling zero-shot touch understanding.", "takeaway_zh": "UniTouch 将触觉信号与视觉、语言、音频嵌入对齐，实现零样本触觉理解。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2401.18084.html", "labs": [], "code_license": null }, { "id": "arxiv-2410.24091", "title": "3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing", "type": "paper", "arxiv": "2410.24091", "year": 2024, "venue": null, "org": "Columbia, UIUC", "url": null, "code": null, "project": null, "topics": [ "tactile", "manipulation" ], "tags": [ "real", "bimanual", "visuotactile", "diffusion" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Fuses dense tactile arrays with 3D vision in a diffusion policy for fragile, contact-rich bimanual tasks.", "takeaway_zh": "在扩散策略中融合密集触觉阵列与 3D 视觉，完成易碎、接触丰富的双臂任务。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2410.24091.html", "labs": [ "columbia" ], "code_license": null }, { "id": "arxiv-2409.08276", "title": "AnySkin: Plug-and-play Skin Sensing for Robotic Touch", "type": "paper", "arxiv": "2409.08276", "year": 2024, "venue": null, "org": "NYU, Meta AI", "url": null, "code": null, "project": null, "topics": [ "tactile", "hardware" ], "tags": [ "real", "sensor", "magnetic-skin" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A replaceable magnetic tactile skin whose learned policies keep working after the skin is swapped.", "takeaway_zh": "可更换的磁性触觉皮肤，更换皮肤后训练好的策略仍能继续使用。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2409.08276.html", "labs": [ "meta", "nyu" ], "code_license": null }, { "id": "arxiv-2111.00071", "title": "ReSkin: versatile, replaceable, lasting tactile skins", "type": "paper", "arxiv": "2111.00071", "year": 2021, "venue": null, "org": "CMU, Meta AI", "url": null, "code": "https://github.com/raunaqbhirangi/reskin_sensor", "project": null, "topics": [ "tactile", "hardware" ], "tags": [ "sensor", "magnetic-skin", "low-cost" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A thin magnetic-elastomer skin read by magnetometers; cheap to make and swap, with learned calibration that carries over to new skins.", "takeaway_zh": "由磁力计读取的薄磁性弹性体皮肤，制作和更换成本低，学习得到的标定可迁移到新皮肤上。", "github_stars": 139, "page": "https://embodied-ai-radar.com/e/arxiv-2111.00071.html", "labs": [ "meta", "cmu" ], "code_license": "MIT" }, { "id": "arxiv-2211.12498", "title": "Touch and Go: Learning from Human-Collected Vision and Touch", "type": "dataset", "arxiv": "2211.12498", "year": 2022, "venue": null, "org": "University of Michigan", "url": null, "code": null, "project": null, "topics": [ "tactile" ], "tags": [ "dataset", "visuotactile", "gelsight" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A dataset of paired video and GelSight touch collected by people probing objects in the wild, for cross-modal tactile learning.", "takeaway_zh": "由人手持 GelSight 在真实场景中触碰物体采集的视觉-触觉配对数据集，用于跨模态触觉学习。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2211.12498.html", "labs": [], "code_license": null }, { "id": "arxiv-2411.02479", "title": "Digitizing Touch with an Artificial Multimodal Fingertip", "type": "paper", "arxiv": "2411.02479", "year": 2024, "venue": null, "org": "Meta FAIR, TU Dresden, UC Berkeley", "url": null, "code": "https://github.com/facebookresearch/digit360", "project": null, "topics": [ "tactile", "hardware" ], "tags": [ "real", "sensor", "vision-based-tactile", "multimodal", "open-source" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Digit 360: an open-design, finger-shaped sensor that senses force, fine texture, vibration and heat, with an on-board neural accelerator for reflex-like local processing.", "takeaway_zh": "Digit 360：开放设计的指尖形触觉传感器，可感知力、细微纹理、振动和温度，并用板载神经网络加速器做类似反射弧的本地处理。", "github_stars": 269, "page": "https://embodied-ai-radar.com/e/arxiv-2411.02479.html", "labs": [ "berkeley", "meta" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2109.04027", "title": "Taxim: An Example-based Simulation Model for GelSight Tactile Sensors", "type": "simulator", "arxiv": "2109.04027", "year": 2021, "venue": null, "org": "CMU", "url": null, "code": "https://github.com/Robo-Touch/Taxim", "project": null, "topics": [ "tactile", "sim" ], "tags": [ "simulator", "gelsight", "vision-based-tactile" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Simulates GelSight images from calibration examples (optical lookup + linear elastic deformation) instead of physics-based rendering, so tactile data can be generated quickly.", "takeaway_zh": "用标定样本（光学查找表 + 线弹性形变）而不是基于物理的渲染来模拟 GelSight 图像，可快速生成触觉数据。", "github_stars": 127, "page": "https://embodied-ai-radar.com/e/arxiv-2109.04027.html", "labs": [ "cmu" ], "code_license": "MIT" }, { "id": "arxiv-2207.10763", "title": "Tactile Gym 2.0: Sim-to-real Deep Reinforcement Learning for Comparing Low-cost High-Resolution Robot Touch", "type": "simulator", "arxiv": "2207.10763", "year": 2022, "venue": null, "org": "University of Bristol", "url": null, "code": "https://github.com/dexterousrobot/tactile_gym", "project": null, "topics": [ "tactile", "sim", "learning" ], "tags": [ "simulator", "sim-to-real", "rl", "tactip", "digit" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Open-source tactile RL environments with sim-to-real transfer across three low-cost optical sensors (TacTip, DIGIT, DigiTac), for comparing them on the same tasks.", "takeaway_zh": "开源触觉强化学习环境，支持三种低成本光学触觉传感器（TacTip、DIGIT、DigiTac）的仿真到真机迁移，便于在相同任务上比较。", "github_stars": 44, "page": "https://embodied-ai-radar.com/e/arxiv-2207.10763.html", "labs": [], "code_license": "GPL-3.0" }, { "id": "arxiv-2103.12269", "title": "GelSlim 3.0: High-Resolution Measurement of Shape, Force and Slip in a Compact Tactile-Sensing Finger", "type": "paper", "arxiv": "2103.12269", "year": 2021, "venue": null, "org": "MIT", "url": null, "code": null, "project": null, "topics": [ "tactile", "hardware" ], "tags": [ "gelsight", "sensor-design", "finger", "force", "slip" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A slim GelSight-style finger that measures contact shape, force and slip at high resolution, designed to fit robot grippers.", "takeaway_zh": "纤薄的 GelSight 类触觉手指，可高分辨率测量接触形状、力与滑移，便于装在机器人夹爪上。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2103.12269.html", "labs": [ "mit" ], "code_license": null }, { "id": "arxiv-2209.13916", "title": "DTact: A Vision-Based Tactile Sensor that Measures High-Resolution 3D Geometry Directly from Darkness", "type": "paper", "arxiv": "2209.13916", "year": 2022, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile", "hardware" ], "tags": [ "sensor-design", "low-cost", "3d-geometry", "open-source" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A low-cost visuotactile sensor that reads contact depth from how dark each pixel gets as the gel is pressed, giving high-resolution 3D geometry without photometric stereo.", "takeaway_zh": "低成本视觉触觉传感器：根据凝胶受压时每个像素变暗的程度直接读出接触深度，无需光度立体即可获得高分辨率三维形状。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2209.13916.html", "labs": [], "code_license": null }, { "id": "arxiv-2308.14277", "title": "9DTact: A Compact Vision-Based Tactile Sensor for Accurate 3D Shape Reconstruction and Generalizable 6D Force Estimation", "type": "paper", "arxiv": "2308.14277", "year": 2023, "venue": null, "org": null, "url": null, "code": "https://github.com/linchangyi1/9DTact", "project": null, "topics": [ "tactile", "hardware" ], "tags": [ "sensor-design", "open-source", "force", "3d-geometry" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Successor to DTact - a compact open-source visuotactile sensor that reconstructs 3D contact shape and estimates 6D force/torque with a model that generalizes to unseen objects.", "takeaway_zh": "DTact 的后续：紧凑的开源视觉触觉传感器，可重建三维接触形状，并用可泛化到新物体的模型估计六维力/力矩。", "github_stars": 196, "page": "https://embodied-ai-radar.com/e/arxiv-2308.14277.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-1810.10191", "title": "Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks", "type": "paper", "arxiv": "1810.10191", "year": 2018, "venue": "ICRA 2019", "org": "Stanford", "url": null, "code": "https://github.com/stanford-iprl-lab/multimodal_representation", "project": null, "topics": [ "tactile", "manipulation", "learning" ], "tags": [ "real", "visuotactile", "force-torque", "representation", "rl" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Learns a compact vision + force/torque representation with self-supervised objectives, then trains a peg-insertion policy with RL on top of it on a real robot.", "takeaway_zh": "用自监督目标学习视觉 + 力/力矩的紧凑联合表征，再在其上用强化学习在真实机器人上训练插孔策略。", "github_stars": 103, "page": "https://embodied-ai-radar.com/e/arxiv-1810.10191.html", "labs": [ "stanford" ], "code_license": "MIT" }, { "id": "arxiv-2212.03858", "title": "See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation", "type": "paper", "arxiv": "2212.03858", "year": 2022, "venue": "CoRL 2022", "org": "Stanford", "url": null, "code": "https://github.com/JunzheJosephZhu/see_hear_feel", "project": null, "topics": [ "tactile", "manipulation" ], "tags": [ "real", "visuotactile", "audio", "multimodal", "imitation-learning" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Fuses camera, contact microphone and GelSight touch with self-attention in an imitation-learned policy, studied on dense packing and pouring tasks.", "takeaway_zh": "在模仿学习策略中用自注意力融合相机、接触麦克风和 GelSight 触觉，在密集装箱与倾倒任务上做了研究。", "github_stars": 46, "page": "https://embodied-ai-radar.com/e/arxiv-2212.03858.html", "labs": [ "stanford" ], "code_license": "MIT" }, { "id": "arxiv-2402.13232", "title": "A Touch, Vision, and Language Dataset for Multimodal Alignment", "type": "paper", "arxiv": "2402.13232", "year": 2024, "venue": "ICML 2024", "org": "UC Berkeley", "url": null, "code": "https://github.com/Max-Fu/tvl", "project": null, "topics": [ "tactile", "datasets" ], "tags": [ "dataset", "vision-based-tactile", "language", "representation" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "TVL pairs tactile and visual observations with language descriptions and trains a touch-vision-language encoder plus a model that describes how things feel.", "takeaway_zh": "TVL 数据集将触觉与视觉观测配上语言描述，并训练触觉-视觉-语言编码器以及能描述物体触感的模型。", "github_stars": 102, "page": "https://embodied-ai-radar.com/e/arxiv-2402.13232.html", "labs": [ "berkeley" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2309.09979", "title": "General In-Hand Object Rotation with Vision and Touch", "type": "paper", "arxiv": "2309.09979", "year": 2023, "venue": "CoRL 2023", "org": "UC Berkeley, Meta AI", "url": null, "code": null, "project": null, "topics": [ "tactile", "manipulation" ], "tags": [ "dexterous-hand", "visuotactile", "sim2real", "rl" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "RotateIt trains in simulation with privileged object information, then distills to a policy that rotates diverse objects in hand using vision, touch and proprioception on a real hand.", "takeaway_zh": "RotateIt 先在仿真中利用特权物体信息训练，再蒸馏为仅用视觉、触觉和本体感觉的策略，在真实灵巧手上旋转多种物体。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2309.09979.html", "labs": [ "berkeley", "meta" ], "code_license": null }, { "id": "arxiv-2408.06506", "title": "TacSL: A Library for Visuotactile Sensor Simulation and Learning", "type": "paper", "arxiv": "2408.06506", "year": 2024, "venue": null, "org": "NVIDIA", "url": null, "code": null, "project": null, "topics": [ "tactile", "sim" ], "tags": [ "sim", "gpu", "vision-based-tactile", "sim2real" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "GPU-based simulation of GelSight-style tactile images and contact forces in Isaac Gym, plus learning tools for sim-to-real transfer of visuotactile policies.", "takeaway_zh": "在 Isaac Gym 中用 GPU 模拟 GelSight 式触觉图像与接触力，并提供用于视触觉策略仿真到真实迁移的学习工具。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2408.06506.html", "labs": [ "nvidia" ], "code_license": null }, { "id": "arxiv-2303.10880", "title": "Rotating without Seeing: Towards In-hand Dexterity through Touch", "type": "paper", "arxiv": "2303.10880", "year": 2023, "venue": "RSS 2023", "org": "UC San Diego", "url": null, "code": "https://github.com/YingYuan0414/in-hand-rotation", "project": "https://touchdexterity.github.io", "topics": [ "tactile", "manipulation" ], "tags": [ "dexterous-hand", "binary-touch", "low-cost", "sim2real", "rl" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Covers a robot hand with cheap binary (contact / no contact) touch sensors and trains an RL policy in simulation that rotates unseen objects in hand on a real robot without any vision.", "takeaway_zh": "在灵巧手上铺设廉价的二值（有/无接触）触觉传感器，在仿真中用强化学习训练策略，迁移到真机后无需视觉即可在手中旋转未见过的物体。", "github_stars": 114, "page": "https://embodied-ai-radar.com/e/arxiv-2303.10880.html", "labs": [ "ucsd" ], "code_license": "MIT" }, { "id": "arxiv-2312.01853", "title": "Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing", "type": "paper", "arxiv": "2312.01853", "year": 2023, "venue": null, "org": "UC San Diego", "url": null, "code": "https://github.com/YingYuan0414/in-hand-rotation", "project": "https://yingyuan0414.github.io/visuotactile/", "topics": [ "tactile", "manipulation" ], "tags": [ "dexterous-hand", "visuotactile", "point-cloud", "sim2real", "rl" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Turns tactile readings into points in the same 3D frame as the camera point cloud and the hand model, so one visuotactile point-cloud input drives in-hand object rotation.", "takeaway_zh": "将触觉读数转换为与相机点云、手部模型处于同一三维坐标系的点，用统一的视触觉点云输入驱动手内物体旋转。", "github_stars": 114, "page": "https://embodied-ai-radar.com/e/arxiv-2312.01853.html", "labs": [ "ucsd" ], "code_license": "MIT" }, { "id": "arxiv-2312.13469", "title": "Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation", "type": "paper", "arxiv": "2312.13469", "year": 2023, "venue": "Science Robotics 2024", "org": "Meta AI, CMU", "url": null, "code": "https://github.com/facebookresearch/neuralfeels", "project": null, "topics": [ "tactile", "perception", "manipulation" ], "tags": [ "visuotactile", "neural-fields", "pose-tracking", "dexterous-hand" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "NeuralFeels fuses vision and touch from a multi-fingered hand into an online neural field, reconstructing an object's shape and tracking its pose while the hand manipulates it.", "takeaway_zh": "NeuralFeels 将多指手的视觉与触觉在线融合为神经场，在操作过程中同时重建物体形状并跟踪其位姿。", "github_stars": 163, "page": "https://embodied-ai-radar.com/e/arxiv-2312.13469.html", "labs": [ "meta", "cmu" ], "code_license": "MIT" }, { "id": "arxiv-2503.02881", "title": "Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation", "type": "paper", "arxiv": "2503.02881", "year": 2025, "venue": "RSS 2025", "org": "SJTU, Tsinghua", "url": null, "code": "https://github.com/xiaoxiaoxh/reactive_diffusion_policy", "project": "https://reactive-diffusion-policy.github.io/", "topics": [ "tactile", "manipulation", "learning" ], "tags": [ "visuotactile", "force", "diffusion-policy", "teleoperation", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A slow latent diffusion policy plans action chunks while a fast policy reacts to tactile/force feedback; reports >35% higher scores than visual imitation baselines on three contact-rich tasks.", "takeaway_zh": "慢速扩散策略在隐空间规划动作块，快速策略根据触觉/力反馈高频调整；论文报告在三个接触丰富任务上比纯视觉模仿学习基线总分高 35% 以上。", "github_stars": 396, "page": "https://embodied-ai-radar.com/e/arxiv-2503.02881.html", "labs": [ "tsinghua", "sjtu" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.31661", "title": "OpenTac: An Open Ecosystem for Customizable, Scalable, and Transferable 3D Tactile Sensing", "type": "paper", "arxiv": "2610.31661", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/opentac/opentac", "project": "https://opentac.github.io", "topics": [ "tactile", "hardware" ], "tags": [ "tactile-skin", "shear-sensing", "open-source-hardware", "design-tool" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-23", "takeaway_en": "Open-source resistive tactile skin whose structured overlay turns shear into pressure patterns, so neighbouring taxels decode shear and normal force; ships with a design tool for custom skins plus hardware and software.", "takeaway_zh": "开源电阻式触觉皮肤：结构化覆层把切向力转成空间压力差异，相邻触点即可解码剪切与法向力；附带可定制皮肤的设计工具，硬件和软件均开源。", "github_stars": 95, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31661.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.31334", "title": "GelSlim 5.0: Unifying Vision and Touch with a Single Fingertip Camera", "type": "paper", "arxiv": "2610.31334", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile", "perception" ], "tags": [ "visuotactile", "gelsight", "fingertip-sensor", "multimodal-transformer" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-23", "takeaway_en": "Fingertip sensor whose one camera switches illumination to alternate between see-through-membrane vision and tactile images — one shared viewpoint, no extrinsic calibration; fused by a transformer (VisTacFusion).", "takeaway_zh": "指尖传感器只用一个摄像头，通过切换照明交替获取“透过膜的视觉图像”和触觉图像，视觉与触觉共享同一视角、无需相机外参标定；再用 Transformer（VisTacFusion）融合两者。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31334.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.30438", "title": "Tactile-Based Robotic Manipulation: From Mechanics-Driven to Data-Driven Perspectives", "type": "paper", "arxiv": "2610.30438", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile", "manipulation" ], "tags": [ "survey", "tactile-manipulation", "contact-mechanics", "imitation", "rl", "vla" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Review (Annual Review of Control, Robotics, and Autonomous Systems) tracing tactile manipulation from contact-mechanics models to learned policies: sensing basics, state estimation, IL, RL, VLAs, predictive models.", "takeaway_zh": "综述（Annual Review of Control, Robotics, and Autonomous Systems 录用稿）：从接触力学建模到数据驱动学习，梳理触觉操作的发展，涵盖传感基础、状态表征、模仿学习、强化学习、VLA 与预测模型。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.30438.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.31472", "title": "VecTile: A Low-Cost Flexible Tactile Array for 3-DoF Force Sensing", "type": "paper", "arxiv": "2610.31472", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://jiannnh.github.io/VecTile/", "topics": [ "tactile", "hardware" ], "tags": [ "tactile-skin", "shear-sensing", "low-cost", "piezoresistive", "diffusion-policy" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "6 mm flexible array: silicone nubs over a piezoresistive layer turn shear into readable pressure patterns, giving 3-axis force per taxel; under 35 USD for 4x4. Reports it helps a Diffusion Policy insert a plug.", "takeaway_zh": "6 毫米厚的柔性触觉阵列：压阻层上的硅胶凸点把切向力转成可区分的压力分布，实现每个触点的三轴力测量；4x4 阵列成本不到 35 美元。报告称其提升了扩散策略插插头任务的表现。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31472.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.24285", "title": "ACTS: Learning Visuo-Tactile World Models from Human Physical Interaction", "type": "paper", "arxiv": "2610.24285", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://acts-anon.github.io/", "topics": [ "tactile", "world-models" ], "tags": [ "gelsight", "human-data", "world-model", "bimanual", "dataset" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Visuo-tactile world model trained only on humans handling GelSight sensors (4.9 h React dataset). Reports that contact dynamics transfer to robot rollouts more reliably than visual appearance does.", "takeaway_zh": "只用人手持 GelSight 传感器的交互数据（4.9 小时 React 数据集）训练视触觉世界模型。报告在机器人回放中接触动态比视觉外观更容易迁移，部分任务的相机视角预测甚至不如直接复制帧。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.24285.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.14156", "title": "Visible Touch: Rendering Contact for Visuomotor Policies", "type": "paper", "arxiv": "2609.14156", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/qaz1678249/visibletouch", "project": "https://visibletouch.github.io/", "topics": [ "tactile" ], "tags": [ "contact", "visuomotor", "vla" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Brings contact into image-conditioned policies by rendering it, targeting the gap left by approaches that need tactile hardware, separate tactile encoders or non-image backbones.", "takeaway_zh": "CoRL 2026 Spotlight。通过渲染接触信息把触觉带入基于图像的视觉运动策略，针对现有方法需要专用触觉硬件、独立触觉编码器或非图像主干，难以与预训练 2D 视觉表征策略兼容的问题。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2609.14156.html", "labs": [], "code_license": "" }, { "id": "arxiv-2606.12406", "title": "FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning", "type": "paper", "arxiv": "2606.12406", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/philiphan0109/factr2_next", "project": "https://jasonjzliu.com/factr2", "topics": [ "tactile" ], "tags": [ "force-sensing", "teleoperation", "low-cost-arms" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. NEXT estimates external joint torques without force sensors, training in 1 minute from 10 minutes of free-motion data, enabling force-feedback teleoperation and better policy learning on low-cost arms.", "takeaway_zh": "CoRL 2026 Oral。NEXT 无需力传感器即可估计外部关节力矩，用 10 分钟自由运动数据训练 1 分钟，为低成本机械臂带来力反馈遥操作与更好的策略学习。", "github_stars": 33, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12406.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.16308", "title": "PACE: Temporal-Role Tactile Conditioning for Chunk-Based Insertion Policies", "type": "paper", "arxiv": "2610.16308", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://pace-insertion.github.io/", "topics": [ "tactile" ], "tags": [ "visuotactile", "insertion", "contact-rich" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. Splits one tactile stream into three temporal roles, persistent states, abrupt contact events and future consequences, instead of a single fusion path in chunk-based insertion policies.", "takeaway_zh": "CoRL 2026 Oral。不再用单一融合路径，而是把同一触觉流拆成三种时序角色：持续接触状态、突发接触事件与未来接触后果，用于分块式插接策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.16308.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.28959", "title": "TactileStep: Sole Tactile Learning for Regulating Foot-Terrain Interaction in Humanoid Locomotion", "type": "paper", "arxiv": "2609.28959", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile" ], "tags": [ "humanoid", "sole-pressure", "locomotion" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Brings sole pressure sensing into humanoid locomotion control, aiming for softer foot-terrain contact than parkour policies that succeed despite harsh landings.", "takeaway_zh": "CoRL 2026 Spotlight。把足底压力感知引入人形运动控制，目标是比“能完成但落地粗暴”的跑酷策略更柔和的足地接触。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.28959.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.20689", "title": "FingerEye: Learning Dexterous Manipulation with Continuous Vision-Tactile Sensing", "type": "paper", "arxiv": "2604.20689", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/ariszxxu/FingerEye", "project": "https://nus-lins-lab.github.io/FingerEyeWeb/", "topics": [ "tactile" ], "tags": [ "vision-tactile", "dexterous", "fingertip" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Fingertip sensing that combines binocular RGB cameras with a compliant contact surface, giving feedback from pre-contact approach through post-contact control.", "takeaway_zh": "CoRL 2026 Spotlight。指尖传感将双目 RGB 相机与柔顺接触面结合，从接触前接近到接触后控制全程提供反馈。", "github_stars": 24, "page": "https://embodied-ai-radar.com/e/arxiv-2604.20689.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.16463", "title": "DeTac: Dual-branch Decoupling of Texture and Force Signals in Vision-Based Tactile Sensors", "type": "paper", "arxiv": "2610.16463", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile" ], "tags": [ "vision-based-tactile", "force", "generative" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Dual-branch generative framework that extracts clean shading and marker images at once from one marker-based tactile frame, avoiding serial mask-based inpainting.", "takeaway_zh": "CoRL 2026 Spotlight。双分支生成框架，可从单帧带标记点的触觉图像同时分离出干净的明暗图与标记图，避免串行掩码修复。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.16463.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.03520", "title": "PanoTac: A Dense Three-Axis Tactile Sensor for Robust Touch-Centric Manipulation", "type": "paper", "arxiv": "2611.03520", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://panotac.github.io", "topics": [ "tactile", "hardware", "manipulation" ], "tags": [ "sensor", "low-cost", "shear", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "A compliant fingertip under $50 gives a dense 12×32 normal and shear map at 60 Hz. Visuo-tactile policies beat vision-only baselines by 46.7 points on average across scooping, pouring and test-tube tasks.", "takeaway_zh": "每个成本低于 50 美元的柔顺指尖，以 60 Hz 输出 12×32 的法向与剪切稠密响应。在舀取、倾倒和试管任务上，视触觉策略平均比纯视觉基线高 46.7 个百分点。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03520.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.00455", "title": "TacMind: Benchmarking and Modeling Tactile Interaction States for Robotic Manipulation", "type": "paper", "arxiv": "2611.00455", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Tacmind-Bench/TacMind", "project": "https://tacmind-bench.github.io/", "topics": [ "tactile", "datasets", "world-models" ], "tags": [ "benchmark", "sim", "tactile-world-model" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "128 simulated tactile tasks in three regimes (Precise, Recognition, Dynamic) with 12 perturbations, plus a tactile world model that treats touch as an evolving state and refines actions with predicted contact.", "takeaway_zh": "128 个仿真触觉任务，分精确、识别、动态三类，含 12 种扰动；并提出动作条件的触觉世界模型，把触觉当作随时间演化的状态，用预测的触觉信息修正动作。", "github_stars": 7, "page": "https://embodied-ai-radar.com/e/arxiv-2611.00455.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.00957", "title": "Evaluating Tactile Foundation Models for End-to-End Imitation Learning in Contact-Rich Manipulation", "type": "paper", "arxiv": "2611.00957", "year": 2026, "venue": "SII 2027", "org": null, "url": null, "code": "https://github.com/isri-aist/TacLatentIL", "project": "https://mmurooka.github.io/tfm4il-project-page/", "topics": [ "tactile", "manipulation", "learning" ], "tags": [ "benchmark", "diffusion-policy", "tactile", "real", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Plugs several pretrained tactile foundation models into Diffusion Policy and compares them in one setup, including fine-tuning and mid-training strategies, with over 5,000 real-world rollouts.", "takeaway_zh": "把多种预训练触觉基础模型接入 Diffusion Policy，在统一设置下比较，并考察编码器微调与任务专用中训练策略，含 5000 多次真实机器人回合。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2611.00957.html", "labs": [], "code_license": "BSD-2-Clause" }, { "id": "arxiv-2611.01483", "title": "$TF^2$-Steering: Action Refinement with Tactile Forecasts and Feedback via Velocity-Field Steering", "type": "paper", "arxiv": "2611.01483", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://yiming-cc.github.io/tf2-steering/", "topics": [ "tactile", "manipulation" ], "tags": [ "flow-matching", "residual-policy", "tactile", "real", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Keeps a vision-based flow-matching policy and learns to steer its velocity field from tactile forecasts and live feedback, so touch can correct actions. Reports gains over baselines in six sim and six real tasks.", "takeaway_zh": "保留视觉流匹配策略，用触觉预测与实时反馈学习对其速度场的残差引导，让触觉能修正动作。报告在六个仿真和六个真实任务上优于基线。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.01483.html", "labs": [], "code_license": null }, { "id": "arxiv-2406.19464", "title": "ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data", "type": "paper", "arxiv": "2406.19464", "year": 2024, "venue": "CoRL 2024", "org": "Stanford", "url": null, "code": "https://github.com/real-stanford/maniwav", "project": null, "topics": [ "tactile", "manipulation" ], "tags": [ "audio", "human-demonstrations", "real", "contact-rich" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "An 'ear-in-hand' gripper records audio with vision during human demos in the wild; contact sound then helps policies when vision alone is ambiguous.", "takeaway_zh": "“手上长耳朵”：夹爪在野外人类演示时同步录制音频与视觉；当视觉信息不足时，接触声帮助策略完成接触密集操作。", "github_stars": 68, "page": "https://embodied-ai-radar.com/e/arxiv-2406.19464.html", "labs": [ "stanford" ], "code_license": "MIT" }, { "id": "arxiv-2502.17432", "title": "FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning", "type": "paper", "arxiv": "2502.17432", "year": 2025, "venue": "RSS 2025", "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile", "manipulation" ], "tags": [ "force-feedback", "teleoperation", "real", "contact-rich" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Low-cost bilateral teleop relays arm forces to the operator, and a curriculum corrupts vision less and less over training so policies learn to attend to force.", "takeaway_zh": "低成本双边遥操作把机械臂受力反馈给操作者；训练时对视觉的破坏逐渐减弱，让策略学会关注力信息。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2502.17432.html", "labs": [], "code_license": null }, { "id": "arxiv-2502.12191", "title": "AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors", "type": "paper", "arxiv": "2502.12191", "year": 2025, "venue": "ICLR 2025", "org": null, "url": null, "code": "https://github.com/GeWu-Lab/AnyTouch", "project": null, "topics": [ "tactile", "perception" ], "tags": [ "representation", "vision-based-tactile", "dataset" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Learns one static-and-dynamic representation across different visuo-tactile sensors, and contributes TacQuad, an aligned multi-sensor tactile dataset.", "takeaway_zh": "跨多种视触觉传感器学习统一的静态-动态表征，并贡献对齐的多传感器触觉数据集 TacQuad。", "github_stars": 97, "page": "https://embodied-ai-radar.com/e/arxiv-2502.12191.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2407.01418", "title": "RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing", "type": "paper", "arxiv": "2407.01418", "year": 2024, "venue": "RSS 2024", "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile", "manipulation", "world-models" ], "tags": [ "dynamics-model", "graph-network", "mpc", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A recurrent graph network estimates object state and latent physics from visuo-tactile history; the learned dynamics model drives MPC for tasks like dense packing.", "takeaway_zh": "循环图网络从视触觉历史估计物体状态与潜在物理属性，学到的动力学模型用于 MPC，完成密集装箱等任务。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2407.01418.html", "labs": [], "code_license": null }, { "id": "arxiv-2405.02794", "title": "Octopi: Object Property Reasoning with Large Tactile-Language Models", "type": "paper", "arxiv": "2405.02794", "year": 2024, "venue": "RSS 2024", "org": null, "url": null, "code": "https://github.com/clear-nus/octopi", "project": null, "topics": [ "tactile", "vla" ], "tags": [ "tactile-language", "dataset", "reasoning" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Combines tactile perception with language so an embodied system can reason about physical properties learned by touch; contributes the PhysiCLeAR dataset.", "takeaway_zh": "把触觉感知与语言结合，让具身系统对触摸获得的物理属性做常识推理；贡献 PhysiCLeAR 数据集。", "github_stars": 78, "page": "https://embodied-ai-radar.com/e/arxiv-2405.02794.html", "labs": [], "code_license": "" }, { "id": "arxiv-2405.04534", "title": "Tactile-Augmented Radiance Fields", "type": "paper", "arxiv": "2405.04534", "year": 2024, "venue": "CVPR 2024", "org": null, "url": null, "code": "https://github.com/dou-yiming/TaRF", "project": null, "topics": [ "tactile", "perception" ], "tags": [ "radiance-field", "vision-based-tactile", "diffusion" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "TaRF puts vision and touch in one 3D space: touch probes are registered to photos, and a conditional diffusion model estimates touch at unprobed positions.", "takeaway_zh": "TaRF 把视觉与触觉放进同一三维空间：将触摸采样配准到照片，并用条件扩散模型估计未采样位置的触觉。", "github_stars": 69, "page": "https://embodied-ai-radar.com/e/arxiv-2405.04534.html", "labs": [], "code_license": "" }, { "id": "arxiv-2505.23175", "title": "LocoTouch: Learning Dynamic Quadrupedal Transport with Tactile Sensing", "type": "paper", "arxiv": "2505.23175", "year": 2025, "venue": "CoRL 2025", "org": null, "url": null, "code": "https://github.com/linchangyi1/LocoTouch", "project": null, "topics": [ "tactile", "locomotion" ], "tags": [ "quadruped", "sim-to-real", "tactile" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A high-density tactile sensor across a quadruped's back lets it carry unsecured cylinders over long distances, a task that usually needs mounting hardware.", "takeaway_zh": "在四足机器人背部铺设高密度触觉传感器，无需固定装置即可长距离运输未固定的圆柱物体。", "github_stars": 62, "page": "https://embodied-ai-radar.com/e/arxiv-2505.23175.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2310.16917", "title": "MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation", "type": "paper", "arxiv": "2310.16917", "year": 2023, "venue": "CoRL 2024", "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile", "manipulation" ], "tags": [ "human-demonstrations", "contact-rich", "tactile" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Learns tactile policies from demonstrations given with human hands, closing the gap of teleop where operators steer by sight while the policy needs touch.", "takeaway_zh": "从人手直接演示学习触觉策略，弥补遥操作中“人靠视觉控制、策略却要用触觉”的落差。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2310.16917.html", "labs": [], "code_license": null }, { "id": "arxiv-2504.16649", "title": "PP-Tac: Paper Picking Using Tactile Feedback in Dexterous Robotic Hands", "type": "paper", "arxiv": "2504.16649", "year": 2025, "venue": "RSS 2025", "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile", "manipulation" ], "tags": [ "dexterous", "deformable", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Picks up paper-like thin objects with a multi-fingered hand carrying high-resolution omnidirectional tactile sensors, tackling state estimation and grasp planning.", "takeaway_zh": "用带高分辨率全向触觉传感器的多指手抓取纸张类薄物体，同时解决状态估计与抓取规划。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2504.16649.html", "labs": [], "code_license": null }, { "id": "arxiv-2312.01236", "title": "Evetac: An Event-based Optical Tactile Sensor for Robotic Manipulation", "type": "hardware", "arxiv": "2312.01236", "year": 2023, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile", "hardware" ], "tags": [ "sensor", "event-camera", "vision-based-tactile" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Replaces the RGB camera of an optical tactile sensor with an event camera; touch processing runs online at 1000 Hz and senses vibrations up to 498 Hz.", "takeaway_zh": "用事件相机取代光学触觉传感器的 RGB 相机；触觉处理以 1000 Hz 在线运行，可感知最高 498 Hz 的振动。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2312.01236.html", "labs": [], "code_license": null }, { "id": "arxiv-2508.11261", "title": "Tactile Robotics: An Outlook", "type": "paper", "arxiv": "2508.11261", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile" ], "tags": [ "survey", "tactile" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A broad outlook on tactile robotics, covering piezoresistive, piezoelectric, capacitive, magnetic and optical sensors and where the field is heading.", "takeaway_zh": "对触觉机器人的综合展望，涵盖压阻、压电、电容、磁性和光学等传感方式及领域走向。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2508.11261.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.04424", "title": "ReflexArc: Bottom-Up Integration of Instantaneous Tactile Reflexes into Whole-Body Humanoid Control", "type": "paper", "arxiv": "2611.04424", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://minusmodulo.github.io/reflexarc-website/", "topics": [ "tactile", "locomotion" ], "tags": [ "real", "humanoid", "g1", "reflex" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Adds a full-body tactile reflex that reaches the motors within 10 ms beneath a learned whole-body policy; on a G1 it cuts first force peak 34.1% versus the same reflex delayed by 20 ms.", "takeaway_zh": "在学习型全身策略之下加入全身触觉反射，10 毫秒内直达电机；在 G1 上，与延迟 20 毫秒的相同反射相比，首次力峰值降低 34.1%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.04424.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.03333", "title": "Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation", "type": "paper", "arxiv": "2610.03333", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Kenn3o3/Vista", "project": "https://vista-paper.github.io/", "topics": [ "tactile", "manipulation" ], "tags": [ "visuotactile", "equivariance", "diffusion-policy", "data-efficiency", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "VISTA: equivariant visuotactile diffusion policy that fuses touch into spherical visual tokens and rotates them with the gripper pose, for more data-efficient contact-rich imitation.", "takeaway_zh": "VISTA：等变视触觉扩散策略，把触觉融合进球面视觉 token 并随末端姿态旋转，提升富接触模仿学习的数据效率。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2610.03333.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2609.40134", "title": "Tactile Curiosity Drives Robot Interaction", "type": "paper", "arxiv": "2609.40134", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/klemensiten/tacex", "project": "https://go.klem.nz/tacex", "topics": [ "tactile", "learning" ], "tags": [ "exploration", "intrinsic-motivation", "tactile", "vla-post-training" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "TacEx points curiosity at touch: it splits model uncertainty by sense so exploration seeks contact, not free-space motion. The collected data trains pick-and-place offline and post-trains VLAs.", "takeaway_zh": "TacEx 让好奇心聚焦触觉：按感知模态分解模型不确定性，使探索去寻找接触而非空中乱动。采集的数据可离线训练抓放策略，也可用于 VLA 后训练。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2609.40134.html", "labs": [], "code_license": "" }, { "id": "arxiv-2604.23272", "title": "Modular Sensory Stream for Integrating Physical Feedback in Vision-Language-Action Models", "type": "paper", "arxiv": "2604.23272", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://jiminlx.github.io/MoSS", "topics": [ "tactile" ], "tags": [ "vla", "force-torque", "multimodal", "fusion", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Adds separate tactile, force and torque streams to a pretrained VLA through joint cross-modal attention, with staged training and an auxiliary task that predicts future physical signals. Real-robot tests.", "takeaway_zh": "为预训练 VLA 增加独立的触觉、力和力矩信号流，通过跨模态联合注意力融合，配合分阶段训练和预测未来物理信号的辅助任务。真机实验验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.23272.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.10093", "title": "UniVTAC: A Unified Simulation Platform for Visuo-Tactile Manipulation Data Generation, Learning, and Benchmarking", "type": "paper", "arxiv": "2602.10093", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/univtac/UniVTAC", "project": "https://univtac.github.io/", "topics": [ "tactile" ], "tags": [ "simulation", "visuotactile", "data-generation", "benchmark", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Simulation platform for visuo-tactile data supporting three common sensors, plus an encoder trained on its synthetic data and an 8-task benchmark. Reports +17.1% on the benchmark and +25% in real tasks.", "takeaway_zh": "视触觉仿真数据合成平台，支持三种常用传感器，并基于合成数据训练视触觉编码器、发布 8 个任务的基准。报告基准成功率提升 17.1%，真机提升 25%。", "github_stars": 193, "page": "https://embodied-ai-radar.com/e/arxiv-2602.10093.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2607.03723", "title": "OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies", "type": "paper", "arxiv": "2607.03723", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://colinyu1.github.io/omnitactune-site/", "topics": [ "tactile" ], "tags": [ "real-world-rl", "residual-policy", "visuotactile", "contact-rich", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Real-world RL that adds a lightweight tactile residual on top of any pretrained visual policy, bootstrapped from the base policy's own rollouts. Reports 5–40% success raised to 85–100% within 40–80 minutes.", "takeaway_zh": "真实世界强化学习：在任意预训练视觉策略之上学习轻量的触觉残差修正，先用基础策略自主执行的数据冷启动。报告四个任务成功率在 40–80 分钟内从 5–40% 提升到 85–100%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.03723.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.04531", "title": "PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation", "type": "paper", "arxiv": "2603.04531", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://akashsharma02.github.io/ptld-website/", "topics": [ "tactile" ], "tags": [ "sim-to-real", "privileged-learning", "in-hand-rotation", "dexterous", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns tactile dexterous skills without tactile simulation: privileged sensors in the real world collect data to distil a tactile state estimator for a sim-trained policy. Reports 182% gain on in-hand rotation.", "takeaway_zh": "无需触觉仿真即可学习触觉灵巧操作：在真实环境中借助特权传感器采集数据，蒸馏出基于触觉的状态估计器，用于仿真训练的策略。报告手内旋转任务提升 182%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.04531.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.18231", "title": "FM-VLA: Force-based Memory for Vision-Language-Action Models in Contact-Rich Manipulation", "type": "paper", "arxiv": "2607.18231", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://qft-333.github.io/FM-VLA-Page/", "topics": [ "tactile" ], "tags": [ "force", "memory", "vla", "non-markovian", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Gives a VLA memory through compact force-history tokens from a pretrained VAE, for tasks like pressing a button a set number of times. Reports over 80% success on three memory-dependent tasks.", "takeaway_zh": "用预训练 VAE 把力信号历史压缩成记忆 token 输入 VLA，解决按指定次数按按钮、擦盘子等依赖记忆的接触任务。报告三个记忆型任务成功率超过 80%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.18231.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.06281", "title": "Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation", "type": "paper", "arxiv": "2606.06281", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://mitas-touch.github.io", "topics": [ "tactile" ], "tags": [ "multi-sensor", "event-based", "gelsight", "flow-matching", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Fuses an RGB camera, a GelSight Mini and a high-rate event-based tactile sensor for a flow-matching policy. Reports 80% average success on five contact-rich tasks vs 35.5% vision-only.", "takeaway_zh": "融合 RGB 相机、GelSight Mini 与高频事件型触觉传感器的多分辨率表示，驱动流匹配策略。报告五个富接触任务平均成功率 80%，纯视觉仅 35.5%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.06281.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.11743", "title": "TacCoRL: Integrating Tactile Feedback into VLA via Simulation", "type": "paper", "arxiv": "2606.11743", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://tac-corl.github.io/", "topics": [ "tactile" ], "tags": [ "vla", "sim-real-cotraining", "rl", "bimanual", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Adds touch to a VLA via sim-real co-training and simulated RL in a real-aligned simulator, learning how contact should change actions near failure. Reports 72.5% vs 50.0% on four bimanual tasks.", "takeaway_zh": "通过仿真-真机协同训练和在对齐真实的模拟器中做强化学习，为 VLA 加入触觉，学习接近失败时触觉应如何调整动作。报告四个双臂任务成功率 72.5%，基线 50.0%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.11743.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.13102", "title": "FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation", "type": "paper", "arxiv": "2606.13102", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/michaelyuancb/ftp1-policy", "project": "https://ftp1-policy.github.io", "topics": [ "tactile" ], "tags": [ "foundation-model", "cross-sensor", "pretraining", "transformer", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Tactile policy pretrained on ~3,000 hours from 26 sources and 21 sensors, mapping image, array and state signals into shared tokens. Reports +17.2% on seen setups and +31% on two unseen sensor setups.", "takeaway_zh": "触觉基础策略：在 26 个数据源、21 种传感器约 3000 小时数据上预训练，把图像型、阵列型与状态型触觉信号映射到统一 token。报告已见配置提升 17.2%，两种未见传感器提升 31%。", "github_stars": 146, "page": "https://embodied-ai-radar.com/e/arxiv-2606.13102.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.13232", "title": "WT-UMI: Tactile-based Whole-Body Manipulation via Force-Supervised Contact-Aware Planning", "type": "paper", "arxiv": "2606.13232", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://wt-umi.github.io/WTUMI/", "topics": [ "tactile" ], "tags": [ "wearable", "whole-body", "humanoid", "admittance-control", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Wearable whole-body tactile interface for both human demos and humanoid teleop, with a planner that predicts poses plus contact forces for a tactile admittance controller. Five contact-rich tasks.", "takeaway_zh": "可穿戴的全身触觉接口，可用于人类演示和人形遥操作；规划器同时预测末端位姿与接触力，作为触觉导纳控制器的参考。在五个富接触任务上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.13232.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.29948", "title": "Heterogeneous Tactile Transformer", "type": "paper", "arxiv": "2606.29948", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/jxbi1010/HTT", "project": "https://jxbi1010.github.io/htt-gh-page/", "topics": [ "tactile" ], "tags": [ "representation-learning", "cross-sensor", "pretraining", "dataset", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Shared tactile representation across sensors: sensor-specific encoders plus a common transformer, pretrained on a new 1.6M-frame paired dataset from four sensors; adapts to new tasks and unseen sensors.", "takeaway_zh": "跨传感器的统一触觉表征：各传感器独立编码器加共享 Transformer 主干，在新的 160 万帧四传感器配对数据集上预训练，可迁移到新任务和未见传感器。", "github_stars": 8, "page": "https://embodied-ai-radar.com/e/arxiv-2606.29948.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2606.14981", "title": "Inference-time Policy Steering via Vision and Touch", "type": "paper", "arxiv": "2606.14981", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/yilin-wu98/vital", "project": "https://yilin-wu98.github.io/vital_website", "topics": [ "tactile" ], "tags": [ "inference-time-steering", "visuotactile", "world-model", "diffusion", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Inference-time steering with vision and touch: visual sampling picks the behaviour, tactile-guided diffusion editing fixes local contact, scored by a visuo-tactile latent world model. Reports +51% over the base policy.", "takeaway_zh": "视觉+触觉的推理时策略引导：视觉采样验证决定执行何种行为，触觉引导的扩散编辑修正局部接触，并由视触觉潜在世界模型打分。报告比基础策略提升 51%。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2606.14981.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2606.30988", "title": "Multisensory Continual Learning: Adapting Pretrained Visuomotor Policies to Force", "type": "paper", "arxiv": "2606.30988", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://jadenvc.github.io/multisensory-continual-learning/", "topics": [ "tactile" ], "tags": [ "continual-learning", "force-torque", "world-action-model", "fine-tuning", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Adds force-torque sensing to a pretrained vision-only world-action model with staged fusion, multisensory future prediction and replay of pretraining data; strong on contact tasks without losing original skills.", "takeaway_zh": "通过分阶段融合、多感官未来预测和预训练数据回放，把力/力矩传感加入预训练的纯视觉世界动作模型；在富接触任务上表现好，同时保住甚至提升原有任务性能。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.30988.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.26093", "title": "ForceBand: Learning Forceful Manipulation with sEMG", "type": "paper", "arxiv": "2606.26093", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Bottle101/ForceBand", "project": "https://forceband-emg.github.io", "topics": [ "tactile" ], "tags": [ "semg", "force", "human-demos", "wearable", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Low-cost wrist sEMG band whose model predicts per-finger forces, turning video demos into force-labelled ones after a short calibration. Reports >50% lower force error than vision and 87% on force-sensitive tasks.", "takeaway_zh": "低成本腕戴式肌电（sEMG）设备，经短暂校准即可预测每根手指的力，把视频演示变成带力标注的演示。报告力预测误差比视觉方法低 50% 以上，力敏感任务成功率 87%。", "github_stars": 94, "page": "https://embodied-ai-radar.com/e/arxiv-2606.26093.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.17055", "title": "T-Rex: Tactile-Reactive Dexterous Manipulation", "type": "paper", "arxiv": "2606.17055", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/ZhuoyangLiu2005/T-Rex", "project": "https://tactile-rex.github.io/", "topics": [ "tactile" ], "tags": [ "vla", "dataset", "mixture-of-transformers", "high-frequency", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Tactile-reactive VLA: 100-hour tactile dataset built around motor primitives, a variable-rate mixture-of-transformers and a temporal tactile VQ-VAE. Reports 30%+ higher average success than the best baseline on 12 tasks.", "takeaway_zh": "触觉反应式 VLA：围绕基本动作采集的 100 小时触觉数据集、可变频率的 Mixture-of-Transformers 架构与时序触觉 VQ-VAE 编码器。报告在 12 个任务上平均成功率比最强基线高 30% 以上。", "github_stars": 352, "page": "https://embodied-ai-radar.com/e/arxiv-2606.17055.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2609.25887", "title": "What is the Better Curriculum: Controller-Shaped Grasping Behavior for Contact Force-Sensitive Manipulation", "type": "paper", "arxiv": "2609.25887", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://shayfeng.github.io/better-curriculum/", "topics": [ "tactile" ], "tags": [ "fragile-objects", "data-collection", "reflex-controller", "act", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Uses a 25 Hz tactile reflex controller as a teacher during data collection so demos carry well-regulated grasp forces; tactile-free ACT then reaches 95% stable grasps on fragile cups. Disturbances still need the reflex.", "takeaway_zh": "数据采集时用 25 Hz 触觉反射控制器当“老师”，让演示自带良好的抓握力；之后不用触觉的 ACT 策略在易碎杯子上达到 95% 稳定抓取。但抵抗外部扰动仍需保留反射控制器。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.25887.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.33235", "title": "Learning with Object-centric Representations of Tactile Interactive Perception for Robot Manipulation", "type": "paper", "arxiv": "2609.33235", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://xinyiyxyx.github.io/tactile-object-centric/", "topics": [ "tactile" ], "tags": [ "interactive-perception", "object-properties", "contrastive", "context", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns object representations from tactile exploration, aligned with text descriptions of properties, and uses them as context for manipulation. Reports task success from 41% to 92% (seen) and 19% to 67% (unseen).", "takeaway_zh": "通过触觉探索学习物体表征，并与物理属性的文本描述对比对齐，再作为语境指导操作策略。报告任务成功率在已见物体上从 41% 升到 92%，未见物体从 19% 升到 67%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.33235.html", "labs": [], "code_license": null }, { "id": "arxiv-2505.20498", "title": "ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generation", "type": "paper", "arxiv": "2505.20498", "year": 2025, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/DongyuLUO/ControlTac", "project": "https://dongyuluo.github.io/controltac", "topics": [ "tactile" ], "tags": [ "tactile-generation", "data-augmentation", "vision-based-tactile", "insertion", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Generates realistic tactile images from one reference image plus contact force and pose, to scale tactile datasets across sensors. Augmented data improves insertion, imitation learning and weighing in real tests.", "takeaway_zh": "以一张参考触觉图像加接触力与接触位姿为条件，生成逼真的触觉图像来扩充跨传感器的触觉数据。在插装、模仿学习与称重等真实任务中，增强数据均带来提升。", "github_stars": 5, "page": "https://embodied-ai-radar.com/e/arxiv-2505.20498.html", "labs": [], "code_license": "" }, { "id": "arxiv-2606.31236", "title": "TactX: Learning Shared Tactile Representations Across Diverse Sensors", "type": "paper", "arxiv": "2606.31236", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile" ], "tags": [ "tactile", "cross-sensor", "representation", "zero-shot-transfer" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Encoders for resistive, magnetic and vision-based tactile sensors are trained on paired contacts into one latent space, so a policy trained with one sensor transfers zero-shot to another on four manipulation tasks.", "takeaway_zh": "为电阻式、磁式和视觉触觉传感器分别训练编码器，利用成对接触数据映射到同一潜空间；在四个操作任务中，用一种传感器训练的策略可零样本迁移到另一种。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.31236.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.14862", "title": "TacStyle: Personalizing Tactile Robot Policies using Structured Behavior Representations", "type": "paper", "arxiv": "2606.14862", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile" ], "tags": [ "tactile", "personalization", "language", "force" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns a structured latent space of behaviour styles (e.g. how much force to apply), and lets language reason over it instead of generating actions directly, for personalized tactile policies like wiping or folding.", "takeaway_zh": "学习按行为风格（如施力大小）组织的结构化潜空间，让语言在其中推理用户偏好而非直接生成动作，从而实现个性化的触觉策略，如擦拭、叠衣。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.14862.html", "labs": [], "code_license": null }, { "id": "arxiv-2512.23864", "title": "Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation", "type": "paper", "arxiv": "2512.23864", "year": 2025, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile" ], "tags": [ "tactile", "vla", "world-model", "contact-rich" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "DreamTacVLA feeds high-res tactile images with wrist and third-person views, aligns them spatially, then fine-tunes with a tactile world model that predicts future touch signals.", "takeaway_zh": "DreamTacVLA 把高分辨率触觉图像与腕部、第三人称视角一起输入并做空间对齐，再用预测未来触觉信号的触觉世界模型微调。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2512.23864.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.11767", "title": "Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning", "type": "paper", "arxiv": "2606.11767", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile" ], "tags": [ "tactile", "dexterous-grasping", "real2sim2real", "diffusion-policy", "leap-hand" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Tactile-only blind grasping on a LEAP Hand: calibrate a digital twin to reproduce real touch signals, pretrain a layout-aware tactile encoder, and distill object-specific RL experts into a diffusion policy.", "takeaway_zh": "在 LEAP 手上实现只凭触觉的盲抓：先构建能复现真实触觉信号的校准数字孪生，再自监督预训练感知传感器布局的触觉编码器，最后把各物体的强化学习专家蒸馏为扩散策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.11767.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.11221", "title": "How Firm Should a Grasp Be?", "type": "paper", "arxiv": "2610.11221", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile" ], "tags": [ "visuotactile", "grasp-force", "material-properties", "dataset" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Estimates mass, elasticity and friction visuotactilely during the grasp and applies the minimum safe grip force; adds a dataset of fruits and vegetables with measured physical properties.", "takeaway_zh": "在抓取过程中用视触觉实时估计物体的质量、弹性和摩擦，从而施加刚好足够的最小夹持力；并提供一个测量了物理属性的水果蔬菜数据集。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.11221.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.15921", "title": "Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing", "type": "paper", "arxiv": "2609.15921", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile" ], "tags": [ "tactile", "dexterous", "cable", "imitation-learning" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Touch2Trace feeds a cable through a dexterous hand from touch alone: a self-supervised encoder for a 32x32 piezoresistive sensor plus a small transformer BC policy at 60 Hz; 93% success vs 0% without touch.", "takeaway_zh": "Touch2Trace 仅凭触觉让灵巧手完成线缆穿引：为 32x32 压阻触觉传感器自监督预训练编码器，配合 60 Hz 的轻量 Transformer 行为克隆策略；成功率 93%，无触觉时为 0%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.15921.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.15910", "title": "SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection", "type": "paper", "arxiv": "2609.15910", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "tactile" ], "tags": [ "tactile", "slip-detection", "accelerometer", "multimodal" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "SlipSense fuses a 240 Hz pressure array with an 8 kHz accelerometer for slip detection: 96.7% macro F1, 76% of slips caught within 23.1 ms; trained on UMI data, it transfers to a dexterous hand.", "takeaway_zh": "SlipSense 融合 240 Hz 压力阵列与 8 kHz 加速度计进行滑移检测：宏 F1 达 96.7%，76% 的滑移在 23.1 毫秒内被检测到；仅用 UMI 数据训练即可零样本迁移到灵巧手。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.15910.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.08939", "title": "RevoSim: Scalable Multimodal Tactile Simulation for Dexterous Manipulation", "type": "paper", "arxiv": "2611.08939", "year": 2026, "venue": null, "org": "BrainCo", "url": null, "code": "https://github.com/BrainCoTech/RevoSim", "project": "https://braincotech.github.io/RevoSim-web/", "topics": [ "tactile", "sim", "manipulation" ], "tags": [ "tactile-simulation", "dexterous-hand", "visuotactile", "gpu-parallel" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-09", "takeaway_en": "Tactile simulator for the BrainCo Revo3 hand modelling 285 piezoresistive taxels and five visuotactile fingertips (pressure, depth, RGB, marker motion); up to 4,096 parallel envs on one RTX 5090.", "takeaway_zh": "为 BrainCo Revo3 灵巧手构建的触觉仿真器，同时模拟 285 个压阻触点和五个视触觉指尖（压力、深度、RGB、标记点位移）；单张 RTX 5090 最多 4096 个并行环境。", "github_stars": 6, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08939.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.09293", "title": "MDF++: Visuo-Tactile World Action Model with End-to-end Representation Learning", "type": "paper", "arxiv": "2611.09293", "year": 2026, "venue": "CoRL 2026", "org": "University of Michigan", "url": null, "code": null, "project": "https://jointmdf.github.io/", "topics": [ "tactile", "world-models", "manipulation" ], "tags": [ "world-action-model", "diffusion", "visuo-tactile", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-09", "takeaway_en": "Learns visuo-tactile latents end-to-end inside a diffusion world-action model not frozen encoders; multi-task cross-modal losses avoid feature collapse. Reports up to 50% gains on real bolt screwing and nut mating.", "takeaway_zh": "在扩散式世界-动作模型中端到端学习视触觉潜表示，而非使用冻结的预训练编码器；借助跨模态多任务学习避免特征坍缩。在真实拧螺栓、螺母配合等任务上报告最高 50% 的提升。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.09293.html", "labs": [], "code_license": null }, { "id": "arxiv-2109.11978", "title": "Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning", "type": "paper", "arxiv": "2109.11978", "year": 2021, "venue": null, "org": "ETH Zurich, NVIDIA", "url": null, "code": "https://github.com/leggedrobotics/legged_gym", "project": null, "topics": [ "locomotion", "sim" ], "tags": [ "sim2real", "quadruped", "rl", "gpu-parallel" ], "robot_types": [ "legged" ], "highlight": true, "rating": 3, "added": null, "takeaway_en": "Thousands of parallel GPU environments train a quadruped walking policy in minutes; legged_gym became a standard starter.", "takeaway_zh": "数千个 GPU 并行环境几分钟就能训练出四足行走策略；legged_gym 成为标准入门框架。", "github_stars": 3176, "page": "https://embodied-ai-radar.com/e/arxiv-2109.11978.html", "labs": [ "nvidia", "eth" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2010.11251", "title": "Learning Quadrupedal Locomotion over Challenging Terrain", "type": "paper", "arxiv": "2010.11251", "year": 2020, "venue": null, "org": "ETH Zurich", "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "sim2real", "quadruped", "rl", "proprioception" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A teacher-student, proprioception-only policy that walks ANYmal over mud, snow and rubble in the wild.", "takeaway_zh": "仅依赖本体感知的师生策略，让 ANYmal 在泥地、雪地和碎石等野外地形行走。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2010.11251.html", "labs": [ "eth" ], "code_license": null }, { "id": "arxiv-1901.08652", "title": "Learning Agile and Dynamic Motor Skills for Legged Robots", "type": "paper", "arxiv": "1901.08652", "year": 2019, "venue": null, "org": "ETH Zurich", "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "sim2real", "quadruped", "rl", "actuator-net" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A learned actuator model closes the sim-to-real gap so RL policies run and recover from falls on ANYmal.", "takeaway_zh": "学习的执行器模型缩小仿真与现实差距，使强化学习策略能让 ANYmal 奔跑并摔倒后起身。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1901.08652.html", "labs": [ "eth" ], "code_license": null }, { "id": "arxiv-2107.04034", "title": "RMA: Rapid Motor Adaptation for Legged Robots", "type": "paper", "arxiv": "2107.04034", "year": 2021, "venue": null, "org": "UC Berkeley, CMU, Meta AI", "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "sim2real", "quadruped", "rl", "adaptation" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "An adaptation module infers environment parameters online, so one policy handles new terrains and payloads in real time.", "takeaway_zh": "自适应模块在线估计环境参数，使同一策略实时适应新地形和负载。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2107.04034.html", "labs": [ "berkeley", "meta", "cmu" ], "code_license": null }, { "id": "arxiv-2309.05665", "title": "Robot Parkour Learning", "type": "paper", "arxiv": "2309.05665", "year": 2023, "venue": null, "org": "Shanghai Qi Zhi, Stanford", "url": null, "code": "https://github.com/ZiwenZhuang/parkour", "project": null, "topics": [ "locomotion" ], "tags": [ "sim2real", "quadruped", "rl", "vision" ], "robot_types": [ "legged" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Distills specialist parkour skills into a single vision-based policy that climbs, leaps and crawls on low-cost quadrupeds.", "takeaway_zh": "将多个跑酷专家技能蒸馏为单一视觉策略，让低成本四足机器人攀爬、跳跃和匍匐。", "github_stars": 1143, "page": "https://embodied-ai-radar.com/e/arxiv-2309.05665.html", "labs": [ "stanford", "qizhi" ], "code_license": "MIT" }, { "id": "arxiv-2309.14341", "title": "Extreme Parkour with Legged Robots", "type": "paper", "arxiv": "2309.14341", "year": 2023, "venue": null, "org": "CMU", "url": null, "code": "https://github.com/chengxuxin/extreme-parkour", "project": null, "topics": [ "locomotion" ], "tags": [ "sim2real", "quadruped", "rl", "vision" ], "robot_types": [ "legged" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "An end-to-end depth-camera policy makes a small quadruped do long jumps, high jumps and handstands.", "takeaway_zh": "端到端深度相机策略让小型四足机器人完成跳远、跳高和倒立。", "github_stars": 1194, "page": "https://embodied-ai-radar.com/e/arxiv-2309.14341.html", "labs": [ "cmu" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2303.03381", "title": "Real-World Humanoid Locomotion with Reinforcement Learning", "type": "paper", "arxiv": "2303.03381", "year": 2023, "venue": null, "org": "UC Berkeley", "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "sim2real", "humanoid", "rl", "transformer" ], "robot_types": [ "humanoid" ], "highlight": true, "rating": 2, "added": null, "takeaway_en": "A causal Transformer policy trained in simulation walks a full-size humanoid outdoors, zero-shot.", "takeaway_zh": "在仿真中训练的因果 Transformer 策略零样本驱动全尺寸人形机器人在户外行走。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2303.03381.html", "labs": [ "berkeley" ], "code_license": null }, { "id": "arxiv-2402.16796", "title": "Expressive Whole-Body Control for Humanoid Robots", "type": "paper", "arxiv": "2402.16796", "year": 2024, "venue": null, "org": "UC San Diego", "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "sim2real", "humanoid", "rl", "motion-imitation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Imitates upper-body human motion capture while keeping robust legs, giving humanoids expressive gestures and dance.", "takeaway_zh": "上半身模仿人类动捕、下肢保持鲁棒行走，让人形机器人做出富有表现力的动作和舞蹈。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2402.16796.html", "labs": [ "ucsd" ], "code_license": null }, { "id": "arxiv-2403.04436", "title": "Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation", "type": "paper", "arxiv": "2403.04436", "year": 2024, "venue": null, "org": "CMU", "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "sim2real", "humanoid", "teleoperation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "H2O — an RL tracking policy lets an RGB camera stream a person's whole-body motion onto a humanoid in real time.", "takeaway_zh": "H2O：强化学习跟踪策略借助 RGB 相机，把人的全身动作实时映射到人形机器人。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2403.04436.html", "labs": [ "cmu" ], "code_license": null }, { "id": "arxiv-2406.10454", "title": "HumanPlus: Humanoid Shadowing and Imitation from Humans", "type": "paper", "arxiv": "2406.10454", "year": 2024, "venue": null, "org": "Stanford", "url": null, "code": "https://github.com/MarkFzp/humanplus", "project": null, "topics": [ "locomotion" ], "tags": [ "real", "humanoid", "teleoperation", "imitation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A humanoid shadows a human from one RGB camera, then learns autonomous skills from the collected data.", "takeaway_zh": "人形机器人通过单个 RGB 相机实时模仿人类，再用采集的数据学习自主技能。", "github_stars": 851, "page": "https://embodied-ai-radar.com/e/arxiv-2406.10454.html", "labs": [ "stanford" ], "code_license": "" }, { "id": "arxiv-2502.01143", "title": "ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills", "type": "paper", "arxiv": "2502.01143", "year": 2025, "venue": null, "org": "CMU, NVIDIA", "url": null, "code": "https://github.com/LeCAR-Lab/ASAP", "project": null, "topics": [ "locomotion" ], "tags": [ "sim2real", "humanoid", "rl", "delta-action" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Learns a residual \"delta action\" model from real rollouts to fix sim physics, enabling agile athletic humanoid motions.", "takeaway_zh": "从真实轨迹学习残差\"增量动作\"模型来修正仿真物理，实现敏捷的人形运动技能。", "github_stars": 2144, "page": "https://embodied-ai-radar.com/e/arxiv-2502.01143.html", "labs": [ "nvidia", "cmu" ], "code_license": "MIT" }, { "id": "unitree-rl-gym", "title": "Unitree RL Gym", "type": "code", "arxiv": null, "year": 2024, "venue": null, "org": "Unitree", "url": null, "code": "https://github.com/unitreerobotics/unitree_rl_gym", "project": null, "topics": [ "locomotion", "hardware", "learning" ], "tags": [ "sim2real", "humanoid", "quadruped", "rl", "isaac-gym" ], "robot_types": [ "humanoid", "legged" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Unitree's official starting point for RL locomotion on Go2, H1 and G1 — train in Isaac Gym, check sim-to-sim in MuJoCo, then deploy to the real robot.", "takeaway_zh": "宇树官方的强化学习运动控制入门仓库，支持 Go2、H1、G1：在 Isaac Gym 中训练，在 MuJoCo 中做 sim2sim 验证，再部署到真机。", "github_stars": 3667, "page": "https://embodied-ai-radar.com/e/unitree-rl-gym.html", "labs": [], "code_license": "BSD-3-Clause" }, { "id": "arxiv-2610.28480", "title": "SuperMimic: Scaling Humanoid Whole-Body Control with One Differentiable World Model", "type": "paper", "arxiv": "2610.28480", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://supermimic.github.io", "topics": [ "locomotion", "world-models", "learning" ], "tags": [ "humanoid", "motion-tracking", "whole-body-control", "differentiable-model" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": "2026-10-21", "takeaway_en": "Rather than distilling per-motion experts or brute-forcing one RL policy, trains a humanoid tracking policy jointly with one differentiable world model of shared dynamics, fit from simulator logs (CoRL 2026 oral).", "takeaway_zh": "SuperMimic 不再蒸馏逐动作专家或用大算力硬训单一 RL 策略，而是把人形机器人动作跟踪策略与一个从仿真日志拟合的、共享动力学的可微世界模型联合训练（CoRL 2026 口头报告）。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.28480.html", "labs": [], "code_license": null }, { "id": "arxiv-2201.08117", "title": "Learning robust perceptive locomotion for quadrupedal robots in the wild", "type": "paper", "arxiv": "2201.08117", "year": 2022, "venue": "Science Robotics 2022", "org": "ETH Zurich, Intel", "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "quadruped", "rl", "perception", "real", "sim-to-real" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "ANYmal fuses proprioception with noisy height-map perception through a learned belief encoder, and is shown hiking an alpine trail and other rough terrain.", "takeaway_zh": "ANYmal 通过学习的信念编码器融合本体感知与带噪声的高程图感知，在高山徒步路线等复杂地形上完成行走。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2201.08117.html", "labs": [ "eth" ], "code_license": null }, { "id": "arxiv-2406.08858", "title": "OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning", "type": "paper", "arxiv": "2406.08858", "year": 2024, "venue": "CoRL 2024", "org": "CMU", "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "teleoperation", "whole-body", "rl", "sim-to-real" ], "robot_types": [ "hand", "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "One RL whole-body controller that a humanoid can follow from a VR headset, verbal commands or an RGB camera, and that also runs learned autonomous policies.", "takeaway_zh": "一个强化学习全身控制器：人形机器人可跟随 VR 头显、语音指令或 RGB 相机的输入，也可执行学习得到的自主策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2406.08858.html", "labs": [ "cmu" ], "code_license": null }, { "id": "arxiv-2410.21229", "title": "HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots", "type": "paper", "arxiv": "2410.21229", "year": 2024, "venue": "ICRA 2025", "org": "NVIDIA, CMU", "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "whole-body", "rl", "distillation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Distills one policy that supports several humanoid control modes (root velocity, joint angles, keypoint tracking) instead of training a separate controller per mode.", "takeaway_zh": "蒸馏出单一策略，支持多种人形控制模式（根部速度、关节角、关键点跟踪），不必为每种模式各训练一个控制器。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2410.21229.html", "labs": [ "nvidia", "cmu" ], "code_license": null }, { "id": "arxiv-2212.03238", "title": "Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior", "type": "paper", "arxiv": "2212.03238", "year": 2022, "venue": "CoRL 2022", "org": "MIT", "url": null, "code": "https://github.com/Improbable-AI/walk-these-ways", "project": null, "topics": [ "locomotion" ], "tags": [ "real", "quadruped", "sim2real", "rl", "unitree-go1" ], "robot_types": [ "legged" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Trains one quadruped policy that exposes gait parameters (frequency, foot swing, posture) as commands, so a human can tune behavior at deploy time for new tasks; open sim-to-real stack for the Unitree Go1.", "takeaway_zh": "训练一个把步态参数（频率、抬脚高度、姿态等）作为指令开放的四足策略，部署时可由人调节行为以适应新任务；附带面向 Unitree Go1 的开源仿真到真实工具链。", "github_stars": 1471, "page": "https://embodied-ai-radar.com/e/arxiv-2212.03238.html", "labs": [ "mit" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2301.10602", "title": "DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning", "type": "paper", "arxiv": "2301.10602", "year": 2023, "venue": "ICRA 2023", "org": "KAIST", "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "real", "quadruped", "blind", "sim2real", "rl" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A blind quadruped policy that infers terrain properties implicitly from proprioception history with a context-aided estimator, enabling robust walking on stairs and rough ground without cameras.", "takeaway_zh": "盲视四足策略：借助上下文辅助估计器从本体感觉历史中隐式推断地形特性，无需相机即可稳健地走楼梯和崎岖地面。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2301.10602.html", "labs": [ "kaist" ], "code_license": null }, { "id": "arxiv-2306.14874", "title": "ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots", "type": "paper", "arxiv": "2306.14874", "year": 2023, "venue": null, "org": "ETH Zurich, NVIDIA", "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "real", "quadruped", "parkour", "hierarchical", "perception" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A learned navigation policy sequences pre-trained skills (walk, jump, climb, crouch) using a learned obstacle reconstruction; reports up to 2 m/s on hardware (Science Robotics).", "takeaway_zh": "分层的全学习系统：导航策略结合学习到的障碍物重建来调度预训练技能（行走、跳跃、攀爬、下蹲）；报告实机速度可达 2 米/秒。发表于 Science Robotics。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2306.14874.html", "labs": [ "nvidia", "eth" ], "code_license": null }, { "id": "arxiv-2406.10759", "title": "Humanoid Parkour Learning", "type": "paper", "arxiv": "2406.10759", "year": 2024, "venue": "CoRL 2024", "org": "Shanghai Qi Zhi, ShanghaiTech, Tsinghua", "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "real", "humanoid", "parkour", "vision", "unitree-h1", "sim2real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Learns a vision-based whole-body parkour policy for the Unitree H1 without motion references, covering skills such as jumping onto platforms and leaping over gaps, and transfers it to the real robot.", "takeaway_zh": "无需动作参考，为 Unitree H1 学习基于视觉的全身跑酷策略，涵盖跳上平台、跨越沟壑等技能，并迁移到真实机器人。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2406.10759.html", "labs": [ "tsinghua", "qizhi" ], "code_license": null }, { "id": "arxiv-2610.29612", "title": "Watch Your Step: Anticipated Perception for Humanoid Locomotion", "type": "paper", "arxiv": "2610.29612", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/leggedrobotics/foothold-prediction-model", "project": "https://leggedrobotics.github.io/watch-your-step", "topics": [ "locomotion", "perception" ], "tags": [ "humanoid", "perceptive-locomotion", "elevation-map", "rl" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-22", "takeaway_en": "A lightweight foothold prediction model strips terrain irrelevant to the next steps from the elevation map before the humanoid policy sees it, aiming at faster training and better transfer to new terrain.", "takeaway_zh": "不再把整张高程图输入人形机器人运动策略，而是先用轻量的落脚点预测模型去掉与接下来几步无关的地形信息，以加快训练并提升对新地形的泛化。", "github_stars": 142, "page": "https://embodied-ai-radar.com/e/arxiv-2610.29612.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2104.02180", "title": "AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control", "type": "paper", "arxiv": "2104.02180", "year": 2021, "venue": "SIGGRAPH 2021", "org": "UC Berkeley, Shanghai Jiao Tong University", "url": null, "code": null, "project": null, "topics": [ "locomotion", "learning" ], "tags": [ "sim", "humanoid", "rl", "motion-prior", "imitation" ], "robot_types": [ "humanoid", "legged" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A discriminator trained on unstructured mocap clips acts as a style reward, so RL learns tasks with natural motion without hand-picked reference clips; widely reused as a gait prior for legged robots.", "takeaway_zh": "用在无结构动捕片段上训练的判别器作为\"风格奖励\"，强化学习无需逐段指定参考动作即可学到自然的动作；后来被广泛用作足式机器人的步态先验。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2104.02180.html", "labs": [ "berkeley", "sjtu" ], "code_license": null }, { "id": "arxiv-2401.17583", "title": "Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion", "type": "paper", "arxiv": "2401.17583", "year": 2024, "venue": "RSS 2024", "org": "CMU, ETH Zurich", "url": null, "code": "https://github.com/LeCAR-Lab/ABS", "project": null, "topics": [ "locomotion", "navigation" ], "tags": [ "sim2real", "quadruped", "rl", "safety", "reach-avoid" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "ABS pairs an agile RL locomotion policy with a learned reach-avoid value network and a recovery policy that takes over near obstacles, so a quadruped can run fast without crashing.", "takeaway_zh": "ABS 将敏捷的强化学习运动策略与学习的可达-规避价值网络及恢复策略结合，接近障碍时由恢复策略接管，让四足机器人高速奔跑而不碰撞。", "github_stars": 632, "page": "https://embodied-ai-radar.com/e/arxiv-2401.17583.html", "labs": [ "cmu", "eth" ], "code_license": "" }, { "id": "arxiv-2610.18950", "title": "$λ_0$: Scaling Open-World Egocentric Human Data for Humanoid Loco-Manipulation", "type": "paper", "arxiv": "2610.18950", "year": 2026, "venue": null, "org": "Alibaba (Lambda-humanoid Team)", "url": null, "code": "https://github.com/LambdaHumanoid/openlambda", "project": "https://lambdahumanoid.github.io/", "topics": [ "locomotion", "manipulation", "datasets" ], "tags": [ "real", "humanoid", "unitree-g1", "human-video", "egocentric", "whole-body", "foundation-model" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-24", "takeaway_en": "HumanVerse, 500 h of egocentric human video with body and hand motion, aligned to the Unitree G1 for mid-training a humanoid model; reports 90% on SIMPLE and 62.5% on real loco-manipulation tasks.", "takeaway_zh": "构建 500 小时的第一视角人类数据集 HumanVerse（含同步的身体与手部动作），对齐到 Unitree G1 后用于人形基础模型 λ0 的中期训练；论文报告 SIMPLE 上成功率 90%、真实移动操作任务 62.5%，人类数据越多下游表现越好。", "github_stars": 159, "page": "https://embodied-ai-radar.com/e/arxiv-2610.18950.html", "labs": [ "alibaba" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2508.08241", "title": "BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion", "type": "paper", "arxiv": "2508.08241", "year": 2025, "venue": null, "org": "UC Berkeley, Stanford", "url": null, "code": "https://github.com/HybridRobotics/whole_body_tracking", "project": null, "topics": [ "locomotion" ], "tags": [ "sim2real", "humanoid", "motion-tracking", "diffusion", "whole-body" ], "robot_types": [ "humanoid" ], "highlight": true, "rating": 2, "added": null, "takeaway_en": "One motion-tracking recipe with shared hyperparameters learns agile skills (cartwheels, flip-kicks); a guided latent diffusion model composes them for new goals like joystick control, zero-shot on hardware.", "takeaway_zh": "用同一套超参数的运动跟踪方案学会侧手翻、飞踢、冲刺等敏捷技能，再用带引导的潜空间扩散模型组合这些技能以完成摇杆遥控、避障等新目标，零样本迁移到真机。", "github_stars": 2443, "page": "https://embodied-ai-radar.com/e/arxiv-2508.08241.html", "labs": [ "berkeley", "stanford" ], "code_license": "MIT" }, { "id": "arxiv-2610.32966", "title": "ViT-H: Generative Video-To-Humanoid Control with Motion Tracking Feedback", "type": "paper", "arxiv": "2610.32966", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://awfuact.github.io/vit-h/", "topics": [ "locomotion" ], "tags": [ "real", "humanoid", "motion-tracking", "human-video", "diffusion", "teleoperation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Skips reconstruction and retargeting: a diffusion model turns monocular human video straight into humanoid reference motion, post-trained with DPO on tracking outcomes; real-time variant teleoperates a G1 under 400 ms.", "takeaway_zh": "跳过人体重建与重定向：扩散模型直接把单目人类视频生成为人形机器人参考动作，并以运动跟踪结果做 DPO 后训练；自回归版本可在 Unitree G1 上实现低于 400 ms 延迟的实时视频遥操作。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.32966.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.24610", "title": "Solving a Giant Rubik's Cube with a Quadruped Robot", "type": "paper", "arxiv": "2610.24610", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://leggedrobotics.github.io/rubik-skills/", "topics": [ "locomotion", "manipulation", "perception" ], "tags": [ "real", "quadruped", "loco-manipulation", "rl", "sim2real", "distillation", "rgbd" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "A quadruped solves a giant Rubik's Cube with its feet: onboard RGB-D estimates cube pose and colours, a planner sequences skills, and RL whole-body skills turn layers. Reports three full autonomous solves.", "takeaway_zh": "四足机器人只用脚完成巨型魔方：机载 RGB-D 估计魔方位姿与颜色状态，规划器把解法转成技能序列，强化学习训练的全身技能负责翻转魔方、一脚固定一脚推动转层。报告完成三次完全自主求解。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.24610.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.35862", "title": "Learning Force-Reactive Humanoid Loco-Manipulation", "type": "paper", "arxiv": "2610.35862", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion", "manipulation", "control" ], "tags": [ "humanoid", "force-control", "whole-body", "diffusion-policy", "umi", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "FLoRe: robot-free demos from force-sensing UMI-style grippers + body trackers train a diffusion policy that outputs per-hand force commands for a compliant whole-body controller; reports 80% vs. 26.7% for position-only.", "takeaway_zh": "FLoRe：用带力传感的 UMI 式手持夹爪与人体追踪器采集无机器人示教，训练扩散策略输出每只手的力指令，由柔顺全身控制器执行；报告在三个接触密集任务上成功率 80%，仅位置控制为 26.7%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.35862.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.35075", "title": "Zero-HLM: Generalizable Humanoid Loco-Manipulation without Real Robot Data", "type": "paper", "arxiv": "2610.35075", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/BIGAI-General-Vision-Lab/Zero-HLM", "project": "https://young-bit.github.io/zero-hlm/", "topics": [ "locomotion", "manipulation", "sim" ], "tags": [ "humanoid", "real-to-sim", "gaussian-splatting", "data-generation", "unitree-g1", "sim2real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Real-to-sim-to-real: smartphone scans become physics-ready sim scenes, whole-body motions are corrected by rollouts, giving 4,800 demos; a sim-only GR00T N1.7 on Unitree G1 reports 66.7% vs. 46.7% trained on real data.", "takeaway_zh": "真实到仿真再到真实：用手机拍摄重建可物理仿真的场景，并根据仿真回放修正全身动作，生成 4800 条示教；仅用仿真数据训练的 GR00T N1.7 在宇树 G1 上报告平均成功率 66.7%，真实数据训练为 46.7%。", "github_stars": 22, "page": "https://embodied-ai-radar.com/e/arxiv-2610.35075.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.34126", "title": "HumanoidNDM: Closing the Training-Deployment Gap of Motion Tracking via Humanoid Neural Dynamics Model", "type": "paper", "arxiv": "2610.34126", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion", "learning" ], "tags": [ "humanoid", "motion-tracking", "real-world-finetuning", "neural-dynamics", "unitree-g1" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Fine-tunes humanoid motion-tracking policies online on the real robot: a proprioceptive neural dynamics model learned from real data supplies gradients, no motion capture needed. Shown on Unitree G1.", "takeaway_zh": "在真机上在线微调人形机器人动作跟踪策略：用真实数据训练的本体感知神经动力学模型提供可反传梯度，无需动捕系统。在 Unitree G1 上验证对多种扰动的快速适应。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.34126.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.35852", "title": "DiffContact: Differentiable Contact Dynamics for Policy Learning and Contact-Implicit Trajectory Optimization", "type": "paper", "arxiv": "2610.35852", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://diffcontact.github.io", "topics": [ "locomotion", "control", "learning" ], "tags": [ "quadruped", "differentiable-simulation", "contact", "trajectory-optimization", "sim2real", "real" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Smooth analytic contact gradients only in the backward pass, hard-contact forward rollout unchanged: a quadruped trotting policy trains in under 2 min on a desktop and transfers to hardware without fine-tuning.", "takeaway_zh": "只在反向传播中换用平滑的解析摩擦接触梯度，前向仍是硬接触仿真：四足小跑策略在台式机上不到 2 分钟训练完成，无需微调即可迁移到真机；在接触隐式轨迹优化中也能找到更丰富的接触解。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.35852.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.29903", "title": "HuMiT: Low-Latency Whole-Body Humanoid Teleoperation via Compact Reference Tracking", "type": "paper", "arxiv": "2610.29903", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/xiaohu-art/Track", "project": "https://xiaohu-art.github.io/Track/", "topics": [ "locomotion", "manipulation" ], "tags": [ "humanoid", "teleoperation", "motion-tracking", "unitree-g1", "real", "vr" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Whole-body humanoid teleoperation from a compact target (root height and velocity + 4 keypoint poses, current frame only): ~20 ms headset-to-policy latency; a Unitree G1 catches a tossed ball.", "takeaway_zh": "只用紧凑参考目标（根高度、根速度与 4 个稀疏关键点的当前帧位姿）实现人形机器人全身遥操作：头显到策略输出延迟约 20 ms，Unitree G1 能接住抛来的球并完成移动操作。", "github_stars": 5, "page": "https://embodied-ai-radar.com/e/arxiv-2610.29903.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.25711", "title": "SWAP: Test-Time Steerable Humanoid Whole-Body Collision Avoidance", "type": "paper", "arxiv": "2610.25711", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://iscoyizj.github.io/SWAP-steerable-humanoid-oa-website/", "topics": [ "locomotion", "control" ], "tags": [ "humanoid", "collision-avoidance", "diffusion-policy", "whole-body-control", "unitree-g1", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Diffusion planner proposes multimodal collision-aware motions; a whole-body RL controller tracks them. Trained by placing obstacles outside each motion's swept space. Real G1; steering reports +34% on target rewards.", "takeaway_zh": "分层框架：感知条件扩散策略生成多模态、考虑碰撞的动作提议，底层全身 RL 控制器负责跟踪与平衡；训练数据靠事后标注获得（只在已执行动作未扫过的空间放置障碍物）。在真实 Unitree G1 上验证，测试时引导使目标奖励平均提升 34%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25711.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.24675", "title": "Action-Aligned Latent Planning for Versatile Humanoid Loco-Manipulation", "type": "paper", "arxiv": "2610.24675", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://fiberaa.github.io/", "topics": [ "locomotion", "manipulation" ], "tags": [ "humanoid", "loco-manipulation", "behavior-foundation-model", "latent-planning", "unitree-g1", "sim-to-real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "FIBER plans in the latent space of a frozen behavioral foundation model: skills are projected into it, one perceptive flow planner is trained offline. Reports 42.0% mean sim success (latent replay: 11.6%); real G1.", "takeaway_zh": "FIBER 以冻结的行为基础模型（BFM）潜在空间作为统一规划表示，把各任务专用策略投影进该空间，再离线训练单一感知流匹配规划器；仿真中搬箱、开门、抓取平均成功率 42.0%（潜在回放基线 11.6%），并在真实 Unitree G1 上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.24675.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.36546", "title": "REACH: An RL-based Hierarchical Humanoid Crawling Framework for Adaptable Confined Space Traversal", "type": "paper", "arxiv": "2610.36546", "year": 2026, "venue": "Humanoids 2026", "org": "SUSTech", "url": null, "code": null, "project": "https://aislab-sustech.github.io/REACH/", "topics": [ "locomotion" ], "tags": [ "humanoid", "crawling", "rl", "unitree-g1", "lidar", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Unitree G1 walks at variable height (1.27–0.50 m) and crawls (0.50–0.20 m), with a learned transition policy between modes and a LiDAR planner that sets height targets for confined-space traversal on hardware.", "takeaway_zh": "让 Unitree G1 在 1.27–0.50 米高度范围内变高步行、在 0.50–0.20 米范围内爬行，并用学习到的过渡策略在两种模式间切换；结合 LiDAR 规划器给出高度目标，在真机上穿越低矮受限空间。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36546.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.36507", "title": "GRAFT: Geometry-Aware Residual Adaptation for Foundation Trackers", "type": "paper", "arxiv": "2610.36507", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://graft-humanoid.github.io/", "topics": [ "locomotion", "learning" ], "tags": [ "humanoid", "behavior-foundation-model", "residual", "terrain", "depth", "unitree-g1", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Keeps a humanoid behavior foundation model frozen and trains a small lower-body residual adapter for stairs, gaps and platforms, distilled to onboard depth; tested on four trackers and on a Unitree G1 with SONIC.", "takeaway_zh": "冻结人形行为基础模型，仅训练一个作用于下肢目标的轻量残差适配器来应对台阶、沟壑和平台，并从特权地形信息蒸馏到机载深度；在四种跟踪器上验证，并在 Unitree G1（SONIC）上真机部署。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36507.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.36452", "title": "Whole-Body Humanoid Control via Stochastic Motion Tokenization", "type": "paper", "arxiv": "2610.36452", "year": 2026, "venue": "Humanoids 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion", "learning" ], "tags": [ "humanoid", "motion-tokenizer", "whole-body-tracking", "text-to-motion", "unitree-g1", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "A stochastic motion tokenizer gives a high-level generator a compact interface to a whole-body tracking policy; reports better G1 motion reconstruction than FSQ/VQ-VAE and real dance/fight/walk sequences.", "takeaway_zh": "用随机运动分词器把行为标签或文本生成的动作压缩成离散表示，直接作为全身跟踪策略的参考；报告在 Unitree G1 动作重建上优于 FSQ 与 VQ-VAE，并在真机上执行舞蹈、格斗、行走及 35 秒的格斗转舞蹈序列。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36452.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.30061", "title": "Test-Time Motion Steering for Perceptive Humanoid Fall Recovery", "type": "paper", "arxiv": "2610.30061", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://osheraz.github.io/RISE/", "topics": [ "locomotion" ], "tags": [ "humanoid", "fall-recovery", "diffusion-planner", "perception", "unitree-g1", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "RISE learns a vocabulary of recovery movements plus a visual diffusion planner whose predicted intent is steered at test time by task costs (heading, speed, path), no retraining; sim and real Unitree G1.", "takeaway_zh": "RISE 学习一组可组合的起身动作词表，并用视觉扩散规划器预测未来身体状态作为动作意图；推理时用任务代价（朝向、起身速度、路径）引导该意图，无需重新训练即可选择不同起身方式；仿真实验并在 Unitree G1 真机演示。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.30061.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.29169", "title": "SMR: Learning streaming motion retargeting for humanoids from human semantic", "type": "paper", "arxiv": "2610.29169", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://logosroboticsgroup.github.io/SMR/", "topics": [ "locomotion", "control" ], "tags": [ "humanoid", "motion-retargeting", "streaming", "transformer", "whole-body-control", "sim" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-29", "takeaway_en": "SMR distils physics experts that track robot-aware targets into a strictly causal Transformer, retargeting human motion to humanoids frame by frame. Reports better closed-loop results than kinematic baselines.", "takeaway_zh": "SMR 先让物理专家在仿真中跟踪由人体动作转换来的机器人任务空间目标，再用其执行轨迹训练严格因果的 Transformer，实现逐帧流式的人形动作重定向；报告在相同全身控制器下闭环表现优于运动学与物理精修基线。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.29169.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.38027", "title": "METATRON: A Promptable Whole-Body Execution Layer for Humanoids", "type": "paper", "arxiv": "2610.38027", "year": 2026, "venue": null, "org": "NVIDIA", "url": null, "code": null, "project": "https://research.nvidia.com/labs/par/projects/metatron/", "topics": [ "locomotion", "control" ], "tags": [ "humanoid", "whole-body-control", "generative-policy", "prompting", "coding-agents", "unitree-g1", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "One frozen humanoid policy driven by prompts (timed body-part targets, velocity, style); skills are plain code, refined by coding agents. On a Unitree G1 it walks, recovers, picks up objects and sits.", "takeaway_zh": "可“提示”的人形全身执行层：冻结的生成式基础策略接收部分目标（定时的身体部位目标、速度指令、风格）生成全身动作；技能是普通代码，可由编码智能体分析动作数据后自动改进，无需按任务重训。在 Unitree G1 上一个检查点即可行走、恢复、遥操作抓取和坐下。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.38027.html", "labs": [ "nvidia" ], "code_license": null }, { "id": "arxiv-2610.39327", "title": "SONIC-K1: Porting a Universal Humanoid Motion Tracker to a Small Humanoid on a Single GPU", "type": "paper", "arxiv": "2610.39327", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Kyk0/sonic-k1", "project": "https://huggingface.co/Kyry10/sonic-k1", "topics": [ "locomotion" ], "tags": [ "humanoid", "motion-tracking", "booster-k1", "open-weights", "low-compute", "sim-to-real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "Open-weights whole-body motion tracker for the small Booster K1, adapting SONIC and trained on one shared GPU. Tracks most held-out clips in MuJoCo; on hardware, upper-body and slow motions work, stepping less so.", "takeaway_zh": "将 SONIC 通用动作跟踪框架移植到小型人形 Booster K1，在一张共享 GPU 上从头训练并开放权重与代码。MuJoCo 中几乎能跟踪所有留出动作片段；真机上上半身和慢速动作表现良好，迈步与转身仍不稳定，作者坦诚指出对动作延迟敏感。", "github_stars": 13, "page": "https://embodied-ai-radar.com/e/arxiv-2610.39327.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.38388", "title": "ELEVATE: Agentic Controller Acquisition from Task Failure for Quadrupedal Manipulation", "type": "paper", "arxiv": "2610.38388", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://agentic-quad-elevate.github.io/public/", "topics": [ "locomotion", "manipulation", "learning" ], "tags": [ "quadruped", "loco-manipulation", "coding-agent", "llm", "reinforcement-learning", "skill-library", "sim-to-real", "real" ], "robot_types": [ "arm", "legged" ], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "When a quadruped manipulator keeps failing, a coding agent writes an RL problem to learn the missing whole-body skill, adds it to a library and retries; transfers to a real robot (18/20 rack retrieval).", "takeaway_zh": "四足机械臂反复失败时，由编码智能体判断缺少哪种全身控制能力，自动编写强化学习问题（奖励、课程、终止条件）训练新控制器并加入技能库再重试；习得扶墙抬升等技能，仿真五项超出初始能力的任务成功率 98.4–100%，无需真机训练即迁移到实体机器人（货架取物 18/20）。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.38388.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.41877", "title": "Movens-Zero: Scaling Humanoid Motion Foundation Models with Large-Scale Human Videos", "type": "paper", "arxiv": "2610.41877", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/wendell0218/Movens-Zero", "project": "https://movens-zero.github.io", "topics": [ "locomotion" ], "tags": [ "humanoid", "motion-foundation-model", "dataset", "flow-matching" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Movens-5M: 5M execution-verified humanoid motions from web videos, aligned with text, RGB, speech and music. Movens-Zero trains one flow-matching action expert on seven tasks for real-time closed-loop control.", "takeaway_zh": "Movens-5M 从互联网视频构建 500 万条经执行验证的人形动作（超 1 万小时），对齐文本、RGB、语音和音乐。Movens-Zero 用一个流匹配动作专家联合训练七种生成任务，支持实时闭环执行；另有四赛道基准 Movens-Intent。", "github_stars": 15, "page": "https://embodied-ai-radar.com/e/arxiv-2610.41877.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.41354", "title": "DiffWBC: Unlocking Steerable General Humanoid Motion Tracking via One-Step Diffusion Whole-Body Control", "type": "paper", "arxiv": "2610.41354", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://diffwbc.github.io/", "topics": [ "locomotion" ], "tags": [ "humanoid", "whole-body-control", "diffusion", "unitree-g1", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Humanoid tracker as a one-step diffusion model: one forward pass per 50 Hz step, test-time steering via cost gradients, failure discovery from plan dispersion without simulation. Deployed on a Unitree G1.", "takeaway_zh": "用一步扩散模型做人形全身跟踪：每个 50 Hz 控制步只需一次前向；用可微代价梯度在测试时操控冻结的控制器，无需重训；用规划分散度在不做物理仿真的情况下发现跟踪失败。已部署到 Unitree G1。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.41354.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.42097", "title": "ROOF: Learning Humanoid Fall Recovery under Overhead Constraints", "type": "paper", "arxiv": "2610.42097", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://lu-yidan.github.io/ROOF/", "topics": [ "locomotion" ], "tags": [ "humanoid", "fall-recovery", "proprioception", "unitree-g1", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Fall recovery beneath overhead obstacles from proprioception alone: rewards reducing robot-obstacle overlap, no prescribed path. Reports 95 of 150 recoveries on a Unitree G1 across seven real obstacle scenes.", "takeaway_zh": "人形在头顶障碍物下跌倒后的恢复，仅用本体感知：奖励减少机器人与障碍物的重叠，而不预设逃脱路径，由单一策略学会何时清障、建立支撑并站起。报告 Unitree G1 在七个真实场景中 150 次尝试成功 95 次。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.42097.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.07457", "title": "CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection", "type": "paper", "arxiv": "2604.07457", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://shepherd1226.github.io/CMP", "topics": [ "locomotion" ], "tags": [ "loco-manipulation", "whole-body-control", "safety", "ood" ], "robot_types": [ "arm" ], "highlight": false, "rating": 1, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Hardens whole-body end-effector tracking for legged manipulators against out-of-distribution inputs such as sensor noise or infeasible commands, using a frame-wise single-step safety manifold.", "takeaway_zh": "CoRL 2026 Spotlight。提升足式移动操作臂全身末端跟踪对分布外输入（传感器噪声、不可行指令）的鲁棒性：用逐帧安全方案把无限时域安全约束转化为计算高效的单步流形约束。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.07457.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.37974", "title": "FORGE: Feasible Online Reduced-Order Reference Generation and Whole-Body Execution for Agile Humanoid Locomotion", "type": "paper", "arxiv": "2610.37974", "year": 2026, "venue": "Humanoids 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "reduced-order-model", "unitree-g1", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Learns a reduced-order reference generator, then trains a whole-body tracker on its online rollouts, so one pair of policies covers many gaits. On a Unitree G1: -2.0 to 2.8 m/s, hopping and continuous transitions.", "takeaway_zh": "先学习降阶模型参考生成器，再用其在线输出训练全身跟踪策略，一对策略即可覆盖多种步态。Unitree G1 实机实现 -2.0 到 2.8 m/s 行走、单腿跳与连续切换。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.37974.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.41334", "title": "HIPC: Humanoid Iterative Planning and Control via Goal-Conditioned Motion Closure", "type": "paper", "arxiv": "2610.41334", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://hipc-humanoid.github.io/", "topics": [ "locomotion" ], "tags": [ "humanoid", "generative-motion", "closed-loop" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Closes the loop between a generative motion model and a humanoid tracker, which usually run open-loop and drift. Deviation-decayed recovery and history re-anchoring keep the feedback from causing jitter or falls.", "takeaway_zh": "把通常开环运行、会累积漂移的生成式运动模型与人形跟踪器闭环耦合。真实状态反馈易致抖动甚至摔倒，因此加入偏差衰减恢复、历史重锚定与延迟补偿。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.41334.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.05160", "title": "GRAIL: Generating Humanoid Loco-Manipulation from World Models", "type": "paper", "arxiv": "2606.05160", "year": 2026, "venue": "CoRL 2026", "org": "NVIDIA", "url": null, "code": "https://github.com/NVlabs/GRAIL", "project": "https://research.nvidia.com/labs/dair/grail/", "topics": [ "locomotion" ], "tags": [ "humanoid", "loco-manipulation", "data-generation", "world-models" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. A fully virtual pipeline for humanoid loco-manipulation data: grounds video world models in 3D assets and simulator-ready scenes, with no teleoperation or physical setups.", "takeaway_zh": "CoRL 2026 Oral。全虚拟的人形移动操作数据管线：把视频世界模型落到 3D 资产与可仿真场景中，无需遥操作或搭建实体环境。", "github_stars": 603, "page": "https://embodied-ai-radar.com/e/arxiv-2606.05160.html", "labs": [ "nvidia" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2606.11092", "title": "RoboNaldo: Accurate, Stable and Powerful Humanoid Soccer Shooting via Motion-Guided Curriculum Reinforcement Learning", "type": "paper", "arxiv": "2606.11092", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "soccer", "curriculum-rl" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. Three-stage curriculum for humanoid soccer shooting: a single human-kick reference serves as a scaffold, bridging stable motion tracking and goal-driven RL that struggles to discover kicks.", "takeaway_zh": "CoRL 2026 Oral。人形足球射门的三阶段课程：以单条人类踢球参考为脚手架，兼顾动作跟踪的稳定性与任务奖励 RL 的目标适应能力。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.11092.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.09158", "title": "TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model", "type": "paper", "arxiv": "2609.09158", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://tango-vla.github.io/", "topics": [ "locomotion" ], "tags": [ "humanoid", "whole-body", "navigation", "vla" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Language-conditioned humanoid traversal of cluttered rooms that treats navigation as whole-body adaptation of arms, torso and gait rather than 2D path planning.", "takeaway_zh": "CoRL 2026 Spotlight。语言条件下的人形机器人杂乱环境穿行：不把导航当二维路径规划，而是手臂、躯干与步态的全身协同调整。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.09158.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.22174", "title": "OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation", "type": "paper", "arxiv": "2606.22174", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/OpenHLM-project/OpenHLM", "project": "https://openhlm-project.github.io/", "topics": [ "locomotion" ], "tags": [ "humanoid", "loco-manipulation", "vla" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. A one-variable-at-a-time empirical study of what a whole-body native VLA needs, mapping language and pixels directly to all humanoid degrees of freedom.", "takeaway_zh": "CoRL 2026 Spotlight。逐变量对照的实证研究：探索直接把语言与图像映射到人形全部自由度的全身原生 VLA 需要哪些条件。", "github_stars": 110, "page": "https://embodied-ai-radar.com/e/arxiv-2606.22174.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.05873", "title": "LadderMan: Learning Humanoid Perceptive Ladder Climbing", "type": "paper", "arxiv": "2606.05873", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://ladderman-robot.github.io", "topics": [ "locomotion" ], "tags": [ "humanoid", "climbing", "perceptive" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. A humanoid system for climbing diverse ladders and manipulating while on them; a two-stage pipeline learns climbing experts through hybrid motion tracking.", "takeaway_zh": "CoRL 2026 Spotlight。让人形机器人攀爬多种梯子并在梯上操作；两阶段流程用混合动作跟踪学习多个攀爬专家策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.05873.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.32909", "title": "HoloBoxing: A Simple Recipe for Humanoid Boxing with 7 Minutes of Single-GPU Training", "type": "paper", "arxiv": "2610.32909", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "whole-body-control", "simulation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "A compact planner sends body-point targets to a frozen pretrained whole-body controller; the seven minutes excludes controller pretraining. Evaluated in simulation.", "takeaway_zh": "紧凑规划器向冻结的预训练全身控制器发送身体点目标；所称七分钟不含控制器预训练。评估在仿真中进行。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.32909.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.37806", "title": "TACOS: Terrain-Aware COntact Supervision for Visual Footstep Planning", "type": "paper", "arxiv": "2610.37806", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/norlab-ulaval/TACOS", "project": null, "topics": [ "locomotion" ], "tags": [ "legged", "self-supervised", "footstep-planning" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Scores footstep instability from factor-graph residuals in legged state estimation and projects it into earlier images to supervise a dense visual predictor.", "takeaway_zh": "用足式状态估计因子图的残差给落足不稳定性打分，并投影到之前的图像上，监督稠密视觉预测器。", "github_stars": 5, "page": "https://embodied-ai-radar.com/e/arxiv-2610.37806.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.01659", "title": "HuG: Learning Humanoid Whole-Body Affordance-Aware Grasping with Active Perception", "type": "paper", "arxiv": "2611.01659", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://xuanbinpeng.com/hug", "topics": [ "locomotion", "manipulation" ], "tags": [ "humanoid", "grasping", "active-perception", "sim-to-real", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Trained in simulation with no real demos. Controller-in-the-loop filtering rejects 73% of kinematically valid grasps that would destabilize the robot; reports 87.5% real-world success on a multi-level shelf task.", "takeaway_zh": "完全在仿真中训练，无真实示范。把控制器放进回路筛选数据，剔除 73% 运动学可行但会使机器人失稳的抓取；作者报告多层货架任务真实成功率 87.5%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.01659.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.02546", "title": "Energy-Based Policy Orchestration", "type": "paper", "arxiv": "2611.02546", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://yewr.github.io/epo", "topics": [ "locomotion", "control" ], "tags": [ "hierarchical", "humanoid", "energy-based", "sim-to-real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Picks at runtime which low-level policy to run and with which goal, using a learned energy per policy. New policies join by training their own energy model, with no retraining of others. Deployed on a real humanoid.", "takeaway_zh": "运行时为每个底层策略学习能量函数，据此选择执行哪个策略、给它什么目标。新增策略只需训练自己的能量模型，无需重训其他策略或中央调度器；已部署到真实人形的移动操作任务。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.02546.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.02613", "title": "MAPO: Mixture of Experts with Alternating Policy Optimization for Universal Humanoid Motion Tracking", "type": "paper", "arxiv": "2611.02613", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://mapo-corl2026.github.io/MAPO/", "topics": [ "locomotion", "learning" ], "tags": [ "humanoid", "motion-tracking", "mixture-of-experts", "ppo" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Dynamic routing makes policy-update divergence misestimated in mixture-of-experts policies, so MAPO optimizes router and experts in turn. Reports tracking 182 motions PPO with MoE fails on, against one the other way.", "takeaway_zh": "动态路由会让混合专家策略的更新散度估计失准，MAPO 因此交替优化路由与专家。报告可跟踪 182 个 PPO+MoE 失败的动作，反过来只有 1 个。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.02613.html", "labs": [], "code_license": null }, { "id": "arxiv-2512.11047", "title": "WholeBodyVLA: Towards Unified Latent VLA for Whole-Body Loco-Manipulation Control", "type": "paper", "arxiv": "2512.11047", "year": 2025, "venue": "ICLR 2026", "org": null, "url": null, "code": "https://github.com/OpenDriveLab/WholebodyVLA", "project": null, "topics": [ "locomotion", "vla" ], "tags": [ "humanoid", "real", "loco-manipulation", "human-demonstrations" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Latent VLA that learns from cheap action-free egocentric video, paired with an RL controller for advancing, turning and squatting; reports a 21.3% improvement over a baseline on the AgiBot X2.", "takeaway_zh": "潜空间 VLA 从低成本、无动作标注的第一视角视频学习，再配合面向前进、转向、下蹲的强化学习控制器；在智元 X2 上报告比基线提升 21.3%。", "github_stars": 584, "page": "https://embodied-ai-radar.com/e/arxiv-2512.11047.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2410.11825", "title": "Learning Smooth Humanoid Locomotion through Lipschitz-Constrained Policies", "type": "paper", "arxiv": "2410.11825", "year": 2024, "venue": null, "org": null, "url": null, "code": "https://github.com/zixuan417/smooth-humanoid-locomotion", "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "sim-to-real", "rl", "lipschitz" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Imposes a Lipschitz constraint on the learned policy as a general route to smooth motion, replacing hand-tuned low-pass filters and smoothness rewards.", "takeaway_zh": "对学习到的策略施加 Lipschitz 约束，作为获得平滑动作的通用方法，取代需手动调参的低通滤波和平滑奖励。", "github_stars": 244, "page": "https://embodied-ai-radar.com/e/arxiv-2410.11825.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-1804.02717", "title": "DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills", "type": "paper", "arxiv": "1804.02717", "year": 2018, "venue": "SIGGRAPH 2018", "org": "UC Berkeley", "url": null, "code": "https://github.com/xbpeng/DeepMimic", "project": null, "topics": [ "locomotion", "control" ], "tags": [ "character", "motion-imitation", "rl" ], "robot_types": [ "humanoid" ], "highlight": true, "rating": 2, "added": "2026-11-04", "takeaway_en": "Showed RL can imitate a wide range of motion clips, flips and spins included, while staying robust. The motion-imitation template much humanoid work builds on.", "takeaway_zh": "证明强化学习能模仿包括空翻在内的大量动作片段并保持鲁棒，是后续人形动作模仿工作的基本范式。", "github_stars": 3131, "page": "https://embodied-ai-radar.com/e/arxiv-1804.02717.html", "labs": [ "berkeley" ], "code_license": "MIT" }, { "id": "arxiv-2205.01906", "title": "ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters", "type": "paper", "arxiv": "2205.01906", "year": 2022, "venue": "SIGGRAPH 2022", "org": "NVIDIA", "url": null, "code": "https://github.com/nv-tlabs/ASE", "project": null, "topics": [ "locomotion", "control" ], "tags": [ "character", "skill-embedding", "adversarial" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "Learns reusable skill embeddings for simulated characters by combining adversarial imitation and unsupervised RL, so new tasks need not start from scratch.", "takeaway_zh": "结合对抗模仿与无监督强化学习，为仿真角色学习可复用的技能嵌入，新任务不必从零训练。", "github_stars": 1131, "page": "https://embodied-ai-radar.com/e/arxiv-2205.01906.html", "labs": [ "nvidia" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2305.06456", "title": "Perpetual Humanoid Control for Real-time Simulated Avatars", "type": "paper", "arxiv": "2305.06456", "year": 2023, "venue": "ICCV 2023", "org": null, "url": null, "code": "https://github.com/ZhengyiLuo/PHC", "project": "https://zhengyiluo.github.io/PHC/", "topics": [ "locomotion", "control" ], "tags": [ "character", "motion-tracking", "humanoid" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "Humanoid controller that scales to ten thousand motion clips with no external stabilising forces and recovers from falls by itself, so resets are not needed.", "takeaway_zh": "人形控制器可扩展到一万条动作片段且无需外部稳定力，并能自行从跌倒中恢复，无需重置。", "github_stars": 1308, "page": "https://embodied-ai-radar.com/e/arxiv-2305.06456.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2502.20390", "title": "InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactions", "type": "paper", "arxiv": "2502.20390", "year": 2025, "venue": "CVPR 2025", "org": null, "url": null, "code": "https://github.com/Sirui-Xu/InterMimic", "project": null, "topics": [ "locomotion", "control" ], "tags": [ "human-object-interaction", "motion-imitation", "teacher-student" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "One policy learns full-body interaction with varied objects from hours of imperfect MoCap: subject-specific teachers refine the data first, then scale up.", "takeaway_zh": "单一策略从数小时不完美动捕数据学习全身人-物交互：先用个体教师策略精修数据，再扩展规模。", "github_stars": 558, "page": "https://embodied-ai-radar.com/e/arxiv-2502.20390.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2511.07820", "title": "SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control", "type": "paper", "arxiv": "2511.07820", "year": 2025, "venue": null, "org": "NVIDIA", "url": null, "code": null, "project": null, "topics": [ "locomotion", "control" ], "tags": [ "humanoid", "motion-tracking", "whole-body", "scaling" ], "robot_types": [ "humanoid" ], "highlight": true, "rating": 2, "added": "2026-11-04", "takeaway_en": "Tests scaling on humanoid control: more network, data and compute yield a generalist motion-tracking foundation model, with no manual reward engineering.", "takeaway_zh": "检验规模定律在人形控制上的效果：扩大网络、数据和算力，得到通用动作跟踪基础模型，无需手工设计奖励。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2511.07820.html", "labs": [ "nvidia" ], "code_license": null }, { "id": "arxiv-2606.03985", "title": "Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking", "type": "paper", "arxiv": "2606.03985", "year": 2026, "venue": null, "org": "Galbot", "url": null, "code": "https://github.com/GalaxyGeneralRobotics/Humanoid-GPT", "project": "https://qizekun.github.io/Humanoid-GPT/", "topics": [ "locomotion", "control" ], "tags": [ "humanoid", "motion-tracking", "transformer", "sim-to-real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A GPT-style causal Transformer pretrained on a 2B-frame retargeted motion corpus, tracking dynamic whole-body motion with zero-shot generalization to unseen motions.", "takeaway_zh": "在 20 亿帧重定向动作语料上预训练的 GPT 式因果 Transformer，跟踪高动态全身动作，并零次微调泛化到未见动作。", "github_stars": 498, "page": "https://embodied-ai-radar.com/e/arxiv-2606.03985.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2605.20373", "title": "SUGAR: A Scalable Human-Video-Driven Generalizable Humanoid Loco-Manipulation Learning Framework", "type": "paper", "arxiv": "2605.20373", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/tianshuwu/SUGAR", "project": "https://tianshuwu.github.io/sugar-humanoid/", "topics": [ "locomotion", "manipulation" ], "tags": [ "humanoid", "loco-manipulation", "human-video" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Turns diverse human videos into deployable humanoid loco-manipulation skills without task-specific rewards, despite occlusion and retargeting errors in the priors.", "takeaway_zh": "把多样的人类视频转化为可部署的人形移动操作技能，无需任务专用奖励，能应对先验中的遮挡与重定向误差。", "github_stars": 133, "page": "https://embodied-ai-radar.com/e/arxiv-2605.20373.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2309.01952", "title": "Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation", "type": "paper", "arxiv": "2309.01952", "year": 2023, "venue": "Humanoids 2023", "org": null, "url": null, "code": "https://github.com/UT-Austin-RPL/TRILL", "project": "https://ut-austin-rpl.github.io/TRILL", "topics": [ "locomotion", "manipulation" ], "tags": [ "humanoid", "teleoperation", "whole-body", "imitation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "TRILL collects VR demonstrations and uses a whole-body controller to turn task-space commands into joint torques, for data-efficient loco-manipulation imitation.", "takeaway_zh": "TRILL 用 VR 采集演示，由全身控制器把任务空间指令转为关节力矩，实现数据高效的人形移动操作模仿。", "github_stars": 125, "page": "https://embodied-ai-radar.com/e/arxiv-2309.01952.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.05113", "title": "HLO-AMP: Hierarchical Loco-Manipulation from Object-Aware Motion Priors", "type": "paper", "arxiv": "2611.05113", "year": 2026, "venue": "CoRL 2026", "org": "RAI Institute", "url": null, "code": null, "project": null, "topics": [ "locomotion", "manipulation" ], "tags": [ "real", "humanoid", "g1", "motion-prior" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Trains the low-level humanoid controller by motion imitation with the object physically in the scene, then a high-level policy on top; on a Unitree G1 it carries varied boxes and recovers from drops.", "takeaway_zh": "让人形底层控制器在场景中真实带着物体做动作模仿训练，再在其上训练高层策略；在宇树 G1 上可搬运不同箱子，并能从掉落中恢复。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05113.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.04839", "title": "Coordinating Humanoid Loco-Manipulation through Action-Conditioned Latent Foresight", "type": "paper", "arxiv": "2611.04839", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://ju6276.github.io/Action-Grounded-Foresight/", "topics": [ "locomotion" ], "tags": [ "real", "sim", "humanoid", "world-action-model" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A latent world-action model scores proposed humanoid command sequences before running them and replans each step; reports 85.7% on seven real tasks against 70.0% for GR00T N1.7.", "takeaway_zh": "用潜空间世界动作模型在执行前为人形指令序列打分，并逐步重规划；报告七项真机任务成功率 85.7%，GR00T N1.7 为 70.0%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.04839.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.12063", "title": "Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL", "type": "paper", "arxiv": "2608.12063", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion", "learning" ], "tags": [ "loco-manipulation", "offline-to-online", "mpc" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Uses sample-based MPC in simulation as an automatic expert, then trains off-policy RL on sparse rewards, avoiding reward shaping; deployed on an arm-equipped Spot and a G1 humanoid.", "takeaway_zh": "在仿真中用采样 MPC 自动生成专家数据，再以稀疏奖励训练离策略 RL，免去奖励塑形；已部署到带臂 Spot 和 G1 人形机器人。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.12063.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.15988", "title": "ResSafe: Learning Safety Filtering with Residual Reinforcement Learning for Humanoids", "type": "paper", "arxiv": "2609.15988", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion", "control" ], "tags": [ "humanoid", "safety", "residual-rl" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Separates performance from safety on humanoids: the nominal policy handles the task while a residual RL policy acts as an implicit safety filter, avoiding competing reward terms.", "takeaway_zh": "在人形机器人上分离性能与安全：名义策略负责任务，残差 RL 策略充当隐式安全过滤器，避免相互竞争的奖励项。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.15988.html", "labs": [], "code_license": null }, { "id": "arxiv-2506.14770", "title": "GMT: General Motion Tracking for Humanoid Whole-Body Control", "type": "paper", "arxiv": "2506.14770", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/zixuan417/humanoid-general-motion-tracking", "project": "https://gmt-humanoid.github.io", "topics": [ "locomotion" ], "tags": [ "humanoid", "motion-tracking" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Trains one unified policy to track diverse human motions on a real humanoid, instead of one policy per skill, using adaptive sampling and a motion mixture-of-experts.", "takeaway_zh": "训练单一统一策略，让真实人形机器人跟踪多样的人体动作，而非每个技能一个策略；核心是自适应采样和动作混合专家结构。", "github_stars": 453, "page": "https://embodied-ai-radar.com/e/arxiv-2506.14770.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2505.02833", "title": "TWIST: Teleoperated Whole-Body Imitation System", "type": "paper", "arxiv": "2505.02833", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": "https://humanoid-teleop.github.io", "topics": [ "locomotion" ], "tags": [ "humanoid", "teleoperation", "whole-body" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Whole-body humanoid teleoperation: retargeted mocap clips train an RL+BC controller, covering coordinated skills instead of isolated walking or manipulation.", "takeaway_zh": "人形全身遥操作：用重定向后的动捕片段训练 RL+BC 控制器，覆盖协调性全身技能，而不限于单独的行走或操作。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2505.02833.html", "labs": [], "code_license": null }, { "id": "arxiv-2502.03206", "title": "A Unified and General Humanoid Whole-Body Controller for Versatile Locomotion", "type": "paper", "arxiv": "2502.03206", "year": 2025, "venue": "RSS 2025", "org": null, "url": null, "code": "https://github.com/InternRobotics/HugWBC", "project": "https://hugwbc.github.io/", "topics": [ "locomotion" ], "tags": [ "humanoid", "whole-body" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "HugWBC: one controller covering running, jumping and hopping, with adjustable gait parameters such as frequency and foot height.", "takeaway_zh": "HugWBC：用一个控制器覆盖跑、跳、单脚跳等动作，并可调节步频、抬脚高度等步态参数。", "github_stars": 170, "page": "https://embodied-ai-radar.com/e/arxiv-2502.03206.html", "labs": [], "code_license": "" }, { "id": "arxiv-2505.03738", "title": "AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control", "type": "paper", "arxiv": "2505.03738", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/OpenTeleVision/AMO", "project": "https://amo-humanoid.github.io", "topics": [ "locomotion" ], "tags": [ "humanoid", "trajectory-optimization", "whole-body" ], "robot_types": [ "hand", "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "AMO joins sim-to-real RL with trajectory optimization for adaptive whole-body control, enabling large-workspace tasks such as picking objects off the ground.", "takeaway_zh": "AMO 将 sim-to-real 强化学习与轨迹优化结合，实现自适应全身控制，支持从地面拾物等大工作空间任务。", "github_stars": 388, "page": "https://embodied-ai-radar.com/e/arxiv-2505.03738.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2511.04131", "title": "BFM-Zero: A Promptable Behavioral Foundation Model for Humanoid Control Using Unsupervised Reinforcement Learning", "type": "paper", "arxiv": "2511.04131", "year": 2025, "venue": "ICLR 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion", "learning" ], "tags": [ "humanoid", "behavior-foundation-model", "unsupervised-rl" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A promptable humanoid foundation model: a shared latent space embeds motions, goals and rewards, so one policy can be prompted for different tasks on a real robot.", "takeaway_zh": "可提示的人形基础模型：用共享隐空间嵌入动作、目标和奖励，同一策略可在真机上按提示执行不同任务。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2511.04131.html", "labs": [], "code_license": null }, { "id": "arxiv-2510.02252", "title": "Retargeting Matters: General Motion Retargeting for Humanoid Motion Tracking", "type": "paper", "arxiv": "2510.02252", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/YanjieZe/GMR", "project": "https://jaraujo98.github.io/retargeting_matters", "topics": [ "locomotion" ], "tags": [ "humanoid", "retargeting" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "Shows retargeting artifacts such as foot sliding hurt tracking policies, and offers GMR, a general retargeter. Its open-source repo is widely used for humanoid data.", "takeaway_zh": "说明脚滑等重定向瑕疵会损害跟踪策略，并提出通用重定向方法 GMR；其开源仓库在人形数据处理中使用广泛。", "github_stars": 2821, "page": "https://embodied-ai-radar.com/e/arxiv-2510.02252.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2509.26633", "title": "OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction", "type": "paper", "arxiv": "2509.26633", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/amazon-far/holosoma", "project": "https://omniretarget.github.io", "topics": [ "locomotion" ], "tags": [ "humanoid", "retargeting", "interaction" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "Retargeting that preserves human-object and human-scene interaction rather than only body pose, generating physically plausible data for humanoid loco-manipulation.", "takeaway_zh": "不仅保持身体姿态，还保留人与物体、人与场景交互的重定向方法，为人形移动操作生成物理上合理的数据。", "github_stars": 1718, "page": "https://embodied-ai-radar.com/e/arxiv-2509.26633.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2509.20322", "title": "VisualMimic: Visual Humanoid Loco-Manipulation via Motion Tracking and Generation", "type": "paper", "arxiv": "2509.20322", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/operator22th/VisualMimic", "project": "https://visualmimic.github.io", "topics": [ "locomotion" ], "tags": [ "humanoid", "vision", "loco-manipulation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Sim-to-real visual loco-manipulation with a task-agnostic keypoint tracker below a policy that generates motion, so egocentric vision replaces external motion capture.", "takeaway_zh": "视觉移动操作 sim-to-real 框架：底层是任务无关的关键点跟踪器，上层策略生成动作，以第一视角视觉取代外部动捕。", "github_stars": 300, "page": "https://embodied-ai-radar.com/e/arxiv-2509.20322.html", "labs": [], "code_license": "" }, { "id": "arxiv-2510.05070", "title": "ResMimic: From General Motion Tracking to Humanoid Whole-body Loco-Manipulation via Residual Learning", "type": "paper", "arxiv": "2510.05070", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/amazon-far/ResMimic", "project": "https://resmimic.github.io/", "topics": [ "locomotion" ], "tags": [ "humanoid", "residual-learning", "loco-manipulation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Two-stage humanoid loco-manipulation: a general motion-tracking policy trained on human data, then a residual policy adds object-aware precision.", "takeaway_zh": "两阶段人形移动操作：先用人体数据训练通用动作跟踪策略，再用残差策略补上感知物体的精确控制。", "github_stars": 79, "page": "https://embodied-ai-radar.com/e/arxiv-2510.05070.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2505.06776", "title": "FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation", "type": "paper", "arxiv": "2505.06776", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "force-control", "loco-manipulation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Dual-agent RL for force-adaptive loco-manipulation: a lower-body agent keeps walking stable while an upper-body agent tracks the end effector under external forces.", "takeaway_zh": "面向力自适应移动操作的双智能体强化学习：下半身智能体保持行走稳定，上半身智能体在外力下跟踪末端位置。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2505.06776.html", "labs": [], "code_license": null }, { "id": "arxiv-2505.07294", "title": "HuB: Learning Extreme Humanoid Balance", "type": "paper", "arxiv": "2505.07294", "year": 2025, "venue": "CoRL 2025", "org": null, "url": null, "code": null, "project": "https://hub-robot.github.io", "topics": [ "locomotion" ], "tags": [ "humanoid", "balance", "rl" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Tackles balance-heavy skills such as one-leg stands and high kicks, where motion tracking usually fails because of reference errors. Oral at CoRL 2025.", "takeaway_zh": "攻克单脚站立、高踢腿等强平衡技能，这类动作常因参考动作误差导致跟踪失败。CoRL 2025 Oral。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2505.07294.html", "labs": [], "code_license": null }, { "id": "arxiv-2506.12851", "title": "KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills", "type": "paper", "arxiv": "2506.12851", "year": 2025, "venue": "NeurIPS 2025", "org": null, "url": null, "code": "https://github.com/TeleHuman/PBHC", "project": "https://kungfubot.github.io", "topics": [ "locomotion" ], "tags": [ "humanoid", "motion-tracking", "agile" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "Learns highly dynamic skills such as kung fu and dancing, through multi-step motion processing and adaptive motion tracking.", "takeaway_zh": "通过多步动作处理和自适应动作跟踪，学习功夫、舞蹈等高动态技能。", "github_stars": 1072, "page": "https://embodied-ai-radar.com/e/arxiv-2506.12851.html", "labs": [], "code_license": "" }, { "id": "arxiv-2502.10363", "title": "BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds", "type": "paper", "arxiv": "2502.10363", "year": 2025, "venue": "RSS 2025", "org": null, "url": null, "code": null, "project": "https://why618188.github.io/beamdojo", "topics": [ "locomotion" ], "tags": [ "humanoid", "sparse-footholds", "rl" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Agile humanoid walking on sparse footholds, using a sampling-based foothold reward to overcome how rarely good footsteps occur during learning.", "takeaway_zh": "让人形机器人在稀疏落脚点上敏捷行走，用基于采样的落脚奖励解决学习中有效落脚过于稀少的问题。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2502.10363.html", "labs": [], "code_license": null }, { "id": "arxiv-2412.13196", "title": "ExBody2: Advanced Expressive Humanoid Whole-Body Control", "type": "paper", "arxiv": "2412.13196", "year": 2024, "venue": null, "org": null, "url": null, "code": null, "project": "https://exbody2.github.io", "topics": [ "locomotion" ], "tags": [ "humanoid", "motion-tracking" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Expressive whole-body tracking trained on mocap and simulated data, decoupling whole-body velocity tracking from body-landmark tracking.", "takeaway_zh": "基于动捕和仿真数据训练的表现性全身跟踪，将整体速度跟踪与身体关键点跟踪解耦。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2412.13196.html", "labs": [], "code_license": null }, { "id": "arxiv-2506.09366", "title": "SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending", "type": "paper", "arxiv": "2506.09366", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/Humanoid-SkillBlender/SkillBlender", "project": "https://usc-gvl.github.io/SkillBlender-web/", "topics": [ "locomotion" ], "tags": [ "humanoid", "skill-blending", "loco-manipulation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Blends reusable skills for humanoid whole-body loco-manipulation, avoiding the task-specific tuning most controllers need.", "takeaway_zh": "通过融合可复用技能实现人形全身移动操作，避免多数控制器所需的按任务调参。", "github_stars": 117, "page": "https://embodied-ai-radar.com/e/arxiv-2506.09366.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2509.14353", "title": "DreamControl: Human-Inspired Whole-Body Humanoid Control for Scene Interaction via Guided Diffusion", "type": "paper", "arxiv": "2509.14353", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/GenRobo/DreamControl", "project": "https://genrobo.github.io/DreamControl/", "topics": [ "locomotion" ], "tags": [ "humanoid", "diffusion", "rl" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A diffusion prior trained on human motion guides RL in simulation, so policies find whole-body solutions for tasks such as opening a drawer that direct RL misses.", "takeaway_zh": "用人体动作训练的扩散先验引导仿真中的 RL，使策略找到开抽屉等任务的全身解法，而直接 RL 难以发现。", "github_stars": 230, "page": "https://embodied-ai-radar.com/e/arxiv-2509.14353.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2510.11072", "title": "PhysHSI: Towards a Real-World Generalizable and Natural Humanoid-Scene Interaction System", "type": "paper", "arxiv": "2510.11072", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/InternRobotics/PhysHSI", "project": "https://why618188.github.io/physhsi/", "topics": [ "locomotion" ], "tags": [ "humanoid", "scene-interaction" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "PhysHSI: a real-world humanoid-scene interaction system for tasks such as carrying objects and sitting on chairs, keeping motion natural and scene perception robust.", "takeaway_zh": "PhysHSI：面向搬物、坐椅等任务的真实人形-场景交互系统，兼顾动作自然与场景感知鲁棒。", "github_stars": 321, "page": "https://embodied-ai-radar.com/e/arxiv-2510.11072.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2509.16757", "title": "HDMI: Learning Interactive Humanoid Whole-Body Control from Human Videos", "type": "paper", "arxiv": "2509.16757", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": "https://hdmi-humanoid.github.io", "topics": [ "locomotion" ], "tags": [ "humanoid", "object-interaction", "video" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "HDMI learns whole-body humanoid-object interaction from monocular RGB videos, by extracting robot and object trajectories and co-tracking them with RL.", "takeaway_zh": "HDMI 从单目 RGB 视频学习人形全身与物体的交互：提取并重定向人与物体轨迹，再用 RL 同时跟踪二者。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2509.16757.html", "labs": [], "code_license": null }, { "id": "arxiv-2410.12773", "title": "Harmon: Whole-Body Motion Generation of Humanoid Robots from Language Descriptions", "type": "paper", "arxiv": "2410.12773", "year": 2024, "venue": "CoRL 2024", "org": null, "url": null, "code": null, "project": "https://ut-austin-rpl.github.io/Harmon/", "topics": [ "locomotion", "vla" ], "tags": [ "humanoid", "language", "motion-generation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Generates humanoid whole-body motion from language, starting from human motion priors and refining with LLM commonsense reasoning. Oral at CoRL 2024.", "takeaway_zh": "从语言描述生成人形全身动作：以人体动作先验初始化，再用 LLM 的常识推理加以修正。CoRL 2024 Oral。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2410.12773.html", "labs": [], "code_license": null }, { "id": "arxiv-2508.21043", "title": "HITTER: A HumanoId Table TEnnis Robot via Hierarchical Planning and Learning", "type": "paper", "arxiv": "2508.21043", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "table-tennis", "planning" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Humanoid table tennis with a model-based planner and an RL whole-body controller; reports up to 106 consecutive shots with a human opponent.", "takeaway_zh": "人形乒乓球系统，结合基于模型的规划器和 RL 全身控制器；报告与人对打最多连续 106 拍。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2508.21043.html", "labs": [], "code_license": null }, { "id": "arxiv-2412.07773", "title": "Mobile-TeleVision: Predictive Motion Priors for Humanoid Whole-Body Control", "type": "paper", "arxiv": "2412.07773", "year": 2024, "venue": "ICRA 2025", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "teleoperation", "whole-body" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Keeps IK and retargeting for precise upper-body control, and uses RL with Predictive Motion Priors for robust legs.", "takeaway_zh": "上半身保留 IK 与重定向以求精确，腿部用结合预测运动先验的 RL 保证鲁棒。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2412.07773.html", "labs": [], "code_license": null }, { "id": "arxiv-2502.01465", "title": "Embrace Collisions: Humanoid Shadowing for Deployable Contact-Agnostics Motions", "type": "paper", "arxiv": "2502.01465", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": "https://project-instinct.github.io", "topics": [ "locomotion" ], "tags": [ "humanoid", "contact", "motion-tracking" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Humanoid shadowing that allows contact with any body part, such as sitting, rolling and getting up, instead of only feet and hands.", "takeaway_zh": "允许任意身体部位接触环境的人形动作跟随，可完成坐、滚、起身等动作，而不只限于手脚接触。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2502.01465.html", "labs": [], "code_license": null }, { "id": "arxiv-2406.06005", "title": "WoCoCo: Learning Whole-Body Humanoid Control with Sequential Contacts", "type": "paper", "arxiv": "2406.06005", "year": 2024, "venue": null, "org": null, "url": null, "code": "https://github.com/LeCAR-Lab/wococo", "project": "https://lecar-lab.github.io/wococo/", "topics": [ "locomotion" ], "tags": [ "humanoid", "contact", "rl" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Learns humanoid tasks as sequences of contacts, reducing the task-specific tuning and state machines RL usually needs.", "takeaway_zh": "把人形任务建模为接触序列来学习，减少 RL 通常需要的按任务调参和状态机设计。", "github_stars": 148, "page": "https://embodied-ai-radar.com/e/arxiv-2406.06005.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2502.08378", "title": "Learning Humanoid Standing-up Control across Diverse Postures", "type": "paper", "arxiv": "2502.08378", "year": 2025, "venue": "RSS 2025", "org": null, "url": null, "code": "https://github.com/OpenRobotLab/HoST", "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "fall-recovery", "rl" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "HoST learns to stand up from diverse postures with RL, without predefined ground-specific motions, and deploys on real robots.", "takeaway_zh": "HoST 用强化学习从多种姿态起身，无需预定义的特定地面动作，并部署到真机。", "github_stars": 644, "page": "https://embodied-ai-radar.com/e/arxiv-2502.08378.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2502.12152", "title": "Learning Getting-Up Policies for Real-World Humanoid Robots", "type": "paper", "arxiv": "2502.12152", "year": 2025, "venue": "RSS 2025", "org": null, "url": null, "code": "https://github.com/RunpeiDong/humanup", "project": "https://humanoid-getup.github.io/", "topics": [ "locomotion" ], "tags": [ "humanoid", "fall-recovery", "rl" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A learning framework for getting up from varied fallen configurations on varied terrains, where hand-designed controllers struggle.", "takeaway_zh": "针对不同跌倒姿态和地形学习起身控制器，而这类场景很难手工设计控制器。", "github_stars": 236, "page": "https://embodied-ai-radar.com/e/arxiv-2502.12152.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2505.10918", "title": "Unleashing Humanoid Reaching Potential via Real-world-Ready Skill Space", "type": "paper", "arxiv": "2505.10918", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/GalaxyGeneralRobotics/OpenWBT", "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "reaching", "skill-space" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "R2S2 tunes primitive skills for sim-to-real, ensembles them into a latent space, and trains a planner over it for large-space humanoid reaching.", "takeaway_zh": "R2S2 先调好可迁移到真机的基础技能，再融合成隐空间，并在其上训练规划器，实现人形大范围够取。", "github_stars": 775, "page": "https://embodied-ai-radar.com/e/arxiv-2505.10918.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2601.07701", "title": "Deep Whole-body Parkour", "type": "paper", "arxiv": "2601.07701", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/project-instinct/InstinctLab", "project": "https://project-instinct.github.io/deep-whole-body-parkour", "topics": [ "locomotion", "perception" ], "tags": [ "humanoid", "parkour", "perception" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Adds exteroceptive sensing to whole-body motion tracking, so a humanoid performs dynamic non-locomotion skills on uneven terrain.", "takeaway_zh": "将外部感知融入全身动作跟踪，让人形机器人在不平整地形上完成高动态的非行走类技能。", "github_stars": 872, "page": "https://embodied-ai-radar.com/e/arxiv-2601.07701.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2601.07718", "title": "Hiking in the Wild: A Scalable Perceptive Parkour Framework for Humanoids", "type": "paper", "arxiv": "2601.07718", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/project-instinct/InstinctLab", "project": "https://project-instinct.github.io/hiking-in-the-wild", "topics": [ "locomotion", "perception" ], "tags": [ "humanoid", "parkour", "depth" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "An end-to-end perceptive framework mapping depth to control for humanoid hiking over diverse unstructured terrain.", "takeaway_zh": "端到端感知框架，将深度输入映射到控制，实现人形机器人在多样非结构化地形上徒步。", "github_stars": 872, "page": "https://embodied-ai-radar.com/e/arxiv-2601.07718.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2602.15827", "title": "Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching", "type": "paper", "arxiv": "2602.15827", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/amazon-far/php_parkour", "project": "https://php-parkour.github.io/", "topics": [ "locomotion" ], "tags": [ "humanoid", "parkour", "motion-matching" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Chains dynamic human skills with motion matching so a humanoid does agile, perception-driven parkour.", "takeaway_zh": "用动作匹配串联高动态人类技能，让人形机器人完成敏捷的感知驱动跑酷。", "github_stars": 287, "page": "https://embodied-ai-radar.com/e/arxiv-2602.15827.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2602.23843", "title": "OmniXtreme: Breaking the Generality Barrier in High-Dynamic Humanoid Control", "type": "paper", "arxiv": "2602.23843", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Perkins729/OmniXtreme", "project": "https://extreme-humanoid.github.io/", "topics": [ "locomotion" ], "tags": [ "humanoid", "motion-tracking", "agile" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Targets the 'generality barrier': tracking fidelity collapsing as motion libraries grow, especially for high-dynamic motions on real hardware.", "takeaway_zh": "针对“通用性壁垒”：动作库扩大后跟踪精度崩塌，在真机高动态动作上尤为明显。", "github_stars": 753, "page": "https://embodied-ai-radar.com/e/arxiv-2602.23843.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2602.02473", "title": "HumanX: Toward Agile and Generalizable Humanoid Interaction Skills from Human Videos", "type": "paper", "arxiv": "2602.02473", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "interaction", "video" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Compiles human video into generalizable real-world humanoid interaction skills, without task-specific design.", "takeaway_zh": "把人类视频转化为可泛化的真实人形交互技能，无需按任务设计。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.02473.html", "labs": [], "code_license": null }, { "id": "arxiv-2510.17792", "title": "SoftMimic: Learning Compliant Whole-body Control from Examples", "type": "paper", "arxiv": "2510.17792", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/Improbable-AI/softmimic", "project": "https://gmargo11.github.io/softmimic/", "topics": [ "locomotion", "control" ], "tags": [ "humanoid", "compliance" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Learns compliant whole-body control from example motions, so a humanoid yields to unexpected contact instead of stiffly correcting back to the reference.", "takeaway_zh": "从示例动作学习柔顺全身控制，使人形机器人在意外接触时顺应，而不是僵硬地回到参考动作。", "github_stars": 74, "page": "https://embodied-ai-radar.com/e/arxiv-2510.17792.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2511.04679", "title": "GentleHumanoid: Learning Upper-body Compliance for Contact-rich Human and Object Interaction", "type": "paper", "arxiv": "2511.04679", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": "https://gentle-humanoid.axell.top", "topics": [ "locomotion", "control" ], "tags": [ "humanoid", "compliance", "impedance" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Integrates impedance control into whole-body tracking so a humanoid's upper body stays compliant in contact with people and objects.", "takeaway_zh": "将阻抗控制融入全身跟踪，使人形上半身在接触人和物体时保持柔顺。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2511.04679.html", "labs": [], "code_license": null }, { "id": "arxiv-2512.14689", "title": "CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation", "type": "paper", "arxiv": "2512.14689", "year": 2025, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://nvlabs.github.io/CHIP/", "topics": [ "locomotion", "control" ], "tags": [ "humanoid", "compliance" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "CHIP is a plug-and-play module adding controllable end-effector stiffness to agile tracking policies, for forceful tasks such as wiping and pushing carts.", "takeaway_zh": "CHIP 是即插即用模块，为敏捷跟踪策略加入可控的末端刚度，用于擦拭、推车等需要施力的任务。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2512.14689.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.00401", "title": "ZEST: Zero-shot Embodied Skill Transfer for Athletic Robot Control", "type": "paper", "arxiv": "2602.00401", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "zero-shot", "motion-imitation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Motion imitation trained from mocap, noisy monocular video and animation, deployed to hardware zero-shot with less per-skill tuning.", "takeaway_zh": "基于动捕、含噪单目视频和动画数据训练动作模仿策略，零样本部署到真机，减少逐技能调参。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.00401.html", "labs": [], "code_license": null }, { "id": "arxiv-2409.06366", "title": "One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion", "type": "paper", "arxiv": "2409.06366", "year": 2024, "venue": null, "org": null, "url": null, "code": "https://github.com/nico-bohlinger/one_policy_to_run_them_all", "project": null, "topics": [ "locomotion", "learning" ], "tags": [ "cross-embodiment", "multi-embodiment" ], "robot_types": [ "humanoid", "legged" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "URMA: one end-to-end architecture that controls many legged morphologies, including quadrupeds, humanoids and hexapods.", "takeaway_zh": "URMA：用一个端到端架构控制四足、人形、六足等多种足式形态。", "github_stars": 233, "page": "https://embodied-ai-radar.com/e/arxiv-2409.06366.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2506.20487", "title": "A Survey of Behavior Foundation Model: Next-Generation Whole-Body Control System of Humanoid Robots", "type": "paper", "arxiv": "2506.20487", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": "https://github.com/yuanmingqi/awesome-bfm-papers", "topics": [ "locomotion" ], "tags": [ "survey", "humanoid", "behavior-foundation-model" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Survey of behavior foundation models, pretrained generalist whole-body controllers that avoid costly retraining for each new task. Has a companion paper list.", "takeaway_zh": "综述行为基础模型：预训练的通用全身控制器，避免每个新任务都重新训练。附配套论文列表。", "github_stars": 349, "page": "https://embodied-ai-radar.com/e/arxiv-2506.20487.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2501.02116", "title": "Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning", "type": "paper", "arxiv": "2501.02116", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion", "manipulation" ], "tags": [ "survey", "humanoid" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Survey of humanoid locomotion and manipulation across control, planning and learning, from model-based methods onward.", "takeaway_zh": "综述人形移动与操作，涵盖控制、规划与学习，从基于模型的方法讲起。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2501.02116.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.06908", "title": "Perceptive Humanoid Motion from Simulation", "type": "paper", "arxiv": "2611.06908", "year": 2026, "venue": null, "org": "Apple", "url": null, "code": "https://github.com/apple-aiml-research/ml-halo", "project": "https://apple-aiml-research.github.io/ml-halo/", "topics": [ "locomotion", "sim" ], "tags": [ "humanoid", "sim2real", "synthetic-data", "vision", "language", "real" ], "robot_types": [ "humanoid" ], "highlight": true, "rating": 2, "added": "2026-11-05", "takeaway_en": "HALO trains humanoid skills on vision-language-motion data synthesised in a thousand procedural homes, so data scales with compute. Reports zero-shot transfer: 91% over 360 trials in six real environments.", "takeaway_zh": "HALO 在上千个程序化生成的家庭场景中合成视觉-语言-动作数据来训练人形技能，数据量随算力增长。报告零微调迁移：六个未建图真实环境的 360 次试验成功率 91%。", "github_stars": 325, "page": "https://embodied-ai-radar.com/e/arxiv-2611.06908.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2611.05507", "title": "Ctrl-F: Force as an Interface for Humanoid Loco-Manipulation", "type": "paper", "arxiv": "2611.05507", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://forcectrl.github.io/", "topics": [ "locomotion", "manipulation", "control" ], "tags": [ "humanoid", "force-control", "motion-tracking", "loco-manipulation", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Adds an explicit 3-D force command per contact body to motion tracking, set online by a planner. On hardware it pushes and kicks boxes up to 20 kg where a force-blind baseline fails, and adapts to added loads.", "takeaway_zh": "在动作跟踪中为每个接触部位加入显式三维力指令，由高层规划器在线给出。实机可推、踢最重 20 kg 的箱子（不感知力的基线会失败），并能适应任务中途增加的负载。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05507.html", "labs": [], "code_license": null }, { "id": "arxiv-2502.14795", "title": "Humanoid-VLA: Towards Universal Humanoid Control with Visual Integration", "type": "paper", "arxiv": "2502.14795", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "vla", "egocentric" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Humanoid-VLA combines language understanding, egocentric perception and motion control toward universal humanoid control.", "takeaway_zh": "Humanoid-VLA 融合语言理解、第一视角感知和运动控制，面向通用人形控制。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2502.14795.html", "labs": [], "code_license": null }, { "id": "arxiv-2502.13134", "title": "RHINO: Learning Real-Time Humanoid-Human-Object Interaction from Human Demonstrations", "type": "paper", "arxiv": "2502.13134", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/TimerChen/RHINO", "project": "https://humanoid-interaction.github.io/", "topics": [ "locomotion" ], "tags": [ "humanoid", "hri", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "RHINO learns real-time humanoid-human-object interaction from human demonstrations, reacting to human signals instead of fixed task stages.", "takeaway_zh": "RHINO 从人类示教学习实时人形-人-物交互，根据人的信号反应，而非固定任务阶段。", "github_stars": 42, "page": "https://embodied-ai-radar.com/e/arxiv-2502.13134.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2503.10626", "title": "NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models", "type": "paper", "arxiv": "2503.10626", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "imitation-learning", "video-diffusion", "humanoid" ], "robot_types": [ "humanoid", "legged" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "NIL learns motor skills across humanoids, quadrupeds and animals from pre-trained video diffusion models, with no demo data.", "takeaway_zh": "NIL 借助预训练视频扩散模型学习人形、四足等形态的运动技能，无需示教数据。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2503.10626.html", "labs": [], "code_license": null }, { "id": "arxiv-2309.09969", "title": "Prompt a Robot to Walk with Large Language Models", "type": "paper", "arxiv": "2309.09969", "year": 2023, "venue": "CDC 2024", "org": null, "url": null, "code": "https://github.com/HybridRobotics/prompt2walk", "project": "https://prompt2walk.github.io/", "topics": [ "locomotion" ], "tags": [ "llm", "gait", "humanoid" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Directly prompts an LLM to output a robot's walking actions, an early test of grounding LLMs in low-level control.", "takeaway_zh": "直接让大模型输出机器人的行走动作，是把大模型用于底层控制的早期尝试。", "github_stars": 120, "page": "https://embodied-ai-radar.com/e/arxiv-2309.09969.html", "labs": [], "code_license": "" }, { "id": "arxiv-2609.38172", "title": "Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation", "type": "paper", "arxiv": "2609.38172", "year": 2026, "venue": "CoRL 2026", "org": "Amazon FAR", "url": null, "code": "https://github.com/amazon-far/PRISM", "project": "https://prism-real2sim2real.github.io/", "topics": [ "locomotion", "manipulation", "sim" ], "tags": [ "humanoid", "loco-manipulation", "video-generation", "real2sim2real", "sim2real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "PRISM turns a few real videos into hundreds of counterfactual human-object clips via video generation, rebuilds them in sim, and trains one humanoid carrying policy that handles unseen objects with no real fine-tuning.", "takeaway_zh": "PRISM 用视频生成把少量真实视频扩增为数百段反事实人-物交互视频，在仿真中重建并训练单一人形搬运策略，无需真机微调即可泛化到未见物体。", "github_stars": 229, "page": "https://embodied-ai-radar.com/e/arxiv-2609.38172.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2602.16705", "title": "HERO: Learning Humanoid End-Effector Control for Visual Whole-Body Open-Vocabulary Object Grasping", "type": "paper", "arxiv": "2602.16705", "year": 2026, "venue": "CoRL 2026", "org": "UIUC", "url": null, "code": "https://github.com/RunpeiDong/HERO", "project": "https://hero-humanoid.github.io/", "topics": [ "locomotion", "manipulation" ], "tags": [ "humanoid", "loco-manipulation", "end-effector-tracking", "grasping", "sim2real", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Modular humanoid grasping: large vision models handle the scene, a sim-trained end-effector tracker with IK and a learned forward model handles the arm. Reports 2.44 cm tracking error, 5.5x better than prior work.", "takeaway_zh": "模块化人形抓取：大视觉模型负责场景理解，仿真训练的末端跟踪策略结合 IK 与学习的正运动学模型负责执行。报告末端跟踪误差 2.44 厘米，比先前最好方法低 5.5 倍。", "github_stars": 21, "page": "https://embodied-ai-radar.com/e/arxiv-2602.16705.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.05501", "title": "Learning Contact Representation for Leg Odometry", "type": "paper", "arxiv": "2606.05501", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/egirgin/leg-odometry-contact-representation", "project": null, "topics": [ "locomotion", "perception" ], "tags": [ "legged", "state-estimation", "contact-detection", "self-supervised", "kalman-filter" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Self-supervised foot-contact estimation from joint encoders and IMU only, fed as a stance belief into a Kalman filter. Reports the lowest leg-odometry error among the compared baselines.", "takeaway_zh": "仅用关节编码器与 IMU 自监督估计足端接触，并作为支撑置信度输入卡尔曼滤波器。报告腿式里程计误差在对比方法中最低。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2606.05501.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.08407", "title": "Social Humanoid: Socially Responsive Human-Humanoid Interaction", "type": "paper", "arxiv": "2611.08407", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://von31.github.io/social-humanoid/", "topics": [ "locomotion", "datasets" ], "tags": [ "humanoid", "human-robot-interaction", "motion-generation", "retargeting", "g1", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Humanoid that reacts to a human partner: a token model generates G1 whole-body motion conditioned on the partner, and a tracker executes it. Adds a 260-hour human-humanoid interaction benchmark.", "takeaway_zh": "让人形机器人对人类伙伴做出回应：token 化序列模型以伙伴动作为条件生成 G1 全身动作，再由跟踪控制器执行。并提供 260 小时的人-人形交互基准。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08407.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.08039", "title": "HITCH: Teaching a Vision-Language Model to Operate a Humanoid through a Whole-Body Harness", "type": "paper", "arxiv": "2611.08039", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://hitch.axell.top/", "topics": [ "locomotion", "vla" ], "tags": [ "humanoid", "vlm", "whole-body", "harness", "g1", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Harness that lets a VLM drive a humanoid: it points at image locations for 3D positions and issues pelvis, torso, hand, walk and gaze commands. After fine-tuning, a real G1 carries a box with no real data.", "takeaway_zh": "为 VLM 控制人形机器人提供“挽具”接口：在图像上点选即得 3D 位置，并下达骨盆、躯干、手、行走和视线指令。用教师轨迹微调后，真实 G1 无需真机数据即可搬箱子。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08039.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.28523", "title": "VLM-Guided Contact Retargeting and Generative Partner Modeling for Humanoid-Human Interaction", "type": "paper", "arxiv": "2610.28523", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://yutoshibata07.github.io/REACT/", "topics": [ "locomotion" ], "tags": [ "humanoid", "human-robot-interaction", "contact", "retargeting", "egocentric-depth", "g1", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "REACT learns humanoid-human contact interactions from human-human mocap: VLM-guided contact retargeting, then onboard-depth policies that also predict the occluded partner. A real G1 does all five interactions.", "takeaway_zh": "REACT 从人-人动捕中学习人形与人的接触交互：VLM 引导接触重定向，再蒸馏为只用机载深度的策略，并预测被遮挡伙伴的状态。真实 G1 完成全部五种交互。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.28523.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.06718", "title": "SkillX: Unified Multi-Skill Policy Learning for Humanoid Soccer", "type": "paper", "arxiv": "2609.06718", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://yzc0731.github.io/SkillX/", "topics": [ "locomotion" ], "tags": [ "humanoid", "soccer", "multi-skill", "amp", "sim2real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "One command-conditioned policy for humanoid soccer skills (dribbling, trapping, shooting) with per-skill motion priors and critics; deployed on a real Noetix E1.", "takeaway_zh": "单一指令条件策略实现人形足球多种技能（带球、停球、射门），每个技能有独立的运动先验与评论家；部署在真实 Noetix E1 上。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.06718.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.26583", "title": "SOLO: Stable Omni-terrain Long-Horizon Perceptive Humanoid Locomotion", "type": "paper", "arxiv": "2608.26583", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://sunpihai-up.github.io/solo/", "topics": [ "locomotion" ], "tags": [ "humanoid", "perceptive-locomotion", "terrain", "distillation", "depth", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Perceptive humanoid locomotion that keeps sharp terrain edges and assigns credit over time during distillation. Reports 96% stepping-stone success and a 1.5 km outdoor walk with one depth camera.", "takeaway_zh": "感知式人形行走：保留地形的清晰边缘，并在蒸馏时做跨时间的信用分配。报告踏石通过率 96%，仅用一个深度相机完成 1.5 公里户外路线。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.26583.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.13850", "title": "Humanoid Hanoi: Investigating Shared Whole-Body Control for Skill-Based Box Rearrangement", "type": "paper", "arxiv": "2602.13850", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/osudrl/Humanoid_Hanoi", "project": "https://osudrl.github.io/Humanoid_Hanoi/", "topics": [ "locomotion" ], "tags": [ "humanoid", "whole-body-control", "skill-composition", "long-horizon", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Humanoid box-rearranging challenge that chains pickup, walking and placing skills. A shared whole-body controller hardened with closed-loop skill data composes best; controller handoffs cause much of the loss.", "takeaway_zh": "人形机器人“汉诺塔”式搬箱挑战，需要反复串联拾取、行走与放置技能。发现用技能闭环数据加固的共享全身控制器组合最稳健，控制器切换是性能损失的重要来源。", "github_stars": 14, "page": "https://embodied-ai-radar.com/e/arxiv-2602.13850.html", "labs": [], "code_license": "" }, { "id": "arxiv-2607.08742", "title": "ContactMimic: Humanoid Object Interaction via Contact Control", "type": "paper", "arxiv": "2607.08742", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/lixinyao11/ContactMimic", "project": "https://lixinyao11.github.io/contactmimic-page", "topics": [ "locomotion" ], "tags": [ "humanoid", "motion-tracking", "contact", "object-interaction", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Humanoid motion tracking that follows explicit part-level contact commands as well as keypoints, so the robot really sits, wipes or pushes, and contact can be switched on or off. 10 sim motions, 5 on hardware.", "takeaway_zh": "人形动作跟踪在关键点之外还跟踪部位级接触指令，让机器人真正坐下、擦拭或推动物体，并可按需产生或抑制接触。仿真 10 种动作，真机 5 种。", "github_stars": 43, "page": "https://embodied-ai-radar.com/e/arxiv-2607.08742.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.01458", "title": "LEGS: Fine-Tuning Teleop-Free VLAs for Humanoid Loco-manipulation in an Embodied Gaussian Splatting World", "type": "paper", "arxiv": "2606.01458", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/hojjunekim/LEGS", "project": "https://hojjunekim.github.io/legs/", "topics": [ "locomotion" ], "tags": [ "humanoid", "gaussian-splatting", "synthetic-data", "vla", "unitree-g1" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Hybrid simulator (mesh foreground over a calibrated 3DGS background) that generates humanoid loco-manipulation demos without teleop. On a G1, VLAs trained only on it match or beat teleop-trained ones.", "takeaway_zh": "混合模拟器：网格前景叠加校准过的 3D 高斯泼溅背景，无需遥操作即可生成人形移动操作演示。在 G1 上，仅用其数据训练的 VLA 达到或超过遥操作数据训练的效果。", "github_stars": 10, "page": "https://embodied-ai-radar.com/e/arxiv-2606.01458.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2602.11321", "title": "ExtremControl: Low-Latency Humanoid Teleoperation with Scaled Velocity Feedforward", "type": "paper", "arxiv": "2602.11321", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://extremcontrol.github.io/", "topics": [ "locomotion" ], "tags": [ "humanoid", "teleoperation", "low-latency", "whole-body-control", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Low-latency humanoid teleoperation: scaled velocity feedforward cuts tracking delay by ~70 ms and link-pose control skips retargeting. End-to-end latency down to 50 ms, enough for juggling and ping-pong ball balancing.", "takeaway_zh": "低延迟人形遥操作：缩放速度前馈让跟踪延迟减少约 70 ms，直接控制末端连杆位姿省去关节重定向。端到端延迟低至 50 ms，可完成抛接与乒乓球平衡等反应型动作。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.11321.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.30645", "title": "VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes", "type": "paper", "arxiv": "2606.30645", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://vision-language-kinematics.github.io/", "topics": [ "locomotion" ], "tags": [ "humanoid", "gaussian-splatting", "synthetic-data", "navigation", "unitree-g1" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Generates 48,000 vision-language-kinematics trajectories in 3DGS-reconstructed rooms with no human input, trains a policy that outputs whole-body kinematics, and runs it on a real G1 via a tracker.", "takeaway_zh": "在 3D 高斯泼溅重建的室内场景中无人工合成 4.8 万条“视觉-语言-运动学”轨迹，训练输出全身运动学轨迹的策略，并通过全身跟踪器在真实 G1 上运行。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.30645.html", "labs": [], "code_license": null }, { "id": "arxiv-2605.27724", "title": "HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning", "type": "paper", "arxiv": "2605.27724", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/NVlabs/humanoidmimicgen", "project": "https://humanoidmimicgen.github.io/", "topics": [ "locomotion" ], "tags": [ "humanoid", "data-generation", "loco-manipulation", "benchmark", "sim" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "MimicGen-style data generation for humanoids: adapts a few whole-body demos to new object poses and links them with locomotion planning, plus a 9-task benchmark. Co-training reports +20% over real data only.", "takeaway_zh": "面向人形机器人的 MimicGen 式数据生成：把少量全身演示适配到新物体位姿，并用移动与操作规划串联，另提出 9 任务基准。协同训练比仅用真机数据提升 20%。", "github_stars": 35, "page": "https://embodied-ai-radar.com/e/arxiv-2605.27724.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2607.02332", "title": "HEFT: Heavy-Payload Full-size Humanoid Teleoperation with Privileged Motion Guidance and Windowed Payload Curriculum", "type": "paper", "arxiv": "2607.02332", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://heft.axell.top/", "topics": [ "locomotion" ], "tags": [ "humanoid", "teleoperation", "payload", "full-size", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Teleoperation for a full-size humanoid carrying heavy loads: privileged motion guidance cleans noisy VR references and a payload curriculum builds robustness. A 175 cm, 65 kg robot tracks motions with up to 24 kg.", "takeaway_zh": "面向全尺寸人形机器人的重载遥操作：特权运动引导修正带噪 VR 参考，窗口化载荷课程提升鲁棒性。175 cm、65 kg 的机器人可携带最多 24 kg 负载跟踪动作。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.02332.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.00374", "title": "Constrained Whole-Body Tracking for Humanoid Robots", "type": "paper", "arxiv": "2606.00374", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/StanfordASL/constrainedmimic", "project": "https://stanfordasl.github.io/constrainedmimic", "topics": [ "locomotion" ], "tags": [ "humanoid", "safety", "control-barrier-functions", "motion-tracking", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Adds runtime constraints (collisions, joint limits, CoM stability) to RL humanoid tracking policies via operational-space control and CBFs at over 1 kHz, even for constraints set after training.", "takeaway_zh": "借助操作空间控制与控制障碍函数，以 1 kHz 以上频率为强化学习人形跟踪策略施加运行时约束（碰撞、关节限位、质心稳定），训练后新增的约束也能满足。", "github_stars": 11, "page": "https://embodied-ai-radar.com/e/arxiv-2606.00374.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2608.20087", "title": "Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking", "type": "paper", "arxiv": "2608.20087", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/noitom-robotics/AdaPT", "project": "https://humanoidtennis.github.io/AdaPT/", "topics": [ "locomotion" ], "tags": [ "humanoid", "sports", "motion-planning", "sim-to-real", "unitree-g1" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns professional tennis serve and rally styles from broadcast video with a planner-plus-tracker design and speed adaptation for sim-to-real. Deployed on a G1 and a 1.7 m Dobot Atom serving without mocap.", "takeaway_zh": "从比赛转播视频学习职业网球发球与对打风格，采用“规划器+跟踪器”分层结构并加入速度自适应缩小仿真-真机差距。部署在 G1 和 1.7 m 的 Dobot Atom 上，无需动捕即可发球。", "github_stars": 112, "page": "https://embodied-ai-radar.com/e/arxiv-2608.20087.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2602.07439", "title": "TextOp: Real-time Interactive Text-Driven Humanoid Robot Motion Generation and Control", "type": "paper", "arxiv": "2602.07439", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/TeleHuman/TextOp", "project": "https://text-op.github.io/", "topics": [ "locomotion" ], "tags": [ "humanoid", "text-to-motion", "diffusion", "motion-tracking", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Real-time text-driven humanoid control: an autoregressive motion diffusion model streams short trajectories from the current command, which can change mid-motion, and a tracking policy executes them on hardware.", "takeaway_zh": "实时文本驱动的人形机器人控制：自回归运动扩散模型根据当前（可随时修改的）文字指令持续生成短时轨迹，再由运动跟踪策略在真机上执行，可流畅切换舞蹈、跳跃等动作。", "github_stars": 553, "page": "https://embodied-ai-radar.com/e/arxiv-2602.07439.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.22998", "title": "TEXEDO : Test Time Scaling for Controller-aware Language-conditioned Humanoid Motion Generation", "type": "paper", "arxiv": "2606.22998", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/JianuoCao/TEXEDO", "project": "https://jianuocao.github.io/TEXEDO/", "topics": [ "locomotion" ], "tags": [ "humanoid", "text-to-motion", "test-time-scaling", "feasibility", "unitree-g1" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Samples many motions from a text-to-motion generator and picks one that a learned verifier says the humanoid can track and that matches the text. Improves tracking and text alignment, incl. on a real G1.", "takeaway_zh": "从文本生成运动模型采样多个候选，用学习到的动态可行性验证器与语义对齐验证器挑选人形机器人既能跟踪又符合文字的动作。仿真与真实 G1 上跟踪精度与文本一致性均提升。", "github_stars": 22, "page": "https://embodied-ai-radar.com/e/arxiv-2606.22998.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2609.00677", "title": "ADAPT: Agile Diffusion Action Priors for Robust and Steerable Online Text-Driven Humanoid Control", "type": "paper", "arxiv": "2609.00677", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://wuyan01.github.io/ADAPT-project/", "topics": [ "locomotion" ], "tags": [ "humanoid", "diffusion-policy", "text-conditioned", "residual-rl", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "End-to-end text-conditioned humanoid control: a diffusion action prior learned from text-labelled state-action data, plus a light residual RL policy for robustness and smooth switching between commands.", "takeaway_zh": "端到端的文本条件人形全身控制：从带文本标签的状态-动作轨迹学习扩散动作先验，再叠加轻量残差强化学习策略提升长时鲁棒性和指令切换的平滑度。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.00677.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.28476", "title": "FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control", "type": "paper", "arxiv": "2606.28476", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://lecar-lab.github.io/FADA-humanoid/", "topics": [ "locomotion" ], "tags": [ "humanoid", "domain-adaptation", "inverse-dynamics", "few-shot", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Planner plus inverse-dynamics student distilled from a privileged policy; at deployment only the IDM is fine-tuned on about 2 minutes of target-domain rollouts. Beats in-context and end-to-end adaptation baselines.", "takeaway_zh": "从特权策略蒸馏出“规划器+逆动力学模型”学生策略；部署时冻结规划器，只用约 2 分钟目标域数据微调逆动力学模型。优于上下文自适应与端到端适应基线。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.28476.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.19635", "title": "Eye, Robot: Learning Expressive Gaze Dynamics via a VLM-Guided Transformer-VAE", "type": "paper", "arxiv": "2610.19635", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "gaze", "transformer-vae", "hri", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Humanoid gaze system: a VLM picks where to look and a Transformer-VAE trained on retargeted human recordings generates coordinated eye, neck and eyelid motion. Lowest joint error on a 2-second benchmark.", "takeaway_zh": "人形机器人注视系统：VLM 选择注视目标，基于重定向人类录像训练的 Transformer-VAE 生成协调的眼、颈与眼睑运动。在 2 秒基准上关节误差最低。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.19635.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.06139", "title": "MotionDisco: Motion Discovery for Extreme Humanoid Loco-Manipulation", "type": "paper", "arxiv": "2606.06139", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "loco-manipulation", "llm", "evolutionary-search", "trajectory-optimization", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Discovers long-horizon, contact-rich humanoid loco-manipulation from scratch: LLM-guided evolutionary search over interaction sequences plus kinodynamic trajectory optimization, then RL tracking on a real humanoid.", "takeaway_zh": "从零发现长时域、多接触的人形机器人移动操作动作：由大模型引导的进化搜索交互序列，配合运动动力学轨迹优化，再训练强化学习跟踪策略部署到真实人形机器人。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.06139.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.09170", "title": "ZeroWBC: Learning Natural Whole-Body Humanoid Interaction from Human Egocentric Data", "type": "paper", "arxiv": "2603.09170", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "whole-body", "egocentric-video", "vlm", "motion-tracking", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Teleop-free humanoid interaction: a fine-tuned VLM generates human whole-body motion from an egocentric image and instruction, retargeted and run by an interaction-aware tracker on a Unitree G1.", "takeaway_zh": "无需遥操作的人形交互：微调后的 VLM 根据第一人称图像和语言指令生成人体全身运动 token，重定向到机器人后由注重交互的跟踪策略在 Unitree G1 上执行。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.09170.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.10132", "title": "TAC-LOCO: Unified Whole-Body Control for Quadrupedal TACtile-Informed LOCO-Manipulation", "type": "paper", "arxiv": "2607.10132", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "quadruped", "loco-manipulation", "tactile", "rl", "real" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "One RL policy for legs, arm and gripper that adds a tactile latent from compliant grippers to proprioception, regulating grasp force and preventing slip. Zero-shot on a Unitree Go2 with a WidowX 250 arm.", "takeaway_zh": "单一强化学习策略同时控制腿、机械臂与夹爪，把柔顺夹爪的触觉潜表示与本体感觉结合，调节握力并防止物体滑落。零样本部署在装有 WidowX 250 机械臂的 Unitree Go2 上。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.10132.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.01126", "title": "Pro-HOI: Perceptive Root-guided Humanoid-Object Interaction", "type": "paper", "arxiv": "2603.01126", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "loco-manipulation", "box-carrying", "digital-twin", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Box-carrying humanoid policy conditioned on a desired root trajectory, with reference motion used only in the reward; a persistent object estimator fuses detection with a digital twin to spot slips and re-grasp.", "takeaway_zh": "人形机器人搬箱策略以期望的根轨迹为条件，参考动作只用于奖励；持续物体估计模块融合实时检测与数字孪生，发现滑落后自动重新抓取。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.01126.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.26201", "title": "OmniContact: Chaining Meta-Skills via Contact Flow for Generalizable Humanoid Loco-Manipulation", "type": "paper", "arxiv": "2606.26201", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "loco-manipulation", "skill-chaining", "dataset" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Uses 'contact flow' (key body trajectories plus binary contact signals) as the interface between a skill-tracking policy and a planner that generates future contact flows; releases a MoCap humanoid-object dataset.", "takeaway_zh": "以“接触流”（关键身体轨迹 + 二值接触信号）作为接口：底层策略跟踪统一的移动操作技能库，高层模块生成未来接触流来串联技能；并发布基于动捕的人-物交互数据集。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.26201.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.31840", "title": "Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data", "type": "paper", "arxiv": "2609.31840", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "badminton", "hierarchical-rl", "motion-prior" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Three-stage hierarchical RL for humanoid badminton: sparse human strokes are augmented into a latent skill space, and a high-level planner composes skills online from the observed shuttle.", "takeaway_zh": "三阶段分层强化学习实现人形机器人打羽毛球：把少量人类击球动作扩充为连续的潜技能空间，高层规划器再根据观测到的羽毛球在线组合技能。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.31840.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.15213", "title": "X-WBC: A Cross-Embodiment Foundation Model for Humanoid Whole-Body Control", "type": "paper", "arxiv": "2609.15213", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "whole-body-control", "cross-embodiment", "foundation-model", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Cross-embodiment humanoid whole-body controller: shared human-motion command tokens and a causal transformer trained on mixed multi-robot rollouts, with small robot-specific modules. Nine sim bodies, four real robots.", "takeaway_zh": "跨本体的人形全身控制基础模型：共享的人体运动指令 token 与在多机器人混合数据上训练的因果 Transformer，加上轻量的机器人专属模块。覆盖 9 种仿真本体与 4 台真机。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.15213.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.18016", "title": "Closing the Loop in Humanoid VLA: Persistent 3D Object Tokens for Verifiable Loco-Manipulation", "type": "paper", "arxiv": "2607.18016", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "vla", "object-tokens", "loco-manipulation", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "POT-VLA keeps role-indexed 3D object records from RGB-D as tokens for the whole-body action expert and for checking whether actions succeeded. On a Unitree G1: 71/80 successes vs 39/80 for GR00T-N1.7.", "takeaway_zh": "POT-VLA 从 RGB-D 维护按角色索引的 3D 物体记录，既作为全身动作专家的输入 token，也用于检验动作是否达成。在 Unitree G1 上成功 71/80 次，GR00T-N1.7 基线为 39/80。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.18016.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.10283", "title": "SwingBot: Learning Whole-Body Brachiation for Humanoid Robots", "type": "paper", "arxiv": "2609.10283", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "brachiation", "rl", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Humanoid brachiation with passive wrist hooks: biomimetic keyframes make release-swing-capture reachable early in training, and a recurrent estimator gives position/contact latents. Continuous bar traversal on hardware.", "takeaway_zh": "带被动腕钩的人形机器人臂荡移动：仿生关键帧让“松手-摆荡-抓杆”在训练早期就能被探索到，循环网络估计位置与接触潜变量。真机实现连续过杆。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.10283.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.01332", "title": "S2M-Trek: From Single to Multi-Sphere Transport via Per-Frame Deep Sets on a Wheel-Legged Robot", "type": "paper", "arxiv": "2606.01332", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "wheel-legged", "loco-manipulation", "deep-sets", "curriculum-rl" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Carries several free-rolling balls on a wheel-legged robot's back with no fences: per-frame Deep Sets respect that ball order can change every frame, which standard history encoders miss and exploit as a shortcut.", "takeaway_zh": "在轮腿机器人背上无围栏地同时运送多个自由滚动的球：逐帧 Deep Sets 编码器考虑到球的顺序每帧都可能变化，而常规历史拼接编码器忽略这一点并把槽位当作捷径。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.01332.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.24466", "title": "FT-WBC: Learning Fault-Tolerant Whole-Body Control for Legged Loco-Manipulation", "type": "paper", "arxiv": "2606.24466", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "legged-manipulator", "fault-tolerance", "whole-body-control", "rl" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Fault-tolerant whole-body control for legged manipulators: a fault estimator finds failed leg joints from proprioception, and a posture module turns arm-driven base requests into safe ones.", "takeaway_zh": "面向腿足机械臂的容错全身控制：故障估计器从下肢本体感觉历史中识别失效关节，姿态适配模块把机械臂策略提出的底盘姿态请求转为安全可执行的指令。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.24466.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.11929", "title": "General Humanoid Whole-Body Control via Pretraining and Fast Adaptation", "type": "paper", "arxiv": "2602.11929", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "whole-body-control", "motion-tracking", "adaptation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "FAST: a general humanoid motion tracker with a small residual adaptation policy under orthogonality and KL constraints for new motions without forgetting, plus center-of-mass-aware terms for balance.", "takeaway_zh": "FAST：通用人形运动跟踪控制器，在正交与 KL 约束下学习轻量残差策略来快速适配新动作且不遗忘，并加入质心相关观测与目标提升平衡。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.11929.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.06564", "title": "Playing Soccer Like Humans Do: Learning Versatile and Agile Humanoid Soccer via Latent Motion Priors", "type": "paper", "arxiv": "2611.06564", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "soccer", "motion-prior", "hierarchical-rl", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "A latent motion prior distilled from human soccer MoCap lets one skill-conditioned policy move, dribble, trap and kick with human-like motion; transfers zero-shot to a Unitree G1.", "takeaway_zh": "从人类足球动捕数据蒸馏出潜在运动先验，使单一技能条件策略能完成移动、带球、停球和射门，动作自然流畅；零样本迁移到 Unitree G1。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.06564.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.28237", "title": "Unleashing Infinite Motion: Scaling Expressive Quadrupedal Motion via Generative Video Priors", "type": "paper", "arxiv": "2606.28237", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "quadruped", "video-generation", "motion-dataset", "motion-tracking" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Uni-Mo generates quadruped motions with an LLM plus a video diffusion model and lifts them to 3D references for tracking policies; the Quad-Imaginarium dataset has 7,488 motions, 96.7% of 392 sampled ran on a Go2.", "takeaway_zh": "Uni-Mo 用大语言模型和视频扩散模型生成四足动作，并提升为 3D 参考轨迹训练跟踪策略；Quad-Imaginarium 数据集含 7488 个动作，抽样的 392 个在 Go2 真机上 96.7% 成功执行。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.28237.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.14554", "title": "MorFiC: Fixing Value Miscalibration for Zero-Shot Quadruped Transfer", "type": "paper", "arxiv": "2603.14554", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "quadruped", "cross-embodiment", "critic", "zero-shot" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "MorFiC finds that a shared critic averages incompatible values across robot bodies and fixes it with multiplicative morphology conditioning; trained on one robot, it transfers zero-shot to seven quadrupeds.", "takeaway_zh": "MorFiC 指出共享价值网络会把不同机身的价值目标混为一谈，并用乘性形态条件化修正该问题；只在一台机器人上训练即可零样本迁移到七种四足机器人。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.14554.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.08780", "title": "Morphology-Conditioned World Model for Cross-Embodiment Quadrupedal Locomotion", "type": "paper", "arxiv": "2604.08780", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "quadruped", "world-model", "cross-embodiment", "zero-shot" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "QWM conditions one latent world model on scale-invariant morphology features and trains policies in imagination; unlike a model-free policy, it transfers zero-shot to unseen quadruped morphologies.", "takeaway_zh": "QWM 以尺度无关的形态特征作为条件训练单一潜空间世界模型，并在想象中训练策略；与无模型策略不同，它能零样本迁移到未见过的四足形态。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.08780.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.15896", "title": "LoComposition: Terrain-Adaptive Energy-Efficient Quadruped Locomotion without Gait Priors", "type": "paper", "arxiv": "2606.15896", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "quadruped", "energy-efficiency", "constraints", "perceptive" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Splits quadruped training into rewards for the task, constraints for limits and energy minimization for gait, with no gait priors; reports 56% lower cost of transport and 96% fewer limit violations, on a Go2.", "takeaway_zh": "将四足训练拆分为：奖励定义任务、约束保证运行限制、能耗最小化决定步态，且不使用任何步态先验；报告运输代价降低 56%、越限减少 96%，并在 Go2 上部署。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.15896.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.07830", "title": "Physics-Guided Biomechanical Gait Adaptation for Humanoid Locomotion on Extreme Sloped Terrains", "type": "paper", "arxiv": "2607.07830", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "slopes", "zmp", "blind" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "HumoSlope trains humanoid walking on steep slopes with a slope-aware ZMP balance prior and a gait adapter that avoids crouching; the blind policy walks outdoor grass slopes up to 32.1 degrees.", "takeaway_zh": "HumoSlope 用基于斜坡支撑面的 ZMP 平衡先验和避免蹲伏步态的适配器训练人形机器人陡坡行走；纯本体感知策略可在户外草坡上行走，坡度最高 32.1 度。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.07830.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.03002", "title": "RPL: Learning Robust Humanoid Perceptive Locomotion on Challenging Terrains", "type": "paper", "arxiv": "2602.03002", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "perceptive", "depth", "distillation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "RPL distills terrain-specific experts into one multi-depth-camera transformer for multi-directional humanoid walking with payloads, with a ray-casting depth renderer about 5x faster than existing pipelines.", "takeaway_zh": "RPL 将各地形专家策略蒸馏为一个多深度相机 Transformer 策略，实现可负重的人形多方向行走，并配套比现有管线快约 5 倍的光线投射深度渲染器。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.03002.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.28960", "title": "Echo in the Steps: Learning Perceptive Humanoid Parkour with Gated Memory", "type": "paper", "arxiv": "2609.28960", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "parkour", "depth", "memory" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Humanoid parkour over sparse footholds from onboard depth: a saliency-guided gated memory keeps useful depth features across frames, and an alternation loss enforces left-right foot placement.", "takeaway_zh": "基于机载深度相机的人形跑酷，跨越稀疏落脚点：显著性引导的门控记忆跨帧保留有用的深度特征，交替损失约束左右脚交替落步。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.28960.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.15733", "title": "MeshMimic: Geometry-Aware Humanoid Motion Learning through 3D Scene Reconstruction", "type": "paper", "arxiv": "2602.15733", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "motion-imitation", "scene-reconstruction", "video" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "MeshMimic reconstructs both human motion and terrain geometry from monocular video, then retargets with contact constraints, so humanoids learn motion-terrain interactions without MoCap.", "takeaway_zh": "MeshMimic 从单目视频中同时重建人体运动与地形几何，再以接触约束进行重定向，使人形机器人无需动捕即可学习动作与地形的交互。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.15733.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.08059", "title": "Perceptive Behavior Foundation Model: Adapting Human Motion Priors to Robot-Centric Terrain", "type": "paper", "arxiv": "2606.08059", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "behavior-foundation-model", "terrain", "motion-tracking" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Perceptive BFM grounds human motion references in the robot's local terrain: synthesized terrain-conformal references train a teacher, and a student tracker adds terrain corrections only when needed.", "takeaway_zh": "Perceptive BFM 让人类动作参考适配机器人所处的局部地形：用合成的贴合地形参考训练教师策略，学生跟踪器仅在必要时加入地形修正。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.08059.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.10288", "title": "MARCH: Model-Assisted Reinforcement Learning for the Perceptive Control of Humanoids over Sparse Footholds", "type": "paper", "arxiv": "2606.10288", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "stepping-stones", "clf", "model-assisted-rl" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "MARCH guides RL with a safe reference from simplified models and a control-Lyapunov reward, then distills to a vision policy; a Unitree G1 crosses sparse footholds with lateral constraints.", "takeaway_zh": "MARCH 用简化模型生成的安全参考轨迹和控制李雅普诺夫奖励引导强化学习，再蒸馏为视觉策略；Unitree G1 可通过带侧向约束的稀疏落脚点。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.10288.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.11553", "title": "CAP: Continuously Adaptive Perception-Blind Humanoid Locomotion via Learned Denoising", "type": "paper", "arxiv": "2609.11553", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "perception-robustness", "depth", "denoising" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "CAP keeps one humanoid policy working as depth degrades: a world-model encoder learns to denoise corrupted depth, alongside a proprioceptive encoder; tested on a G1 indoors and outdoors.", "takeaway_zh": "CAP 让单一人形策略在深度感知退化时仍能工作：世界模型编码器学习对受损深度去噪，并配合本体感知编码器；在 G1 上进行了室内外测试。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.11553.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.07096", "title": "RoboDreamer: Anticipatory Humanoid Locomotion with Predictive State-Space Models", "type": "paper", "arxiv": "2609.07096", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "motion-tracking", "mamba", "distillation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "RoboDreamer distills a humanoid teacher into a Mamba student that sees randomly masked recent observations, so it infers missing state from history; tested in IsaacLab, MuJoCo and on a G1.", "takeaway_zh": "RoboDreamer 将人形教师策略蒸馏为 Mamba 学生策略，训练时随机遮蔽最近观测，使其从历史推断缺失状态；在 IsaacLab、MuJoCo 和 G1 上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.07096.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.16542", "title": "ADAPT: Analytical Disturbance-Aware Policy Training for Humanoid Locomotion", "type": "paper", "arxiv": "2606.16542", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "disturbance-observer", "force-interaction" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "ADAPT feeds a humanoid policy an analytical whole-body disturbance observer that estimates external force/torque from robot dynamics without F/T sensors; more robust to pushes and payloads on a G1.", "takeaway_zh": "ADAPT 为人形策略提供解析式全身扰动观测器，无需力/力矩传感器即可由机器人动力学在线估计外力与力矩；在 G1 上对推搡和负载更鲁棒。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.16542.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.18147", "title": "Walk Like This: Stylized Locomotion for Physics-Based Characters through Example-Driven Diffusion", "type": "paper", "arxiv": "2610.18147", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "stylized-locomotion", "diffusion", "character-animation", "motion-tracking" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Walk Like This: give a style clip and a joystick velocity; a style-conditioned autoregressive diffusion model generates the reference and an RL controller tracks it, in simulation and on a robot.", "takeaway_zh": "Walk Like This：给定风格参考片段和摇杆速度指令，风格条件自回归扩散模型生成运动参考，再由强化学习控制器跟踪，在仿真角色和真实机器人上均可运行。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.18147.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.09492", "title": "Actuator Dynamics Curricula for Narrow-Viability Tasks in Legged Robot Learning", "type": "paper", "arxiv": "2609.09492", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "legged", "curriculum", "actuator", "spot" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Starts RL with stiff joints and anneals stiffness to the identified value as episodes lengthen, which enlarges the viable set; enables a quadruped-to-handstand transition on Spot that otherwise never succeeds.", "takeaway_zh": "强化学习训练初期使用较高的关节刚度，随回合变长逐步退火到辨识值，从而扩大可行状态集；使原本无法学会的 Spot 由四足站立转为倒立的动作得以完成并上真机。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.09492.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.16164", "title": "Trajectory-Level Automatic Curriculum Learning for Legged Locomotion on Unstructured Terrain", "type": "paper", "arxiv": "2608.16164", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "legged", "curriculum", "unstructured-terrain" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "TACL builds curricula from trajectory tasks on unstructured terrain maps: an evaluator learns difficulty for the current policy and a sampler proposes the next tasks; +18.5% on the hardest terrain vs handcrafted.", "takeaway_zh": "TACL 直接在非结构化地形图上以轨迹任务构建课程：评估器学习当前策略的任务难度，采样器据此提出下一批任务；在最难地形上比人工课程成功率高 18.5%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.16164.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.16457", "title": "SHOES: Sim-to-real Hardware-adaptation via On-policy Expansion of Skills for Legged Robots", "type": "paper", "arxiv": "2610.16457", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "quadruped", "adaptation", "sim-to-real", "morphology" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "SHOES adapts a frozen sim-trained quadruped policy to footwear by fine-tuning only small identity-initialized modulation modules on the robot, with a workspace-bounded sampler; Go2 tested with shoes.", "takeaway_zh": "SHOES 冻结仿真训练的四足策略，仅在真机上微调以恒等初始化的轻量调制模块来适应穿鞋带来的形态变化，并用限定工作空间的采样器保证安全；在穿鞋的 Go2 上测试。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.16457.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.06958", "title": "Mind the Phase: Effective Rank and Representation Health in Legged Locomotion", "type": "paper", "arxiv": "2609.06958", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "legged", "representation", "analysis", "rl" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Studies locomotion policies via effective rank of the network Jacobian conditioned on gait phase; normalization and residual connections help, giving up to 2x lower joint jitter on a real Spot.", "takeaway_zh": "以步态相位为条件分析运动策略网络雅可比的有效秩；发现归一化与残差连接更健康，在真实 Spot 上关节抖动最多降低 2 倍。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.06958.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.14840", "title": "Push the Limit: Learning High-Speed Sprinting and Aggressive Cornering on a Full-Scale Humanoid", "type": "paper", "arxiv": "2610.14840", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "locomotion" ], "tags": [ "humanoid", "sprinting", "agility", "amp" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "A full-scale humanoid sprints above 7 m/s and corners with up to 25 degrees of trunk lean, using causal attention over feedback, a heading-direction reward and skill-conditioned motion-prior discriminators.", "takeaway_zh": "全尺寸人形机器人以超过 7 m/s 冲刺，并以最高 25 度躯干侧倾高速过弯；方法包括对反馈的因果注意力、航向奖励以及按技能条件化的运动先验判别器。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.14840.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.09605", "title": "DKWBC: Decoupling Motion Tracking and Balancing for Humanoid Whole-Body Control", "type": "paper", "arxiv": "2611.09605", "year": 2026, "venue": null, "org": "Robbyant, Zhejiang University", "url": null, "code": null, "project": "https://robbyant-research.github.io/DKWBC/", "topics": [ "locomotion", "control" ], "tags": [ "humanoid", "whole-body-control", "inverse-dynamics", "residual-rl" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-09", "takeaway_en": "Splits humanoid whole-body control: inverse-dynamics feedforward plus PD give tracking torques, an RL residual adds only balance corrections; one balance policy carries across motion datasets, payloads and contact.", "takeaway_zh": "拆分人形全身控制：逆动力学前馈加 PD 反馈提供跟踪力矩，强化学习残差只负责平衡修正；同一平衡策略可跨动作数据集、负载与物理交互泛化，仿真与真机均有验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.09605.html", "labs": [ "robbyant" ], "code_license": null }, { "id": "arxiv-2611.09266", "title": "SUGARv2: Learning Diverse Visual Humanoid Loco-Manipulation Skills from Synthesized Interaction References", "type": "paper", "arxiv": "2611.09266", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://tianshuwu.github.io/sugarv2-humanoid/", "topics": [ "locomotion", "manipulation" ], "tags": [ "humanoid", "loco-manipulation", "motion-synthesis", "teacher-student", "sim-to-real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-09", "takeaway_en": "Follow-up to SUGAR: synthesizes robot and object reference motions from reusable task templates, then trains visual policies with one mimic reward and teacher distillation; 91.0% mean sim success on five tasks.", "takeaway_zh": "SUGAR 的后续：用可复用的任务模板合成机器人与物体参考动作，再以统一的模仿奖励和教师蒸馏训练视觉策略；五项任务仿真平均成功率 91.0%，并完成真机迁移。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.09266.html", "labs": [], "code_license": null }, { "id": "arxiv-1803.10122", "title": "World Models", "type": "paper", "arxiv": "1803.10122", "year": 2018, "venue": null, "org": "Google Brain, IDSIA", "url": null, "code": null, "project": "https://worldmodels.github.io/", "topics": [ "world-models" ], "tags": [ "sim", "model-based-rl", "vae", "rnn" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "The classic VAE + RNN agent that learns a compressed model of its environment and trains a controller inside the \"dream\".", "takeaway_zh": "经典的 VAE + RNN 智能体，学习环境的压缩模型并在\"梦境\"中训练控制器。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1803.10122.html", "labs": [ "google-deepmind" ], "code_license": null }, { "id": "arxiv-2301.04104", "title": "Mastering Diverse Domains through World Models", "type": "paper", "arxiv": "2301.04104", "year": 2023, "venue": null, "org": "Google DeepMind", "url": null, "code": "https://github.com/danijar/dreamerv3", "project": null, "topics": [ "world-models" ], "tags": [ "sim", "model-based-rl" ], "robot_types": [], "highlight": true, "rating": 3, "added": null, "takeaway_en": "DreamerV3 — one set of hyperparameters works across 150+ tasks, including collecting diamonds in Minecraft from scratch.", "takeaway_zh": "DreamerV3：一套超参数适用于 150 多个任务，包括从零开始在《我的世界》中挖到钻石。", "github_stars": 3954, "page": "https://embodied-ai-radar.com/e/arxiv-2301.04104.html", "labs": [ "google-deepmind" ], "code_license": "MIT" }, { "id": "arxiv-2206.14176", "title": "DayDreamer: World Models for Physical Robot Learning", "type": "paper", "arxiv": "2206.14176", "year": 2022, "venue": null, "org": "UC Berkeley", "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "real", "quadruped", "arm", "model-based-rl" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Dreamer trained directly on real robots — a quadruped learns to walk in about an hour with no simulator.", "takeaway_zh": "直接在真实机器人上训练 Dreamer——四足机器人无需仿真器，约一小时学会行走。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2206.14176.html", "labs": [ "berkeley" ], "code_license": null }, { "id": "arxiv-2310.06114", "title": "Learning Interactive Real-World Simulators", "type": "paper", "arxiv": "2310.06114", "year": 2023, "venue": null, "org": "UC Berkeley, Google DeepMind", "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "video-generation", "simulator" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "UniSim — an action-conditioned video model used as a simulator to train policies that transfer to the real world.", "takeaway_zh": "UniSim：以动作为条件的视频生成模型充当仿真器，训练出的策略可迁移到真实世界。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2310.06114.html", "labs": [ "berkeley", "google-deepmind" ], "code_license": null }, { "id": "arxiv-2402.15391", "title": "Genie: Generative Interactive Environments", "type": "paper", "arxiv": "2402.15391", "year": 2024, "venue": null, "org": "Google DeepMind", "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "video-generation", "latent-actions" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Learns playable environments and latent actions from unlabeled internet videos alone.", "takeaway_zh": "仅从无标注的网络视频中学习可交互环境与潜在动作。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2402.15391.html", "labs": [ "google-deepmind" ], "code_license": null }, { "id": "arxiv-2501.03575", "title": "Cosmos World Foundation Model Platform for Physical AI", "type": "paper", "arxiv": "2501.03575", "year": 2025, "venue": null, "org": "NVIDIA", "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "video-generation", "open-weights", "synthetic-data" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Open video world foundation models plus tokenizers and data pipelines for generating physical-AI training data.", "takeaway_zh": "开放的视频世界基础模型及分词器与数据管线，用于生成物理 AI 训练数据。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2501.03575.html", "labs": [ "nvidia" ], "code_license": null }, { "id": "arxiv-2506.09985", "title": "V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning", "type": "paper", "arxiv": "2506.09985", "year": 2025, "venue": null, "org": "Meta AI", "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "representation", "planning", "real", "arm" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A self-supervised video encoder, post-trained with a little robot data, plans zero-shot pick-and-place by latent prediction.", "takeaway_zh": "自监督视频编码器经少量机器人数据后训练，通过潜空间预测实现零样本抓放规划。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2506.09985.html", "labs": [ "meta" ], "code_license": null }, { "id": "arxiv-1912.01603", "title": "Dream to Control: Learning Behaviors by Latent Imagination", "type": "paper", "arxiv": "1912.01603", "year": 2019, "venue": null, "org": "University of Toronto, Google", "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "model-based-rl", "latent-dynamics" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "The first Dreamer — learns a latent world model from pixels and trains the policy entirely on imagined rollouts.", "takeaway_zh": "第一代 Dreamer：从像素学习潜空间世界模型，并完全在想象的轨迹上训练策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1912.01603.html", "labs": [ "google-deepmind", "toronto" ], "code_license": null }, { "id": "arxiv-2310.16828", "title": "TD-MPC2: Scalable, Robust World Models for Continuous Control", "type": "paper", "arxiv": "2310.16828", "year": 2023, "venue": null, "org": "UC San Diego", "url": null, "code": "https://github.com/nicklashansen/tdmpc2", "project": null, "topics": [ "world-models", "control" ], "tags": [ "model-based-rl", "mpc", "multitask" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "An implicit (decoder-free) world model planned with MPC; one set of hyperparameters works across many continuous-control tasks and scales to a multitask agent.", "takeaway_zh": "无解码器的隐式世界模型配合 MPC 规划；同一组超参数适用于大量连续控制任务，并可扩展为多任务智能体。", "github_stars": 1000, "page": "https://embodied-ai-radar.com/e/arxiv-2310.16828.html", "labs": [ "ucsd" ], "code_license": "MIT" }, { "id": "arxiv-2411.04983", "title": "DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning", "type": "paper", "arxiv": "2411.04983", "year": 2024, "venue": null, "org": "NYU", "url": null, "code": "https://github.com/gaoyuezhou/dino_wm", "project": null, "topics": [ "world-models" ], "tags": [ "planning", "representation", "offline" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Predicts future DINOv2 patch features instead of pixels, so a world model trained on offline data can plan to new visual goals at test time.", "takeaway_zh": "预测未来的 DINOv2 图像块特征而非像素，使基于离线数据训练的世界模型能在测试时规划到新的视觉目标。", "github_stars": 628, "page": "https://embodied-ai-radar.com/e/arxiv-2411.04983.html", "labs": [ "nyu" ], "code_license": "MIT" }, { "id": "arxiv-2610.26855", "title": "Ropedia W0: Scaling Loco-Manipulation World Models across Embodiments with Human Experience", "type": "paper", "arxiv": "2610.26855", "year": 2026, "venue": null, "org": null, "url": "https://ropedia.github.io/ropedia-w0/", "code": "https://github.com/Ropedia/ropedia-w0", "project": "https://ropedia.github.io/ropedia-w0/", "topics": [ "world-models" ], "tags": [ "egocentric", "human-data", "cross-embodiment", "loco-manipulation" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-10-20", "takeaway_en": "An egocentric world model covering both walking and manipulating, trained on ~10k hours of human video plus ~3k hours of robot data under one shared kinematic action representation.", "takeaway_zh": "同时覆盖移动与操作的第一人称世界模型，在约 1 万小时人类视频和约 3 千小时机器人数据上训练，用统一的运动学表示描述不同本体的动作。", "github_stars": 296, "page": "https://embodied-ai-radar.com/e/arxiv-2610.26855.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2601.07823", "title": "Robotic Video World Models: A Survey of Applications, Research Challenges, Future Directions", "type": "paper", "arxiv": "2601.07823", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/irom-princeton/awesome-robotics-video-world-model-papers", "project": null, "topics": [ "world-models" ], "tags": [ "survey", "video-generation", "awesome-list" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-21", "takeaway_en": "A survey of video world models in robotics - where they replace or complement physics simulators (e.g. deformable objects), their applications, and open challenges - with a maintained paper list.", "takeaway_zh": "机器人视频世界模型综述：它们在哪些场景可替代或补充物理仿真器（如可变形物体）、主要应用与开放挑战，并附持续更新的论文列表。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2601.07823.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-1811.04551", "title": "Learning Latent Dynamics for Planning from Pixels", "type": "paper", "arxiv": "1811.04551", "year": 2019, "venue": "ICML 2019", "org": "Google Brain, DeepMind", "url": null, "code": "https://github.com/google-research/planet", "project": null, "topics": [ "world-models" ], "tags": [ "sim", "model-based-rl", "planning", "latent-dynamics" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "PlaNet — learns a latent dynamics model (the RSSM later reused by Dreamer) from images and plans actions directly in latent space with CEM.", "takeaway_zh": "PlaNet：从图像中学习潜空间动力学模型（即后来 Dreamer 沿用的 RSSM），并用 CEM 直接在潜空间中规划动作。", "github_stars": 1261, "page": "https://embodied-ai-radar.com/e/arxiv-1811.04551.html", "labs": [ "google-deepmind" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2010.02193", "title": "Mastering Atari with Discrete World Models", "type": "paper", "arxiv": "2010.02193", "year": 2021, "venue": "ICLR 2021", "org": "Google Research, DeepMind", "url": null, "code": "https://github.com/danijar/dreamerv2", "project": null, "topics": [ "world-models" ], "tags": [ "sim", "model-based-rl", "discrete-latents" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "DreamerV2 — switches Dreamer to categorical latent variables and learns Atari behaviour purely inside the world model, reaching human-level scores.", "takeaway_zh": "DreamerV2：把 Dreamer 的潜变量改为离散类别分布，仅在世界模型的想象中学习 Atari 策略，达到人类水平。", "github_stars": 1063, "page": "https://embodied-ai-radar.com/e/arxiv-2010.02193.html", "labs": [ "google-deepmind" ], "code_license": "MIT" }, { "id": "arxiv-2209.00588", "title": "Transformers are Sample-Efficient World Models", "type": "paper", "arxiv": "2209.00588", "year": 2023, "venue": "ICLR 2023", "org": "University of Geneva", "url": null, "code": "https://github.com/eloialonso/iris", "project": null, "topics": [ "world-models" ], "tags": [ "sim", "model-based-rl", "transformer", "tokenizer" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "IRIS — a discrete autoencoder turns frames into tokens and a Transformer predicts the next tokens; the agent learns in imagination with only ~2 hours of Atari gameplay.", "takeaway_zh": "IRIS：离散自编码器把画面变成 token，Transformer 预测后续 token；智能体只用约 2 小时的 Atari 游戏经验便在想象中学会策略。", "github_stars": 903, "page": "https://embodied-ai-radar.com/e/arxiv-2209.00588.html", "labs": [], "code_license": "GPL-3.0" }, { "id": "arxiv-2302.00111", "title": "Learning Universal Policies via Text-Guided Video Generation", "type": "paper", "arxiv": "2302.00111", "year": 2023, "venue": "NeurIPS 2023", "org": "MIT, Google", "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "video-generation", "planning", "language" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "UniPi — casts decision making as text-conditioned video generation, then recovers actions from the generated frames with an inverse dynamics model.", "takeaway_zh": "UniPi：把决策问题转化为文本条件的视频生成，再用逆动力学模型从生成的画面中恢复动作。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2302.00111.html", "labs": [ "google-deepmind", "mit" ], "code_license": null }, { "id": "arxiv-2203.04955", "title": "Temporal Difference Learning for Model Predictive Control", "type": "paper", "arxiv": "2203.04955", "year": 2022, "venue": "ICML 2022", "org": "UC San Diego", "url": null, "code": "https://github.com/nicklashansen/tdmpc", "project": null, "topics": [ "world-models" ], "tags": [ "sim", "model-based-rl", "mpc", "planning" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "TD-MPC — a task-oriented latent model used for short-horizon planning, with a learned value function covering the long-term return; the predecessor of TD-MPC2.", "takeaway_zh": "TD-MPC：用面向任务的潜空间模型做短视距规划，由学习到的价值函数估计长期回报；是 TD-MPC2 的前身。", "github_stars": 530, "page": "https://embodied-ai-radar.com/e/arxiv-2203.04955.html", "labs": [ "ucsd" ], "code_license": "MIT" }, { "id": "arxiv-2610.30117", "title": "CW-WAM: A Structured Contact-Wrench World Action Model for Force-Sensitive Manipulation", "type": "paper", "arxiv": "2610.30117", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://cw-wam-project.github.io/", "topics": [ "world-models", "manipulation" ], "tags": [ "world-action-model", "force-control", "contact-rich", "video-prediction" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-22", "takeaway_en": "World action model that jointly predicts video, motion and contact wrench, outputting time-aligned motion–wrench chunks as control references (hybrid motion-force style) for force-sensitive manipulation.", "takeaway_zh": "一个同时预测未来视频、运动与接触力旋量的世界动作模型，借鉴力位混合控制思路，输出时间对齐的运动-力旋量片段作为控制参考，面向仅靠视觉无法感知接触与负载的力敏感操作任务。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.30117.html", "labs": [], "code_license": null }, { "id": "arxiv-2312.13139", "title": "Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation", "type": "paper", "arxiv": "2312.13139", "year": 2024, "venue": "ICLR 2024", "org": "ByteDance Research", "url": null, "code": "https://github.com/bytedance/GR-1", "project": null, "topics": [ "world-models", "manipulation" ], "tags": [ "world-action-model", "video-prediction", "pretraining", "gpt" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "GR-1 — a GPT-style model that predicts both actions and future frames; pre-training on Ego4D video prediction before robot fine-tuning lifts CALVIN unseen-scene success from 53.3% to 85.4% (as reported).", "takeaway_zh": "GR-1：GPT 式模型同时预测动作与未来画面；先在 Ego4D 上做视频预测预训练、再用机器人数据微调，论文报告 CALVIN 未见场景成功率从 53.3% 提升到 85.4%。", "github_stars": 309, "page": "https://embodied-ai-radar.com/e/arxiv-2312.13139.html", "labs": [ "bytedance" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2412.14803", "title": "Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations", "type": "paper", "arxiv": "2412.14803", "year": 2025, "venue": "ICML 2025", "org": "Tsinghua University, Shanghai Qi Zhi Institute, RobotEra", "url": null, "code": "https://github.com/roboterax/video-prediction-policy", "project": null, "topics": [ "world-models", "manipulation" ], "tags": [ "world-action-model", "video-diffusion", "diffusion-policy", "dexterous" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "VPP — fine-tunes Stable Video Diffusion on robot and human videos and feeds its one-pass predictive features to a diffusion policy; reports 4.33 average length on CALVIN ABC→D and real Franka / dexterous-hand results.", "takeaway_zh": "VPP：在机器人与人类操作视频上微调 Stable Video Diffusion，只做一次前向提取\"预测性\"视觉特征再交给扩散策略；论文报告 CALVIN ABC→D 平均完成长度 4.33，并有 Franka 与灵巧手真机实验。", "github_stars": 418, "page": "https://embodied-ai-radar.com/e/arxiv-2412.14803.html", "labs": [ "tsinghua", "qizhi", "robotera" ], "code_license": "MIT" }, { "id": "arxiv-2503.00200", "title": "Unified Video Action Model", "type": "paper", "arxiv": "2503.00200", "year": 2025, "venue": "RSS 2025", "org": "Stanford University", "url": null, "code": "https://github.com/ShuangLI59/unified_video_action", "project": null, "topics": [ "world-models", "manipulation" ], "tags": [ "world-action-model", "video-generation", "diffusion", "umi" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "UVA — one shared latent for video and actions with two light diffusion heads; masked training lets the same model act as policy, video generator and forward/inverse dynamics model, while keeping policy inference fast.", "takeaway_zh": "UVA：视频与动作共享一个潜表示，配两个轻量扩散头解码；通过掩码训练，同一模型既能当策略，也能做视频生成和正/逆动力学，且策略推理仍然较快。", "github_stars": 417, "page": "https://embodied-ai-radar.com/e/arxiv-2503.00200.html", "labs": [ "stanford" ], "code_license": "MIT" }, { "id": "arxiv-2506.21539", "title": "WorldVLA: Towards Autoregressive Action World Model", "type": "paper", "arxiv": "2506.21539", "year": 2025, "venue": null, "org": "Alibaba DAMO Academy, Zhejiang University", "url": null, "code": "https://github.com/alibaba-damo-academy/WorldVLA", "project": null, "topics": [ "world-models", "vla" ], "tags": [ "world-action-model", "autoregressive", "libero" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "One autoregressive model that is both a VLA policy and an image-predicting world model; the two help each other on LIBERO, and an attention mask hiding earlier actions curbs error build-up in action chunks.", "takeaway_zh": "单个自回归模型同时充当 VLA 策略与预测未来图像的世界模型，二者在 LIBERO 上相互促进；论文还发现自回归生成动作块会累积误差，用屏蔽先前动作的注意力掩码可以缓解。", "github_stars": 1144, "page": "https://embodied-ai-radar.com/e/arxiv-2506.21539.html", "labs": [ "alibaba" ], "code_license": "" }, { "id": "arxiv-2508.05635", "title": "Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation", "type": "paper", "arxiv": "2508.05635", "year": 2025, "venue": "ICLR 2026", "org": "AgiBot, NUS, Beihang University", "url": null, "code": "https://github.com/AgibotTech/Genie-Envisioner-V1", "project": null, "topics": [ "world-models", "manipulation", "sim" ], "tags": [ "world-action-model", "video-diffusion", "neural-simulator", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Genie Envisioner (AgiBot) — a video-diffusion world model (GE-Base) plus a flow-matching action decoder (GE-Act), an action-conditioned neural simulator (GE-Sim) and the EWMBench benchmark in one platform.", "takeaway_zh": "智元 Genie Envisioner：一个平台集成视频扩散世界模型 GE-Base、流匹配动作解码器 GE-Act、动作条件神经仿真器 GE-Sim，以及世界模型评测基准 EWMBench。", "github_stars": 592, "page": "https://embodied-ai-radar.com/e/arxiv-2508.05635.html", "labs": [ "agibot", "nus" ], "code_license": "" }, { "id": "arxiv-2610.30991", "title": "UniFlowWAM: A Unified World-Action Model with Visual Semantics and 4D Geometry", "type": "paper", "arxiv": "2610.30991", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://kksham111.github.io/UniFlowWAM_ProjectPage/", "topics": [ "world-models", "vla" ], "tags": [ "world-action-model", "3d-flow", "4d-geometry" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-23", "takeaway_en": "World action model that brings scene-level 3D flow (tracked 3D points) inside the policy rather than as a separate module or extra head, so 4D geometry and visual semantics are modelled together with actions.", "takeaway_zh": "世界动作模型：把场景级三维流（跟踪的三维点）纳入策略内部，而不是作为独立模块或额外输出头，让 4D 几何、视觉语义与动作一起建模。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.30991.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.18774", "title": "GreenWAM: A World Foundation Model for Robotics", "type": "paper", "arxiv": "2610.18774", "year": 2026, "venue": null, "org": "Sber Robotics Center", "url": null, "code": "https://github.com/greenvla/GreenWAM", "project": "https://greenvla.github.io/GreenWAM", "topics": [ "world-models", "vla" ], "tags": [ "world-action-model", "cross-embodiment", "humanoid", "technical-report" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": "2026-10-23", "takeaway_en": "One world model that is also the policy, across 20 robot families mapped to a shared joint-space action format; reports strong WorldArena, RoboTwin 2.0 and LIBERO-plus results and a one-step distilled humanoid policy.", "takeaway_zh": "同一个世界模型兼作策略，用共享的关节坐标动作空间覆盖 20 类机器人；论文报告其在 WorldArena、RoboTwin 2.0、LIBERO-plus 上表现突出，并蒸馏出可实时运行的单步人形机器人策略。", "github_stars": 12, "page": "https://embodied-ai-radar.com/e/arxiv-2610.18774.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.30320", "title": "Self-Distilling Future Video Representations for World Action Models", "type": "paper", "arxiv": "2610.30320", "year": 2026, "venue": null, "org": "KAIST AI", "url": null, "code": "https://github.com/suhyeok-jang/FRED", "project": "https://huiwon-jang.github.io/FRED/", "topics": [ "world-models" ], "tags": [ "world-action-model", "video-diffusion", "self-distillation", "human-to-robot", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-24", "takeaway_en": "FRED trains a world action model to predict better one-step future video features via self-distillation plus history context; reports large gains on long-horizon, out-of-domain and human-video-only tasks.", "takeaway_zh": "FRED 通过自蒸馏并引入历史观测，让世界动作模型在单步去噪中预测出更好的未来视频表征；报告在长时程、域外环境以及只有人类视频示范的任务上提升明显。", "github_stars": 12, "page": "https://embodied-ai-radar.com/e/arxiv-2610.30320.html", "labs": [ "kaist" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.30136", "title": "Agentic World Action Models", "type": "paper", "arxiv": "2610.30136", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/ZhuoyangLiu2005/agentic-wam", "project": "https://agentic-wam.github.io/", "topics": [ "world-models", "manipulation" ], "tags": [ "world-action-model", "agentic", "code-as-policy", "skill-library", "bimanual", "sim", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-10-25", "takeaway_en": "AW-0 jointly generates code, future video and actions in one mixture-of-transformers; code structures planning and becomes reusable skills. Reports gains over VLA and WAM baselines on RoboTwin 2.0 and real two-arm tasks.", "takeaway_zh": "AW-0 在同一个混合 Transformer 中联合生成代码、未来视频和机器人动作三路输出：代码既组织多步预测与控制，又沉淀为可复用技能库；报告在 RoboTwin 2.0 和真机双臂任务上优于 VLA 与 WAM 基线。", "github_stars": 168, "page": "https://embodied-ai-radar.com/e/arxiv-2610.30136.html", "labs": [], "code_license": "BSD-2-Clause" }, { "id": "arxiv-2610.31474", "title": "Skeleton-Grounded Latent Action Model for Robot World Models", "type": "paper", "arxiv": "2610.31474", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://aopolin-lv.github.io/SkeLAM/", "topics": [ "world-models" ], "tags": [ "latent-action", "video-world-model", "droid", "policy-evaluation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-25", "takeaway_en": "SkeLAM splits latent actions into a robot-skeleton-grounded kinematic part and a visual residual, so world-model rollouts follow robot controls; on DROID reports 19.5% lower end-effector error than DreamDojo.", "takeaway_zh": "SkeLAM 把潜在动作拆成由机器人骨架监督的运动学部分和保留其余视觉动态的残差部分，使世界模型生成更贴合机器人控制指令；报告在 DROID 上末端轨迹误差比 DreamDojo 低 19.5%，预测成功率与真机评测一致。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31474.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.32965", "title": "RynnUnify: Towards Unified Embodied Brain", "type": "paper", "arxiv": "2610.32965", "year": 2026, "venue": null, "org": "Alibaba DAMO Academy", "url": null, "code": "https://github.com/alibaba-damo-academy/RynnUnify", "project": "https://alibaba-damo-academy.github.io/RynnUnify.github.io/", "topics": [ "world-models", "vla", "perception" ], "tags": [ "unified-model", "video-prediction", "visual-planning", "world-action-model", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "A 16B model interleaving video prediction and language reasoning for embodied understanding and long-horizon visual planning, plus a world-action extension for control; reports 51.5% on 20 RoboCasa365 tasks.", "takeaway_zh": "16B 统一具身模型，交替进行视频预测与语言推理，兼顾具身理解与长程视觉规划，并扩展出用于机器人控制的世界-动作版本；另提出 Rynn-VP、Rynn-GEU 评测，报告在 20 个 RoboCasa365 组合任务上成功率 51.5%。", "github_stars": 78, "page": "https://embodied-ai-radar.com/e/arxiv-2610.32965.html", "labs": [ "alibaba" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2505.12705", "title": "DreamGen: Unlocking Generalization in Robot Learning through Video World Models", "type": "paper", "arxiv": "2505.12705", "year": 2025, "venue": "CoRL 2025", "org": "NVIDIA", "url": null, "code": "https://github.com/NVIDIA/GR00T-Dreams", "project": "https://research.nvidia.com/labs/gear/dreamgen", "topics": [ "world-models", "learning", "locomotion" ], "tags": [ "synthetic-data", "video-world-model", "inverse-dynamics", "humanoid", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Fine-tunes video world models on a robot to dream new demos, labels them with latent actions or an inverse dynamics model, and trains policies on them; a humanoid learns 20+ new behaviors from one-scene teleop data.", "takeaway_zh": "把视频世界模型微调到目标机器人上“做梦”生成新示范，再用隐动作或逆动力学模型补上动作标签用于训练策略；人形机器人仅凭单一场景的遥操作数据学会 20 多种新行为。", "github_stars": 631, "page": "https://embodied-ai-radar.com/e/arxiv-2505.12705.html", "labs": [ "nvidia" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.28613", "title": "Physical Canvas: A Vision Tactile World Model For Robot Policy Evaluation And Learning", "type": "paper", "arxiv": "2610.28613", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://zebinx.github.io/PhysCanvas_HomePage/", "topics": [ "world-models", "tactile" ], "tags": [ "visuotactile", "video-generation", "policy-evaluation", "grpo", "real", "contact-rich" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Action-conditioned world model that predicts multi-view RGB and tactile images together in one video space, post-trained for state consistency; used for closed-loop policy evaluation and GRPO updates.", "takeaway_zh": "动作条件的视触觉世界模型：在统一的视频空间中同时预测多视角 RGB 与触觉图像，并经一致性后训练保证与真实任务状态吻合；可用于闭环策略评估和 GRPO 策略更新。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.28613.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.26813", "title": "PICA-WAM: Visual Perturbation Invariance via Cross-Appearance Self-Alignment in World Action Models", "type": "paper", "arxiv": "2610.26813", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/YudeZou/PICA-WAM-release", "project": null, "topics": [ "world-models", "vla" ], "tags": [ "wam", "robustness", "self-supervised", "ema-teacher", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Auxiliary training task for world action models: an EMA teacher sees the clean frame, the online model sees a perturbed one and aligns its action features. Reports better robustness for four WAMs.", "takeaway_zh": "为世界-动作模型加一个自监督辅助任务：EMA 教师看原始画面，在线模型看外观扰动后的画面并对齐其动作表征；报告在 RoboTwin 2.0、LIBERO-Plus 等基准上提升四种 WAM 对视觉扰动的鲁棒性，且不损失原有性能。", "github_stars": 9, "page": "https://embodied-ai-radar.com/e/arxiv-2610.26813.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.35850", "title": "OSWAM: An Effective World Action Model with One-Step Action Generation", "type": "paper", "arxiv": "2610.35850", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/PolyU-VCLab/OSWAM", "project": "https://gwenzhang.github.io/OSWAM/", "topics": [ "world-models", "vla" ], "tags": [ "wam", "one-step", "real-time", "distillation", "driving", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "World action model that acts in one diffusion step: 'imagination tokens' replace random-noise initialisation as an action prior. Reports ~3× speedup over direct-policy WAMs, 40 ms latency on an RTX 5090.", "takeaway_zh": "单步扩散即可生成动作的世界动作模型：用“想象 token”代替随机噪声初始化，为动作提供先验。报告比直接策略式 WAM 快约 3 倍，编译后在 RTX 5090 上延迟低至 40 毫秒，覆盖自动驾驶与具身控制。", "github_stars": 21, "page": "https://embodied-ai-radar.com/e/arxiv-2610.35850.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.34663", "title": "Video World Models Need Predictable Latents", "type": "paper", "arxiv": "2610.34663", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/mattia-scardecchia/predictable-latents", "project": null, "topics": [ "world-models", "learning" ], "tags": [ "video-world-model", "latent-space", "autoencoder", "policy-evaluation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Train the video world model's autoencoder for temporal predictability, not just reconstruction, with a past-conditioned decoder. Reports up to 5× cheaper inference, 7× faster training and lower long-horizon / OOD error.", "takeaway_zh": "视频世界模型的潜空间不应只为重建而训练：加入时间可预测性目标并配合以历史为条件的解码器。报告推理成本最多降低 5 倍、训练快 7 倍以上，长时域与分布外误差降低 15–24%，与真实策略评测更一致。", "github_stars": 7, "page": "https://embodied-ai-radar.com/e/arxiv-2610.34663.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.30781", "title": "DeXplicit: Making Physical State Explicit in Dexterous Manipulation World Models", "type": "paper", "arxiv": "2610.30781", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Abecid/DeXplicit", "project": "https://dexplicit.github.io", "topics": [ "world-models", "manipulation" ], "tags": [ "dexterous", "3d-dynamics", "video-generation", "hand-object" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Dexterous-manipulation world model that first predicts object motion and deformation in metric 3D from a hand trajectory, then renders video. Reports 27–40% lower moved-point error than PointWorld.", "takeaway_zh": "灵巧操作世界模型：先根据手部轨迹在度量 3D 空间中显式预测物体运动与形变，再据此渲染视频。报告在 HOT3D、OakInk2、PhysTwin 上运动点误差比 PointWorld 低 27–40%，两步因果生成大幅提速。", "github_stars": 10, "page": "https://embodied-ai-radar.com/e/arxiv-2610.30781.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.27387", "title": "Training Controllable World Models via Action Influence Maximization", "type": "paper", "arxiv": "2610.27387", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "world-models", "learning" ], "tags": [ "world-model", "video-generation", "data-collection", "controllability", "active-learning" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Why action-conditioned video models ignore actions: expert demos carry too little action influence. AIM actively collects data to raise it; 500 such episodes + 3k demos match a 300k-demo model on controllability.", "takeaway_zh": "解释动作条件视频世界模型为何忽略动作：专家示教的“动作影响力”太低，模型容量再大也无法弥补。AIM 主动采集数据提高这一指标；报告 500 条主动采集数据加 3000 条示教即可在末端可控性上追平 30 万条专家数据。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.27387.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.25763", "title": "TrajWAM: An Efficient Trajectory-Guided World Action Model for Dynamic Robotic Manipulation", "type": "paper", "arxiv": "2610.25763", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://kevingeng3.github.io/TrajWAM/", "topics": [ "world-models", "manipulation" ], "tags": [ "wam", "dynamic-manipulation", "video-generation", "distillation", "one-step", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "World action model for moving targets: forecasts the object's future masks, steers a video model with a Trajectory ControlNet, and distills the pipeline to one-step video-action inference to cut latency.", "takeaway_zh": "面向动态目标的世界动作模型：先预测目标物体的未来掩码轨迹，用 Trajectory ControlNet 引导视频模型生成与轨迹一致的未来，再通过解耦蒸馏实现一步视频-动作推理以降低延迟。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25763.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.37091", "title": "GAWM: A Compact Latent World-Action Model for Multi-Task Robot Manipulation", "type": "paper", "arxiv": "2610.37091", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/gaoxiang12/ga-wm", "project": null, "topics": [ "world-models", "manipulation" ], "tags": [ "wam", "dinov3", "latent-prediction", "robotwin", "libero", "bimanual" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Compact (<100M) world-action model: predicts frozen-DINOv3 tokens at two future horizons and decodes action chunks, with no robot or video pretraining. Reports beating billion-parameter WAMs on RoboTwin 2.0.", "takeaway_zh": "参数不到 1 亿的紧凑世界动作模型：在冻结 DINOv3 特征空间中预测两个未来时刻的 token 并直接回归动作块，无需机器人或视频预训练；报告在 RoboTwin 2.0 上超过十亿参数级 WAM。", "github_stars": 32, "page": "https://embodied-ai-radar.com/e/arxiv-2610.37091.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.36823", "title": "TAPESTRY: A Single Backbone for Video Generation and Robot Control", "type": "paper", "arxiv": "2610.36823", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://tapestry-wam.github.io", "topics": [ "world-models", "manipulation" ], "tags": [ "wam", "video-diffusion", "action-tokens", "real", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Turns a pretrained video diffusion transformer into a policy by encoding each action step as one patch in the video latent space; at test time it outputs an action chunk in one DDIM step (84 ms), no video generation.", "takeaway_zh": "把预训练视频扩散 Transformer 改造成策略：每个时间步的动作编码为视频潜空间中的一个 patch，联合训练视频、视频+动作与纯动作三种扩散目标；测试时一步 DDIM 生成动作块（84 毫秒），无需生成视频。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36823.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.36791", "title": "RynnWorld-Latent: A Cross-Embodiment World Model Conditioned on Universal Latent Actions", "type": "paper", "arxiv": "2610.36791", "year": 2026, "venue": null, "org": "Alibaba DAMO Academy", "url": null, "code": "https://github.com/alibaba-damo-academy/RynnWorld-Latent", "project": "https://alibaba-damo-academy.github.io/RynnWorld-Latent.github.io", "topics": [ "world-models", "learning" ], "tags": [ "world-model", "latent-actions", "cross-embodiment", "human-video", "data-generation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Cross-embodiment video world model conditioned on RynnLAM's 3D latent actions, pretrained on 23 datasets (~553M frames) of human, robot and sim video; its generated data improves LIBERO and real-robot policies.", "takeaway_zh": "以 RynnLAM 的三维潜在动作为统一条件接口的跨本体视频世界模型，在 23 个数据集（约 5.5 亿帧）的人类、机器人和仿真视频上预训练；生成的数据提升了 LIBERO 与真机策略成功率。", "github_stars": 17, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36791.html", "labs": [ "alibaba" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.36068", "title": "Rollback: Experience Backtracking in World Models for Policy Improvement", "type": "paper", "arxiv": "2610.36068", "year": 2026, "venue": null, "org": "AgiBot", "url": null, "code": null, "project": "https://rollback-wm.github.io/", "topics": [ "world-models", "learning" ], "tags": [ "world-model", "counterfactual", "policy-improvement", "real-world-rl", "lora", "real" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-10-28", "takeaway_en": "Rewinds an action-conditioned world model to key decision points to try alternative actions, turning outcomes into action-level supervision; real data keeps correcting the model. Reports +10.7–11.9 pts on 3 real tasks.", "takeaway_zh": "把动作条件世界模型当作反事实引擎：回到关键决策点尝试其他动作并对比结果，得到动作级监督而无需单独的价值模型；真机经验再通过 LoRA 持续修正世界模型。报告在 3 个真实任务上比最强基线高 10.7–11.9 个百分点。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36068.html", "labs": [ "agibot" ], "code_license": null }, { "id": "arxiv-2610.36840", "title": "Trace-WAM: Learning to Act via 3D Motion Prediction for World Action Models", "type": "paper", "arxiv": "2610.36840", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://trace-wam.github.io/", "topics": [ "world-models", "manipulation" ], "tags": [ "wam", "3d-motion", "point-tracking", "mixture-of-transformers", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Adds future 3D point-motion prediction as an auxiliary task to a world action model (video/motion/action MoT); inference stays one visual pass. Reports gains over Fast-WAM with few demos and under shift.", "takeaway_zh": "在世界动作模型中加入未来三维点运动预测作为辅助目标（视频/运动/动作三专家 MoT），推理仍只需一次视觉前向；报告在少量示范和分布偏移下明显优于 Fast-WAM（RoboCasa-GR1、LIBERO、LIBERO-Plus）。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36840.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.36624", "title": "Adapting Video Models for Robot Control via Motion-guided Asymmetric Prediction", "type": "paper", "arxiv": "2610.36624", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://ismty0805.github.io/mogap/", "topics": [ "world-models", "manipulation" ], "tags": [ "wam", "video-diffusion", "auxiliary-loss", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "MoGAP adds a training-only auxiliary path to world action models that corrupts moving regions more than static ones, focusing visual supervision on motion. Reports GR-1 tabletop gains (Fast-WAM 38.8% → 47.5%).", "takeaway_zh": "MoGAP 为世界动作模型增加一条仅在训练时使用的辅助预测分支：对运动区域加更重的噪声、以静态区域为上下文重建，使视觉监督聚焦于动态部分；报告在 GR-1 桌面基准上 Fast-WAM 从 38.8% 提升到 47.5%，Cosmos3-Nano 从 52.3% 到 62.3%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36624.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.35877", "title": "Calibrated for Which Policy? Conformal Uncertainty for Fixed World Models Under Intervention", "type": "paper", "arxiv": "2610.35877", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/noumenal-ai/conformal-realrobot-lab", "project": null, "topics": [ "world-models", "learning" ], "tags": [ "world-model", "uncertainty", "conformal-prediction", "distribution-shift", "theory", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Shows a fixed world model's uncertainty can break when the deployment policy changes; reweights conformal calibration by a learned state-action ratio. Reports 90% sets covering 55.0% vs 90.9% on real Franka data.", "takeaway_zh": "指出部署策略一变，固定世界模型的不确定性校准就可能失效；用域分类器学到的状态-动作密度比做加权共形校准，并给出覆盖率保证（部分用 Lean 4 形式化验证）。报告在真实 Franka 轨迹上，普通 90% 共形集合仅覆盖 55.0%，加权后为 90.9%。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2610.35877.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.31429", "title": "PhysTrace: Interpretable Temporal Evaluation of Physical Consistency in Video Generative Models", "type": "paper", "arxiv": "2610.31429", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/PhysTrace/PhysTrace", "project": null, "topics": [ "world-models", "datasets" ], "tags": [ "evaluation", "video-models", "intuitive-physics", "mllm", "training-free" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-29", "takeaway_en": "Training-free physics check for video models: an MLLM states history-based expectations and PhysTrace marks when the video breaks them; adds IntPhys2-Timing (reports up to 70.7% hit rate).", "takeaway_zh": "无需训练的视频模型物理一致性评测：由多模态大模型根据历史帧给出物理预期，再定位视频中违背预期的时间段并给出可解释描述；新增带违规区间标注的 IntPhys2-Timing（报告命中率最高 70.7%），面向依赖视频模型的世界动作模型。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31429.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.15768", "title": "LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies", "type": "paper", "arxiv": "2606.15768", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/RLinf/LaWAM", "project": "https://rlinf.github.io/LaWAM/", "topics": [ "world-models", "vla" ], "tags": [ "wam", "latent-world-model", "latent-action", "libero", "robotwin", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "World action model without video generation: predicts future features in a vision foundation model's latent space as policy subgoals. Reports 98.6% on LIBERO and up to 24× lower latency than pixel WAMs.", "takeaway_zh": "不生成视频的世界动作模型：在视觉基础模型的特征空间中训练潜动作模型，预测未来观测特征作为“潜在视觉子目标”来条件化动作生成。报告 LIBERO 98.6%、RoboTwin 91.22%，每个动作块推理 187 ms，比像素级 WAM 延迟低最多 24 倍。", "github_stars": 163, "page": "https://embodied-ai-radar.com/e/arxiv-2606.15768.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.39278", "title": "WorldFlow: Joint Video and 3D Motion Prediction via Large-Scale Pretraining", "type": "paper", "arxiv": "2610.39278", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/worldflow-rbs/worldflow", "project": "https://worldflow-rbs.github.io/", "topics": [ "world-models" ], "tags": [ "video-world-model", "3d-trajectories", "rectified-flow", "mixture-of-transformers", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "World model that jointly denoises future video and 3D point trajectories in one Mixture-of-Transformers, trained on 1.44M samples; adds WorldFlowBench, reports 54% lower ADE than the best baseline on SpatialCog.", "takeaway_zh": "同时预测未来视频与查询点 3D 轨迹的生成式世界模型：3D 运动以与视频网格对齐的位移 token 表示，由统一的 Mixture-of-Transformers 在 rectified flow 目标下联合去噪；在 144 万样本上训练，新建 WorldFlowBench，报告 SpatialCog 上 ADE 比最强外部基线降低 54.2%。", "github_stars": 19, "page": "https://embodied-ai-radar.com/e/arxiv-2610.39278.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.42146", "title": "Hierarchical World Models with Implicit Dynamics", "type": "paper", "arxiv": "2610.42146", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://id-wm.github.io", "topics": [ "world-models" ], "tags": [ "hierarchical", "planning", "mpc", "inverse-dynamics" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "ID-WM pairs a high-level model that samples reachable transitions with a low-level world model used as an inverse-dynamics solver. Reports a 135% gain over single-level world-model MPC on long-horizon tasks.", "takeaway_zh": "ID-WM 将状态可达性与局部控制解耦：高层模型在状态流形上采样可行转移，低层世界模型充当逆动力学求解器，把子目标落实为精确动作。报告比单层世界模型 MPC 提升 135%，比最优策略与扩散规划器提升 38.5%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.42146.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.40748", "title": "WorldBridge: Bridging Video and Action for Task Generalization in World Action Models", "type": "paper", "arxiv": "2610.40748", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://worldbridge-jd.github.io/", "topics": [ "world-models" ], "tags": [ "wam", "task-generalization", "robotwin", "flow-matching" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Separates video and action generalization in WAMs: on RoboTwin 2.0 unseen tasks, adding action-free videos lifts success from 25.07% to 55.90%, and one labeled demo per task to 70.70%.", "takeaway_zh": "拆解世界动作模型（WAM）的视频泛化与动作泛化：在 RoboTwin 2.0 的分布外任务上，加入无动作标签视频使成功率由 25.07% 升到 55.90%，每任务再加一条带标签演示升到 70.70%。WorldBridge 全数据设定报告 96.23%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.40748.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.15922", "title": "World Action Models are Zero-shot Policies", "type": "paper", "arxiv": "2602.15922", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/dreamzero0/dreamzero", "project": "https://dreamzero0.github.io/", "topics": [ "world-models" ], "tags": [ "world-action-model", "video-diffusion", "zero-shot" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. DreamZero: a World Action Model on a pretrained video diffusion backbone, jointly predicting video and actions, so it learns skills from heterogeneous robot data without repetitive demonstrations.", "takeaway_zh": "CoRL 2026 Oral。DreamZero：基于预训练视频扩散骨干的世界动作模型，联合预测视频与动作，可从异构机器人数据学习技能，无需重复示范。", "github_stars": 2764, "page": "https://embodied-ai-radar.com/e/arxiv-2602.15922.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.14564", "title": "Chunk-wise Model-based Policy Optimization", "type": "paper", "arxiv": "2610.14564", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/song-wx/CMPO", "project": "https://song-wx.github.io/cmpo.github.io/", "topics": [ "world-models" ], "tags": [ "chunk-world-model", "model-based-rl" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Model-based RL that uses a chunk world model as a differentiable training-time evaluator, targeting compounding error, inference latency and BPTT instability.", "takeaway_zh": "CoRL 2026 Spotlight。将分块世界模型用作训练期可微评估器的模型式强化学习，针对累积误差、推理延迟与 BPTT 不稳定问题。", "github_stars": 5, "page": "https://embodied-ai-radar.com/e/arxiv-2610.14564.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.09499", "title": "Targeting World Models to Compromise Robot Learning Pipelines", "type": "paper", "arxiv": "2606.09499", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "security", "data-poisoning" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Shows world models are a stealthy data-poisoning entry point: compromised policies can be deployed even when the ground-truth training data looks safe.", "takeaway_zh": "CoRL 2026 Spotlight。展示世界模型可成为隐蔽的数据投毒入口：即使真实训练数据看似安全，部署的策略仍可能被破坏。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.09499.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.39232", "title": "Seeing and Imagining as One: Unified Video Generation and 4D Perception for Embodied World Modeling", "type": "paper", "arxiv": "2610.39232", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://gensight-rbs.github.io/", "topics": [ "world-models" ], "tags": [ "world-model", "video-diffusion", "4d-perception" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "GenSight puts future video generation and dense perception (depth, embodiment segmentation, 2D flow) into one video diffusion transformer with a shared prediction space.", "takeaway_zh": "GenSight 把未来视频生成与稠密感知（深度、本体分割、二维光流）放进同一个视频扩散 Transformer，共用一个预测空间。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.39232.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.01844", "title": "OpenNWM: A Generalist Navigation World Model with Latent Action Pretraining", "type": "paper", "arxiv": "2611.01844", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/OpenSpace-Lab/OpenNWM", "project": null, "topics": [ "world-models", "navigation" ], "tags": [ "navigation", "latent-actions", "pretraining", "dataset" ], "robot_types": [ "mobile", "aerial" ], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Learns latent actions from action-free video, then aligns them with physical motion in post-training. Comes with NavAnywhere: 17.5M observations from 15 sources across ground robots, people and drones.", "takeaway_zh": "先从无动作标注的视频中学习潜在动作，再在后训练阶段对齐真实运动。配套数据集 NavAnywhere 含 17.5M 条视觉观测，来自 15 个来源，覆盖地面机器人、人和无人机。", "github_stars": 13, "page": "https://embodied-ai-radar.com/e/arxiv-2611.01844.html", "labs": [], "code_license": "" }, { "id": "arxiv-2611.01039", "title": "pico-WAM: Evaluating Counterfactual Prediction and Planning Error in a Compact World Action Model", "type": "paper", "arxiv": "2611.01039", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/keivalya/pico-wam", "project": null, "topics": [ "world-models" ], "tags": [ "world-action-model", "diagnostics", "push-t", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Is a failing world action model limited by proposals or scoring? On Push-T, simulator costs in place of learned scores add 12.2 points of coverage; simulator states in proposals show no detected gain.", "takeaway_zh": "世界动作模型失败时，问题出在动作提议还是轨迹评分？在 Push-T 上，把学习的评分换成仿真器代价可提升 12.2 个百分点的覆盖率，而在提议阶段换用仿真器状态未见增益。提供可复现的诊断流程。", "github_stars": 12, "page": "https://embodied-ai-radar.com/e/arxiv-2611.01039.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.01677", "title": "Xiaomi Auto World Model 2.0: Causal World Models for Real-World Driving and Embodied Intelligence", "type": "paper", "arxiv": "2611.01677", "year": 2026, "venue": null, "org": "Xiaomi", "url": null, "code": null, "project": "https://xiaomiautoworldmodel.github.io/CausalWorldModel/", "topics": [ "world-models" ], "tags": [ "driving", "streaming", "3d-gaussians", "physical-ai" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "A streaming multi-view causal world model. Its 3D Gaussian reconstruction feeds renderings back as scene context for long rollouts; the model targets closed-loop simulation, training and world action models.", "takeaway_zh": "流式多视角因果世界模型：用 3D 高斯重建的渲染结果作为场景上下文来维持长时生成，面向闭环仿真、闭环训练和世界动作模型应用。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.01677.html", "labs": [ "xiaomi" ], "code_license": null }, { "id": "arxiv-2606.20781", "title": "World Action Models: A Survey", "type": "paper", "arxiv": "2606.20781", "year": 2026, "venue": null, "org": "NUS", "url": null, "code": null, "project": "https://world-action-models.github.io/", "topics": [ "world-models" ], "tags": [ "survey", "world-action-model" ], "robot_types": [], "highlight": true, "rating": 2, "added": null, "takeaway_en": "Separates WAMs from world models, video generators and VLAs, then sorts work by what is generated (rendered, latent or none) and how it is built; concludes the field is moving toward generating less of the future.", "takeaway_zh": "厘清 WAM 与世界模型、视频生成模型、VLA 的区别，并按生成内容（渲染、潜空间或不生成）和架构分类；结论是领域正走向少生成未来，只保留控制所需。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.20781.html", "labs": [ "nus" ], "code_license": null }, { "id": "arxiv-2611.05377", "title": "DreamX: Video Diffusion Model is a Universal Latent Action Learner", "type": "paper", "arxiv": "2611.05377", "year": 2026, "venue": null, "org": "KAIST", "url": null, "code": "https://github.com/cvlab-kaist/DreamX", "project": "https://cvlab-kaist.github.io/DreamX/", "topics": [ "world-models" ], "tags": [ "video-diffusion", "latent-action", "cross-embodiment" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "Reuses a pretrained video diffusion model to extract compact latent actions (1,200 to 4 tokens per frame) that act as a shared interface across humans, robots and environments, including zero-shot action transfer.", "takeaway_zh": "复用预训练视频扩散模型提取紧凑潜动作（每帧 1,200 压到 4 个 token），作为人、机器人和不同环境之间的共享接口，并支持零示教动作迁移。", "github_stars": 6, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05377.html", "labs": [ "kaist" ], "code_license": "" }, { "id": "arxiv-2611.04870", "title": "PrimeSim: A Generalist World Model Simulator from Large-Scale Cross-Embodiment Demonstrations", "type": "paper", "arxiv": "2611.04870", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/PrimebotTech/PrimeSim", "project": "https://primebottech.github.io/PrimeSim-website/", "topics": [ "world-models", "sim" ], "tags": [ "action-conditioned", "multiview", "open-weights" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "An autoregressive multiview world model running at 10.1 FPS that adapts to held-out robots in 100 to 200 gradient steps; its authors release weights and code and note that fine local calibration still trails.", "takeaway_zh": "自回归多视角世界模型，运行 10.1 FPS，100 至 200 步梯度即可适配未见平台；作者开放权重和代码，并说明细粒度局部标定仍落后于同类。", "github_stars": 8, "page": "https://embodied-ai-radar.com/e/arxiv-2611.04870.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.02800", "title": "Cosmos 3: Omnimodal World Models for Physical AI", "type": "paper", "arxiv": "2606.02800", "year": 2026, "venue": null, "org": "NVIDIA", "url": null, "code": "https://github.com/nvidia/cosmos", "project": "https://research.nvidia.com/labs/cosmos-lab/cosmos3/", "topics": [ "world-models" ], "tags": [ "world-foundation-model", "open-weights" ], "robot_types": [], "highlight": true, "rating": 2, "added": "2026-11-04", "takeaway_en": "A family of omnimodal models that handles language, image, video, audio and action in one mixture-of-transformers, folding VLMs, video generators, world simulators and world-action models into a single framework.", "takeaway_zh": "全模态世界模型家族，用一个 Mixture-of-Transformers 同时处理语言、图像、视频、音频和动作，把 VLM、视频生成、世界模拟器和世界动作模型并入同一框架。", "github_stars": 12340, "page": "https://embodied-ai-radar.com/e/arxiv-2606.02800.html", "labs": [ "nvidia" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2511.00062", "title": "World Simulation with Video Foundation Models for Physical AI", "type": "paper", "arxiv": "2511.00062", "year": 2025, "venue": null, "org": "NVIDIA", "url": null, "code": "https://github.com/nvidia-cosmos/cosmos-predict2.5", "project": null, "topics": [ "world-models", "sim" ], "tags": [ "world-foundation-model", "open-weights" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "A flow-based world foundation model unifying Text2World, Image2World and Video2World, released at 2B and 14B, with Cosmos-Transfer2.5 for Sim2Real translation.", "takeaway_zh": "基于流的世界基础模型，统一 Text2World、Image2World 和 Video2World，发布 2B 和 14B 两种规模，并配有用于 Sim2Real 转换的 Cosmos-Transfer2.5。", "github_stars": 1394, "page": "https://embodied-ai-radar.com/e/arxiv-2511.00062.html", "labs": [ "nvidia" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2602.06949", "title": "DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos", "type": "paper", "arxiv": "2602.06949", "year": 2026, "venue": null, "org": "NVIDIA", "url": null, "code": "https://github.com/NVIDIA/DreamDojo", "project": "https://dreamdojo-world.github.io/", "topics": [ "world-models" ], "tags": [ "human-video", "dexterous", "latent-action" ], "robot_types": [ "hand" ], "highlight": true, "rating": 2, "added": "2026-11-04", "takeaway_en": "A foundation world model pretrained on 44k hours of egocentric human video, using continuous latent actions as proxies for missing labels, then post-trained on small robot datasets.", "takeaway_zh": "在 4.4 万小时第一视角人类视频上预训练的基础世界模型，用连续潜动作代替缺失的动作标签，再用小规模机器人数据后训练。", "github_stars": 1132, "page": "https://embodied-ai-radar.com/e/arxiv-2602.06949.html", "labs": [ "nvidia" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2601.21998", "title": "Causal World Modeling for Robot Control", "type": "paper", "arxiv": "2601.21998", "year": 2026, "venue": null, "org": "Robbyant", "url": null, "code": "https://github.com/Robbyant/lingbot-va", "project": "https://technology.robbyant.com/lingbot-va", "topics": [ "world-models", "vla" ], "tags": [ "wam", "autoregressive", "real" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "LingBot-VA: an autoregressive diffusion framework that learns frame prediction and policy together, with shared video-action latents, closed-loop rollout and asynchronous inference.", "takeaway_zh": "LingBot-VA：同时学习帧预测和策略的自回归扩散框架，具有视频动作共享潜空间、闭环推演和异步推理。", "github_stars": 1979, "page": "https://embodied-ai-radar.com/e/arxiv-2601.21998.html", "labs": [ "robbyant" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2609.07398", "title": "OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining", "type": "paper", "arxiv": "2609.07398", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/OpenWAM-Official/OpenWAM", "project": "https://openwam-official.github.io/", "topics": [ "world-models", "vla" ], "tags": [ "wam", "open-weights", "framework" ], "robot_types": [], "highlight": true, "rating": 2, "added": "2026-11-04", "takeaway_en": "An open research stack that splits world-action models into composable modules, then runs controlled studies on what to inherit from video priors and how world and action learning interact.", "takeaway_zh": "开放研究栈，把世界动作模型拆成可组合模块，并通过对照实验研究该从视频先验继承什么、世界学习与动作学习如何相互作用。", "github_stars": 1189, "page": "https://embodied-ai-radar.com/e/arxiv-2609.07398.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2603.16666", "title": "Fast-WAM: Do World Action Models Need Test-time Future Imagination?", "type": "paper", "arxiv": "2603.16666", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/yuantianyuan01/FastWAM", "project": "https://yuantianyuan01.github.io/FastWAM/", "topics": [ "world-models", "vla" ], "tags": [ "wam", "latency" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "Asks whether WAMs need future imagination at test time: keeps video co-training during training but skips video generation at inference, separating the two effects in a controlled comparison.", "takeaway_zh": "追问世界动作模型在测试时是否需要想象未来：训练时保留视频联合训练，推理时跳过视频生成，以对照实验区分两者的作用。", "github_stars": 1654, "page": "https://embodied-ai-radar.com/e/arxiv-2603.16666.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2603.17240", "title": "GigaWorld-Policy: An Efficient Action-Centered World--Action Model", "type": "paper", "arxiv": "2603.17240", "year": 2026, "venue": null, "org": "GigaAI", "url": null, "code": "https://github.com/open-gigaai/giga-world-policy", "project": "https://gigaai-research.github.io/GigaWorld-Policy/", "topics": [ "world-models", "vla" ], "tags": [ "wam", "action-centered" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "An action-centered WAM that predicts actions from the observation and makes video generation optional, so motion accuracy depends less on forecast quality and inference is cheaper.", "takeaway_zh": "以动作为中心的世界动作模型：由观测预测动作，视频生成变为可选，动作精度不再依赖视频预测质量，推理开销也更低。", "github_stars": 1412, "page": "https://embodied-ai-radar.com/e/arxiv-2603.17240.html", "labs": [], "code_license": "" }, { "id": "arxiv-2603.23376", "title": "ABot-PhysWorld: Interactive World Foundation Model for Robotic Manipulation with Physics Alignment", "type": "paper", "arxiv": "2603.23376", "year": 2026, "venue": null, "org": "AMAP", "url": null, "code": "https://github.com/amap-cvlab/ABot-PhysWorld", "project": null, "topics": [ "world-models" ], "tags": [ "world-foundation-model", "physics", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A 14B action-controllable video model trained on three million manipulation clips, with DPO-style post-training that suppresses unphysical results such as penetration, plus the EZSbench benchmark.", "takeaway_zh": "14B 动作可控视频模型，基于 300 万条操作片段训练，用 DPO 式后训练抑制穿模等非物理现象，并提出 EZSbench 基准。", "github_stars": 373, "page": "https://embodied-ai-radar.com/e/arxiv-2603.23376.html", "labs": [], "code_license": "" }, { "id": "arxiv-2608.26103", "title": "Zero-WAM: In-Context World-Action Modeling from Human Videos for Open-Ended Task Generalization", "type": "paper", "arxiv": "2608.26103", "year": 2026, "venue": null, "org": "Robbyant", "url": null, "code": "https://github.com/robbyant-research/Zero-WAM", "project": "https://robbyant-research.github.io/Zero-WAM/", "topics": [ "world-models" ], "tags": [ "wam", "human-video", "zero-shot" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Performs unseen manipulation tasks by following a human video placed in context, with no parameter update; it turns robot trajectories into matched human videos to get paired training data.", "takeaway_zh": "通过跟随上下文中的人类视频执行未见过的操作任务，无需更新参数；并把机器人轨迹自动转成匹配的人类视频，以获得成对训练数据。", "github_stars": 453, "page": "https://embodied-ai-radar.com/e/arxiv-2608.26103.html", "labs": [ "robbyant" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2604.27792", "title": "Motubrain: An Advanced World Action Model for Robot Control", "type": "paper", "arxiv": "2604.27792", "year": 2026, "venue": null, "org": "ShengShu", "url": null, "code": null, "project": "https://www.shengshu.com/en/motubrain", "topics": [ "world-models" ], "tags": [ "wam", "multiview" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A three-stream world action model under a UniDiffuser formulation; one model covers policy, world modeling, video generation and inverse dynamics, and it scales to video-only and cross-embodiment data.", "takeaway_zh": "基于 UniDiffuser 的三流世界动作模型，一个模型同时覆盖策略、世界建模、视频生成和逆动力学，并可利用纯视频和跨形态数据。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.27792.html", "labs": [], "code_license": null }, { "id": "arxiv-2605.00078", "title": "Being-H0.7: A Latent World-Action Model from Egocentric Videos", "type": "paper", "arxiv": "2605.00078", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "world-models", "vla" ], "tags": [ "wam", "latent", "human-video" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A latent world-action model that adds future-aware latent queries to a VLA-style policy and trains them with a future-informed branch, without generating future frames.", "takeaway_zh": "潜空间世界动作模型，在 VLA 式策略中加入面向未来的潜查询，并用利用未来信息的分支训练，全程不生成未来帧。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2605.00078.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.01955", "title": "WALL-WM: Carving World Action Modeling at the Event Joints", "type": "paper", "arxiv": "2606.01955", "year": 2026, "venue": null, "org": "X Square Robot", "url": null, "code": "https://github.com/X-Square-Robot/wall-x", "project": "https://x2robot.com/pages/wm", "topics": [ "world-models", "vla" ], "tags": [ "wam", "event-based" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "Uses semantically coherent action events rather than fixed-length chunks as the unit of learning, to resolve the timescale mismatch between language, vision and control.", "takeaway_zh": "用语义完整的动作事件代替固定长度动作块作为学习单元，以解决语言、视觉和控制之间的时间尺度不匹配。", "github_stars": 1320, "page": "https://embodied-ai-radar.com/e/arxiv-2606.01955.html", "labs": [ "x-square" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2609.31394", "title": "InternW0-$Δ$: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data", "type": "paper", "arxiv": "2609.31394", "year": 2026, "venue": null, "org": "InternRobotics", "url": null, "code": "https://github.com/InternRobotics/InternW0-Delta", "project": "https://internrobotics.github.io/InternW0-Delta/", "topics": [ "world-models" ], "tags": [ "wam", "open-data" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A WAM combining a video expert, an action expert, a frozen VLM and training-only 4D geometric distillation, pretrained on a heterogeneous corpus with over 20K hours of open data.", "takeaway_zh": "结合视频专家、动作专家、冻结 VLM 和仅训练时使用的 4D 几何蒸馏的世界动作模型，在含 2 万多小时开放数据的异构语料上预训练。", "github_stars": 140, "page": "https://embodied-ai-radar.com/e/arxiv-2609.31394.html", "labs": [ "shanghai-ai-lab" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.10270", "title": "Video Prediction Policy 2: Predict Better, Act Better", "type": "paper", "arxiv": "2610.10270", "year": 2026, "venue": null, "org": "RobotEra", "url": null, "code": "https://github.com/roboterax/video-prediction-policy-2", "project": "https://robert-gyj.github.io/video-prediction-policy-2", "topics": [ "world-models" ], "tags": [ "wam", "video-prediction" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Blames WAM motion errors on video bases not tuned for manipulation and on naive action add-ons, then continues pretraining the video model on manipulation clips with event-level captions.", "takeaway_zh": "认为世界动作模型的运动错误来自未针对操作优化的视频底座和粗糙的动作模块，因此在带事件级描述的操作视频上继续预训练视频模型。", "github_stars": 370, "page": "https://embodied-ai-radar.com/e/arxiv-2610.10270.html", "labs": [ "robotera" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.10515", "title": "RoboJEPA: Scaling Robotic Latent World Models", "type": "paper", "arxiv": "2610.10515", "year": 2026, "venue": null, "org": "Meta", "url": null, "code": "https://github.com/facebookresearch/robo_jepa", "project": "https://robojepa.github.io/", "topics": [ "world-models" ], "tags": [ "jepa", "scaling-laws" ], "robot_types": [], "highlight": true, "rating": 2, "added": "2026-11-04", "takeaway_en": "A JEPA world model trained across 12 robot embodiments; its imagination error follows a power law in compute, so model quality can be predicted beyond the fitted scale.", "takeaway_zh": "跨 12 种机器人形态训练的 JEPA 世界模型；其想象误差随算力服从幂律，因此可以预测超出拟合规模时的模型质量。", "github_stars": 81, "page": "https://embodied-ai-radar.com/e/arxiv-2610.10515.html", "labs": [ "meta" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2505.15659", "title": "FLARE: Robot Learning with Implicit World Modeling", "type": "paper", "arxiv": "2505.15659", "year": 2025, "venue": null, "org": "NVIDIA", "url": null, "code": null, "project": "https://research.nvidia.com/labs/gear/flare", "topics": [ "world-models", "vla" ], "tags": [ "latent", "diffusion", "humanoid" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Adds a few tokens to a VLA-style diffusion transformer and aligns them with latents of future observations, so the policy anticipates the future without a separate world model.", "takeaway_zh": "只在 VLA 式扩散 Transformer 中加入少量 token，并让它们对齐未来观测的潜表示，使策略无需独立世界模型就能预判未来。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2505.15659.html", "labs": [ "nvidia" ], "code_license": null }, { "id": "arxiv-2606.17030", "title": "Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation", "type": "paper", "arxiv": "2606.17030", "year": 2026, "venue": null, "org": "Alibaba", "url": null, "code": null, "project": "https://pre.qwen.ai/blog?id=qwen-robotworld", "topics": [ "world-models" ], "tags": [ "language-conditioned", "world-foundation-model" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A language-conditioned video world model using language as a shared action interface across manipulation, driving, indoor navigation and human-to-robot transfer.", "takeaway_zh": "语言条件视频世界模型，把自然语言作为操作、驾驶、室内导航和人到机器人迁移的统一动作接口。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.17030.html", "labs": [ "alibaba" ], "code_license": null }, { "id": "arxiv-2607.11643", "title": "Xiaomi-Robotics-U0: Unified Embodied Synthesis with World Foundation Model", "type": "paper", "arxiv": "2607.11643", "year": 2026, "venue": null, "org": "Xiaomi", "url": null, "code": "https://github.com/XiaomiRobotics/Xiaomi-Robotics-U0", "project": null, "topics": [ "world-models" ], "tags": [ "world-foundation-model", "autoregressive" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A 38-billion-parameter multimodal autoregressive model that treats embodied generation as an extension of image and video generation, keeping the prior of the pretrained world foundation model.", "takeaway_zh": "380 亿参数的多模态自回归模型，把具身生成视为图像和视频生成的延伸，以保留预训练世界基础模型的先验。", "github_stars": 189, "page": "https://embodied-ai-radar.com/e/arxiv-2607.11643.html", "labs": [ "xiaomi" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.28128", "title": "PhysisForcing: Physics Reinforced World Simulator for Robotic Manipulation", "type": "paper", "arxiv": "2606.28128", "year": 2026, "venue": null, "org": "Peking University", "url": null, "code": "https://github.com/DAGroup-PKU/PhysisForcing", "project": "https://dagroup-pku.github.io/PhysisForcing.github.io/", "topics": [ "world-models" ], "tags": [ "physics", "video-world-model" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A training framework that improves the physical consistency of video world simulators by focusing supervision on physics-informative regions at pixel and semantic levels.", "takeaway_zh": "训练框架，把监督集中在具有物理信息的区域，在像素和语义两个层面提升视频世界模拟器的物理一致性。", "github_stars": 137, "page": "https://embodied-ai-radar.com/e/arxiv-2606.28128.html", "labs": [ "pku" ], "code_license": "MIT" }, { "id": "arxiv-2512.10675", "title": "Evaluating Gemini Robotics Policies in a Veo World Simulator", "type": "paper", "arxiv": "2512.10675", "year": 2025, "venue": null, "org": "Google DeepMind", "url": null, "code": null, "project": "https://veo-robotics.github.io/", "topics": [ "world-models" ], "tags": [ "policy-evaluation", "video-world-model" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Uses a Veo-based generative system for robot policy evaluation across nominal performance, out-of-distribution generalization, and physical and semantic safety.", "takeaway_zh": "用基于 Veo 的生成系统评估机器人策略，覆盖常规性能、分布外泛化以及物理和语义安全。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2512.10675.html", "labs": [ "google-deepmind" ], "code_license": null }, { "id": "arxiv-2511.11520", "title": "Scalable Policy Evaluation with Video World Models", "type": "paper", "arxiv": "2511.11520", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": "https://miscsubmission.github.io/world_model_policy_eval/", "topics": [ "world-models" ], "tags": [ "policy-evaluation", "video-world-model" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Adds action conditioning to pretrained video generators to get learned world models for policy evaluation, as an alternative to costly real tests and hand-built simulation.", "takeaway_zh": "给预训练视频生成模型加入动作条件，得到可用于策略评估的世界模型，替代昂贵的真机测试和手工搭建的仿真。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2511.11520.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.22078", "title": "Do World Action Models Generalize Better than VLAs? A Robustness Study", "type": "paper", "arxiv": "2603.22078", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Robot-Robustness/RoboTwin2.0-Plus", "project": "https://robot-robustness.github.io/RoboTwin2.0-Plus/", "topics": [ "world-models", "vla" ], "tags": [ "robustness", "wam" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A robustness study that compares world action models against VLAs under perturbation, to test the claim that explicit future prediction gives better generalization.", "takeaway_zh": "在扰动下对比世界动作模型与 VLA 的鲁棒性研究，用于检验显式未来预测能带来更好泛化这一说法。", "github_stars": 16, "page": "https://embodied-ai-radar.com/e/arxiv-2603.22078.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2609.16074", "title": "World-Action Models for Robot Learning and Control: A Survey", "type": "paper", "arxiv": "2609.16074", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://rcl-robotics.github.io/Awesome-World-Action-Models", "topics": [ "world-models" ], "tags": [ "survey", "wam" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A robotics-oriented WAM survey that separates WAMs from world models, model-based RL and reactive VLAs, with a taxonomy of representations, architectures, training and data.", "takeaway_zh": "面向机器人的世界动作模型综述，厘清其与世界模型、基于模型的强化学习和反应式 VLA 的区别，并按表示、架构、训练和数据分类。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.16074.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.00836", "title": "From World Models to World Action Models: A Concise Tutorial for Robotics", "type": "paper", "arxiv": "2607.00836", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/clearlab-sustech/WorldModelSurvey", "project": null, "topics": [ "world-models" ], "tags": [ "tutorial", "wam" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A concise tutorial rather than a survey: defines world models and WAMs for robotics and compares spatial intelligence models, JEPA and Cosmos in a single frame.", "takeaway_zh": "简明教程而非综述：界定机器人领域的世界模型和世界动作模型，并在统一视角下比较空间智能模型、JEPA 和 Cosmos。", "github_stars": 54, "page": "https://embodied-ai-radar.com/e/arxiv-2607.00836.html", "labs": [], "code_license": "" }, { "id": "arxiv-2508.17600", "title": "GWM: Towards Scalable Gaussian World Models for Robotic Manipulation", "type": "paper", "arxiv": "2508.17600", "year": 2025, "venue": "ICCV 2025", "org": null, "url": null, "code": "https://github.com/Gaussian-World-Model/gaussianwm", "project": "https://gaussian-world-model.github.io/", "topics": [ "world-models" ], "tags": [ "gaussian-splatting", "3d" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Predicts future states by propagating Gaussian primitives under robot actions with a latent DiT and 3D VAE, usable for representation learning and as a neural simulator.", "takeaway_zh": "用潜空间 DiT 和 3D VAE 预测高斯基元在机器人动作下的传播，可用于表征学习，也可作为神经模拟器。", "github_stars": 145, "page": "https://embodied-ai-radar.com/e/arxiv-2508.17600.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2506.23126", "title": "ParticleFormer: A 3D Point Cloud World Model for Multi-Object, Multi-Material Robotic Manipulation", "type": "paper", "arxiv": "2506.23126", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": "https://suninghuang19.github.io/particleformer_page/", "topics": [ "world-models" ], "tags": [ "3d", "point-cloud", "deformable" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A Transformer point cloud world model for interactions among rigid, deformable and flexible materials, trained from real perception data without elaborate scene reconstruction.", "takeaway_zh": "Transformer 点云世界模型，可描述刚体、可变形和柔性材料之间的交互，直接用真实感知数据训练，无需复杂场景重建。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2506.23126.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.28993", "title": "ST-WAM: Semantic-Temporal World Action Model for Robust Manipulation under Visual Distribution Shifts", "type": "paper", "arxiv": "2607.28993", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Thu-WangMX/ST-WAM-Semantic-Temporal-World-Action-Model", "project": "https://thu-wangmx.github.io/st-wam/", "topics": [ "world-models" ], "tags": [ "wam", "robustness", "dinov3" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Finds that WAM futures can hallucinate training-domain content under visual shift, and uses DINOv3 as the shared representation for future prediction and history retrieval to keep actions robust.", "takeaway_zh": "发现视觉偏移下世界动作模型预测的未来会幻想出训练域内容，于是用 DINOv3 作为未来预测和历史检索的共享表示，以保持动作鲁棒。", "github_stars": 25, "page": "https://embodied-ai-radar.com/e/arxiv-2607.28993.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2605.27491", "title": "GE-Sim 2.0: A Roadmap Towards Comprehensive Closed-loop Video World Simulators for Robotic Manipulation", "type": "paper", "arxiv": "2605.27491", "year": 2026, "venue": null, "org": "AgiBot", "url": null, "code": "https://github.com/AgibotTech/GE-Sim-V2", "project": "https://ge-sim-v2.github.io/", "topics": [ "world-models", "sim" ], "tags": [ "video-world-model", "closed-loop", "simulator" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A closed-loop video simulator retrained on thousands of hours of real robot data, with a state expert, a world judge that scores rollouts, and 25-frame rollouts in 2.3 seconds on one H100.", "takeaway_zh": "闭环视频世界模拟器，用数千小时真机数据重新训练，加入状态专家和为推演打分的世界评判器，在单张 H100 上 2.3 秒生成 25 帧推演。", "github_stars": 166, "page": "https://embodied-ai-radar.com/e/arxiv-2605.27491.html", "labs": [ "agibot" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2405.18418", "title": "Hierarchical World Models as Visual Whole-Body Humanoid Controllers", "type": "paper", "arxiv": "2405.18418", "year": 2024, "venue": null, "org": null, "url": null, "code": "https://github.com/nicklashansen/puppeteer", "project": "https://nicklashansen.com/rlpuppeteer", "topics": [ "world-models", "locomotion" ], "tags": [ "humanoid", "hierarchical", "visual-control" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Hierarchical world model for visual whole-body humanoid control: a high-level agent commands a low-level one, with no reward design or skill primitives.", "takeaway_zh": "用于视觉全身人形控制的分层世界模型：高层智能体指挥低层智能体，不依赖奖励设计或技能基元。", "github_stars": 220, "page": "https://embodied-ai-radar.com/e/arxiv-2405.18418.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2611.06627", "title": "Interactive World Simulation with Cosmos for Physical AI", "type": "paper", "arxiv": "2611.06627", "year": 2026, "venue": null, "org": "NVIDIA", "url": null, "code": "https://github.com/nvidia/cosmos", "project": "https://research.nvidia.com/labs/cosmos-lab/cosmos-simulation", "topics": [ "world-models", "sim" ], "tags": [ "world-foundation-model", "real-time", "video", "simulation" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-05", "takeaway_en": "Adapts Cosmos models for real-time interactive simulation: streams observations in response to actions, or renders them from an external simulator's state. Shown on manipulation, video transfer and multi-view AV.", "takeaway_zh": "将 Cosmos 模型用于实时交互仿真的统一框架：根据动作流式生成观测，或依据外部仿真器的物理状态生成观测。示例涵盖操作、可控视频迁移和多视角自动驾驶。", "github_stars": 12340, "page": "https://embodied-ai-radar.com/e/arxiv-2611.06627.html", "labs": [ "nvidia" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2611.06120", "title": "ReactWorld: Structuring Interaction Responses for Embodied Video Generation", "type": "paper", "arxiv": "2611.06120", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://metaengine-world.github.io/ReactWorld/", "topics": [ "world-models" ], "tags": [ "video", "contact", "generative" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Video world model that first predicts where and how objects will respond to contact, then feeds that into object features and temporal attention; reports 3.29 points above PhysisForcing on RBench.", "takeaway_zh": "视频世界模型：先预测物体对接触的响应位置和方式，再反馈到物体特征与时序注意力；报告在 RBench 上比 PhysisForcing 高 3.29 分。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.06120.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.05448", "title": "Jev with Foresight: Safe Language-Guided Autonomous Driving via a Language World Model", "type": "paper", "arxiv": "2611.05448", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://ys-qu.github.io/jev-ad-website", "topics": [ "world-models", "navigation" ], "tags": [ "driving", "language-world-model", "safety" ], "robot_types": [ "vehicle" ], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Predicts how nearby vehicles respond to each candidate action and feeds those consequences to a typed decision model; reports replayed NGSIM collisions falling from 48.2% to 2.8%.", "takeaway_zh": "预测每个候选动作下周围车辆的反应，再把后果交给类型化决策模型比较；报告在回放的 NGSIM 片段中碰撞率从 48.2% 降至 2.8%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05448.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.31716", "title": "Does LeWorldModel Preserve Local Action Geometry?", "type": "paper", "arxiv": "2610.31716", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://geometry.vasi.boo/", "topics": [ "world-models" ], "tags": [ "jepa", "evaluation", "action-sensitivity" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Compares how a pretrained LeWorldModel and the simulator respond to small action changes; their most sensitive directions differ by a median of about 36–42° at the smallest scale, so evaluation should be scale-specific.", "takeaway_zh": "比较预训练 LeWorldModel 与仿真器对微小动作变化的响应；最小尺度下最敏感方向的中位夹角相差约 36–42°，说明评估应按尺度进行。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31716.html", "labs": [], "code_license": null }, { "id": "arxiv-2512.15840", "title": "Large Video Planner Enables Generalizable Robot Control", "type": "paper", "arxiv": "2512.15840", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/buoyancy99/large-video-planner", "project": "https://www.boyuan.space/large-video-planner/", "topics": [ "world-models", "vla" ], "tags": [ "video-generation", "video-planning", "foundation-model", "zero-shot", "real", "open-data" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Builds a robot foundation model on video instead of language: an open video planner trained on internet-scale human video makes zero-shot plans that are turned into robot actions. Model and dataset are released.", "takeaway_zh": "以视频而非语言为主干构建机器人基础模型：在互联网规模人类视频上训练开源视频规划器，零样本生成视频计划再转为机器人动作；模型与数据集均已开源。", "github_stars": 265, "page": "https://embodied-ai-radar.com/e/arxiv-2512.15840.html", "labs": [], "code_license": "" }, { "id": "arxiv-2606.19531", "title": "ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?", "type": "paper", "arxiv": "2606.19531", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/yuyangalin/ImageWAM", "project": "https://zhangwenyao1.github.io/ImageWAM/", "topics": [ "world-models", "manipulation" ], "tags": [ "wam", "image-editing", "flow-matching", "efficiency", "real", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Do world action models need video generation? ImageWAM conditions a flow-matching action expert on image-editing KV caches instead. Reports beating VLA baselines at 1/6 the FLOPs and 1/4 the latency of video WAMs.", "takeaway_zh": "追问世界动作模型是否真需要视频生成：ImageWAM 改用图像编辑模型去噪时的 KV 缓存来条件化流匹配动作专家；报告优于 VLA 基线，计算量为视频 WAM 的 1/6、延迟为 1/4。", "github_stars": 189, "page": "https://embodied-ai-radar.com/e/arxiv-2606.19531.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2606.08242", "title": "Light-WAM: Efficient World Action Models with State-Fusion Action Decoding", "type": "paper", "arxiv": "2606.08242", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/L1ziang/Light-WAM", "project": null, "topics": [ "world-models", "manipulation" ], "tags": [ "wam", "efficiency", "action-chunking", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Lightweight world action model: compact video backbone with future supervision in a downsampled latent, and an action expert that pools several layers to predict chunks in one pass. Reports 0.44B trainable params, 72 ms.", "takeaway_zh": "轻量世界动作模型：紧凑视频主干在降采样潜空间中做未来监督，动作专家融合多层特征一次前向预测动作块；报告可训练参数仅 0.44B，推理延迟 72 毫秒。", "github_stars": 94, "page": "https://embodied-ai-radar.com/e/arxiv-2606.08242.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2606.13769", "title": "$μ_0$: A Scalable 3D Interaction-Trace World Model", "type": "paper", "arxiv": "2606.13769", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Yoonkyo/mu0", "project": "https://mu0-wm.github.io/", "topics": [ "world-models", "manipulation" ], "tags": [ "3d-traces", "cross-embodiment", "action-free", "pretraining", "vlm" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "World model that predicts 3D traces of key points (objects, tools, hands) instead of pixels or actions, pretrained from video without action labels. Trace-conditioned policies are reported competitive with π0-style VLAs.", "takeaway_zh": "不预测像素也不预测动作，而是预测物体、工具、手等关键点的 3D 轨迹，可用无动作标签的视频预训练。报告以轨迹为条件的策略可与 π0 类 VLA 相当。", "github_stars": 72, "page": "https://embodied-ai-radar.com/e/arxiv-2606.13769.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.00267", "title": "StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement", "type": "paper", "arxiv": "2606.00267", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/CMU-IntentLab/StressDream", "project": "https://stressdream.github.io/", "topics": [ "world-models", "learning" ], "tags": [ "policy-evaluation", "stress-testing", "video-generation", "vlm", "noise-optimization" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Steers a video world model toward rare but plausible bad outcomes (e.g. task failures) by optimizing its initial noise with a VLM objective, to evaluate and improve policies. Driving and manipulation.", "takeaway_zh": "通过优化视频世界模型的初始噪声（以 VLM 为目标），把想象引向罕见但合理的不利结果（如任务失败），用于评估和改进策略；覆盖自动驾驶与操作。", "github_stars": 26, "page": "https://embodied-ai-radar.com/e/arxiv-2606.00267.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.07687", "title": "What Makes Video World Model Latents Action-Relevant: Prediction over Reconstruction", "type": "paper", "arxiv": "2606.07687", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/k1seul/corl2026-action-relevant-latents", "project": null, "topics": [ "world-models", "learning" ], "tags": [ "representation", "probing", "v-jepa", "inverse-dynamics" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Probe study of which video world model latents carry action information: temporal video pretraining matters, pixel reconstruction quality does not. V-JEPA-style encoders give the best trade-off.", "takeaway_zh": "探测哪些视频世界模型的潜变量含有动作信息：关键在于时序视频预训练，而非像素重建质量；V-JEPA 类编码器取得最佳权衡。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2606.07687.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.08156", "title": "La-JEPA: Compact Robot Policies with Latent World Modeling", "type": "paper", "arxiv": "2611.08156", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "world-models", "vla" ], "tags": [ "wam", "jepa", "v-jepa", "compact-policy", "real", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Small world action model trained from scratch on frozen V-JEPA 2.1 features, predicting future features only in training. Reports SOTA on RoboTwin 2.0 among policies without embodied pretraining.", "takeaway_zh": "在冻结的 V-JEPA 2.1 特征上从零训练的紧凑世界动作模型，仅在训练时预测未来特征。报告在无具身预训练的策略中 RoboTwin 2.0 最优，并修正了 LIBERO-Plus 的指令。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08156.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.08338", "title": "GPWM: Modeling the World as 3D Particle Motion", "type": "paper", "arxiv": "2611.08338", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://robertren1122.github.io/gpwm/", "topics": [ "world-models", "manipulation", "datasets" ], "tags": [ "particles", "3d-dynamics", "deformable", "planning", "diffusion-forcing" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Generative world model of 3D particle and rigid-body motion covering rigid, deformable, cloth and granular materials. Reports 31% lower error than prior learned dynamics; planning searches its latent noise.", "takeaway_zh": "生成式 3D 粒子与刚体运动世界模型，统一覆盖刚体、可变形体、布料与颗粒材料。报告预测误差比此前最好的学习动力学低 31%，并通过在潜噪声空间搜索来做规划。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08338.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.17526", "title": "Box-WAM: Box Trajectories as Compact State Representations for World Action Models", "type": "paper", "arxiv": "2610.17526", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/quincy-u/box-wam", "project": "https://box-wam.github.io", "topics": [ "world-models", "manipulation" ], "tags": [ "wam", "object-centric", "bounding-boxes", "efficiency" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "World action model that predicts oriented bounding-box trajectories of task objects instead of future video frames: much cheaper, and reported to predict actions better.", "takeaway_zh": "世界动作模型不再预测未来视频帧，而是预测任务相关物体的有向包围盒轨迹：计算量大幅下降，并报告动作预测更好。", "github_stars": 8, "page": "https://embodied-ai-radar.com/e/arxiv-2610.17526.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.08780", "title": "DepthWorld: 3D World Model for Robot Manipulation", "type": "paper", "arxiv": "2610.08780", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Jai2500/depthworld", "project": "https://www.jaibardhan.com/depthworld", "topics": [ "world-models", "datasets" ], "tags": [ "3d", "depth", "video-diffusion", "droid", "calibration" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Recalibrates DROID into DROID-3D with metric depth and multi-view extrinsics, then trains a video world model that predicts RGB and depth. Depth supervision reportedly also improves RGB by 1.48 dB PSNR.", "takeaway_zh": "把 DROID 重新标定为带度量深度与多视角外参的 DROID-3D，并训练同时预测 RGB 与深度的视频世界模型。报告深度监督还让 RGB 预测提升 1.48 dB PSNR。", "github_stars": 4, "page": "https://embodied-ai-radar.com/e/arxiv-2610.08780.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2608.29242", "title": "AnyWorld: Factorized Egocentric World Models for Cross-Embodiment Generalization", "type": "paper", "arxiv": "2608.29242", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/xpeng-robotics/AnyWorld", "project": "https://xpeng-robotics.github.io/anyworld/", "topics": [ "world-models", "datasets" ], "tags": [ "egocentric", "human-video", "cross-embodiment", "data-generation", "humanoid" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Factorizes an interaction into action, camera and embodiment so one human video can be re-rendered as many robot rollouts. Generated data helps on RoboCasa GR1 and a real IRON humanoid.", "takeaway_zh": "把一次交互分解为动作、相机与本体三个因素，使一段人类视频可重新生成为多种机器人视角的数据。生成数据在 RoboCasa GR1 与真实 IRON 人形上带来提升。", "github_stars": 30, "page": "https://embodied-ai-radar.com/e/arxiv-2608.29242.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.20679", "title": "MemoryVAM: Integrating Memory into Video Action Model for Robot Manipulation", "type": "paper", "arxiv": "2606.20679", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://MemoryVAM.github.io/", "topics": [ "world-models" ], "tags": [ "memory", "video-action-model", "long-horizon", "libero", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Adds episodic memory tokens to video-world-model policies so actions can depend on events no longer in view. Reports LIBERO-Mem success up from 5% to 42.5% and 75–80% on real memory tasks.", "takeaway_zh": "为视频世界模型策略加入情景记忆 token，使动作能依赖已不在视野中的事件。报告 LIBERO-Mem 成功率从 5% 提升到 42.5%，真机记忆类任务约 75–80%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.20679.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.20119", "title": "NovaPlan: Zero-Shot Long-Horizon Manipulation via Closed-Loop Video Language Planning", "type": "paper", "arxiv": "2602.20119", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/jiahui-fu/NovaPlan", "project": "https://nova-plan.github.io/", "topics": [ "world-models" ], "tags": [ "video-generation", "planning", "zero-shot", "long-horizon", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Zero-shot long-horizon manipulation: a VLM proposes and checks several generated video plans, actions follow object or hand flow, and execution is monitored with local corrections such as poking.", "takeaway_zh": "零样本长程操作：VLM 提出并验证多条生成的视频计划，动作由物体流或手部流转换而来，执行中持续监控并做戳推等局部纠正。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2602.20119.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2607.20653", "title": "PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics", "type": "paper", "arxiv": "2607.20653", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/lunarlab-gatech/PhysCoRe", "project": "https://lunarlab-gatech.github.io/PhysCoRe-website/", "topics": [ "world-models" ], "tags": [ "deformable", "mpm", "residual-learning", "material-identification", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "World model for deformable objects that couples a differentiable MPM simulator with networks that infer per-particle elasticity from video and correct the simulator's residual errors. Real manipulation sequences.", "takeaway_zh": "可变形物体世界模型：可微 MPM 仿真器配合两个网络，一个从视觉推断逐粒子弹性，一个学习修正仿真器的残差误差。在真实操作序列上验证。", "github_stars": 16, "page": "https://embodied-ai-radar.com/e/arxiv-2607.20653.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2607.08436", "title": "EgoWAM: World Action Models Beyond Pixels with In-the-Wild Egocentric Human Data", "type": "paper", "arxiv": "2607.08436", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/GaTech-RL2/EgoWAM", "project": "https://gatech-rl2.github.io/egowam.github.io", "topics": [ "world-models" ], "tags": [ "human-video", "egocentric", "world-action-model", "dino", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Controlled study of which world-prediction target best transfers egocentric human data to robots: pixels transfer weakly, DINO features give up to 4x OOD generalization, 3D flow gives +20–30% in domain.", "takeaway_zh": "对照研究哪种世界预测目标最能把第一人称人类数据迁移到机器人：像素迁移较弱，DINO 特征带来最高 4 倍的分布外泛化，3D 运动流在域内提升 20–30%。", "github_stars": 47, "page": "https://embodied-ai-radar.com/e/arxiv-2607.08436.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2603.09030", "title": "PlayWorld: Learning Robot World Models from Autonomous Play", "type": "paper", "arxiv": "2603.09030", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://robot-playworld.github.io/", "topics": [ "world-models" ], "tags": [ "video-world-model", "self-play", "policy-evaluation", "rl", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Trains an action-conditioned video world model purely from autonomous robot play instead of human demos, capturing failure-rich contact. Reports up to 40% better policy evaluation and +65% real success after RL in it.", "takeaway_zh": "完全用机器人自主“玩耍”数据（而非人类演示）训练动作条件视频世界模型，覆盖更多接触与失败情形。报告策略评估提升最高 40%，在其中做强化学习后真机成功率提升 65%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.09030.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.01027", "title": "$τ_0$-WM: A Unified Video-Action World Model for Robotic Manipulation", "type": "paper", "arxiv": "2606.01027", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/sii-research/tau-0-wm", "project": "https://tau0-wm.github.io", "topics": [ "world-models" ], "tags": [ "video-action-model", "simulator", "test-time-compute", "large-scale", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "One video-diffusion backbone serves as both a video-action policy and an action-conditioned simulator that scores progress, trained on ~27,300 hours of robot, UMI and human data; samples and ranks actions at test time.", "takeaway_zh": "同一个视频扩散主干既是视频-动作策略，也是可预测任务进度的动作条件模拟器；训练数据约 2.73 万小时（真机、UMI、人类视频等），推理时采样并排序候选动作。", "github_stars": 521, "page": "https://embodied-ai-radar.com/e/arxiv-2606.01027.html", "labs": [], "code_license": "" }, { "id": "arxiv-2602.13977", "title": "WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL", "type": "paper", "arxiv": "2602.13977", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://wovr-corl.github.io", "topics": [ "world-models" ], "tags": [ "rl", "vla", "video-world-model", "hallucination", "libero" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "RL post-training of VLAs inside a video world model, with keyframe-initialised rollouts and world-model–policy co-evolution to keep hallucination in check. Reports strong LIBERO results and real-robot gains.", "takeaway_zh": "在视频世界模型中对 VLA 做强化学习后训练，通过关键帧初始化的短推演和世界模型-策略协同演化来抑制幻觉。报告 LIBERO 表现优异并在多个真机平台上提升。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.13977.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.10983", "title": "Scaling World Model for Hierarchical Manipulation Policies", "type": "paper", "arxiv": "2602.10983", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/vista-wm/Vista-WM", "project": "https://vista-wm.github.io/", "topics": [ "world-models" ], "tags": [ "hierarchical", "goal-images", "subgoals", "vla", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "A large pretrained world model plans subtasks and generates goal images; a VLA follows them. Reports the same VLA rising from 14% to 69% in novel scenes with these generated goals.", "takeaway_zh": "用大规模预训练世界模型做高层规划，把任务拆成子任务并生成目标图像，由底层 VLA 跟随执行。报告同结构 VLA 在新场景中的成功率借此从 14% 提升到 69%。", "github_stars": 26, "page": "https://embodied-ai-radar.com/e/arxiv-2602.10983.html", "labs": [], "code_license": "" }, { "id": "arxiv-2608.10232", "title": "FACT: Failure-Aware Causal Training for World-Action Models", "type": "paper", "arxiv": "2608.10232", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Bariona/FACT", "project": "https://fact-wam.github.io/", "topics": [ "world-models" ], "tags": [ "world-action-model", "failure-data", "progress-estimation", "bimanual", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Action-conditioned world-action model that also learns from failed rollouts, predicting future video and task progress for the action actually taken; progress scores can rank sampled actions.", "takeaway_zh": "以实际执行动作为条件的世界动作模型，把失败轨迹也作为监督，预测未来视频与任务进度；进度分可在推理时为候选动作排序。", "github_stars": 109, "page": "https://embodied-ai-radar.com/e/arxiv-2608.10232.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2602.06219", "title": "Coupled Local and Global World Models for Efficient First Order RL", "type": "paper", "arxiv": "2602.06219", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://coupled-global-local-wm-rl.pages.dev/", "topics": [ "world-models" ], "tags": [ "rl", "diffusion-world-model", "first-order-gradients", "push-t", "quadruped" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "RL inside a learned diffusion world model without a simulator: the big model rolls out trajectories while a light latent surrogate supplies gradients. Beats PPO on sample efficiency on Push-T; quadruped task too.", "takeaway_zh": "不用物理仿真器，直接在学习到的扩散世界模型中训练策略：大模型负责生成轨迹，轻量潜在代理模型提供一阶梯度。在 Push-T 上样本效率明显优于 PPO，并在四足任务上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.06219.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.03208", "title": "Hierarchical Planning with Latent World Models", "type": "paper", "arxiv": "2604.03208", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/kevinghst/HWM_PLDM", "project": "https://kevinghst.github.io/HWM/", "topics": [ "world-models" ], "tags": [ "planning", "latent-world-model", "hierarchical-mpc", "macro-actions", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Hierarchical MPC on latent visual world models at two time scales, linked by latent subgoals and learned macro-actions. Reports 70% vs 0% on real Franka pick-and-place from one goal image, with up to 3x less compute.", "takeaway_zh": "在两个时间尺度的潜在视觉世界模型上做分层 MPC，以潜在子目标和学习到的宏动作衔接。报告真实 Franka 单目标图抓放成功率 70%（单层规划为 0%），计算量最多少 3 倍。", "github_stars": 197, "page": "https://embodied-ai-radar.com/e/arxiv-2604.03208.html", "labs": [], "code_license": "" }, { "id": "arxiv-2607.22535", "title": "Robot-Factored World Models via Robot Rendering", "type": "paper", "arxiv": "2607.22535", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://bjkim95.github.io/rofacto/", "topics": [ "world-models" ], "tags": [ "video-world-model", "robot-rendering", "urdf", "cross-embodiment", "human-video" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Video world model that renders the robot's own motion from its URDF and controller, so the network only predicts how the scene responds. Transfers to unseen robots and can animate retargeted human demos.", "takeaway_zh": "视频世界模型把机器人自身运动交给控制器和 URDF 渲染，网络只需预测场景的响应。可迁移到未见过的机器人本体，并能由重定向的人类演示生成机器人操作视频。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.22535.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.06832", "title": "STRIPS-WM: Learning Grounded Propositional STRIPS-style World Models from Images", "type": "paper", "arxiv": "2606.06832", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://elpislab.org/projects/strips-wm/", "topics": [ "world-models" ], "tags": [ "symbolic-planning", "strips", "predicates", "visual-planning", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns STRIPS-style operators (preconditions, add/delete effects) over latent predicates from image transitions, then plans classically from start and goal images. Beats visual-rollout and latent-search methods.", "takeaway_zh": "直接从图像转移数据学习 STRIPS 式符号算子（前提条件、增删效果）和潜在二值谓词，再从起始图与目标图用经典规划求解。优于视觉推演和潜在图搜索等基线。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.06832.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.07850", "title": "Learning Robotic World Models with Semantic and Visual Feedback", "type": "paper", "arxiv": "2611.07850", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Alexander-wu/RoboImagine", "project": "https://huggingface.co/easylearning/RoboImagine", "topics": [ "world-models" ], "tags": [ "world-model", "post-training", "video-prediction", "vlm-judge", "grpo" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "RoboImagine post-trains autoregressive robot world models by comparing candidate futures, scored by a frozen multimodal judge plus pixel/perceptual terms. Reports +1.26 dB PSNR on RT-1 over continued SFT.", "takeaway_zh": "RoboImagine 通过比较多条候选未来来后训练自回归机器人世界模型：冻结的多模态评估器给出语义偏好，像素与感知层面的一致性项约束视觉误差。在 RT-1 上报告 PSNR 比继续监督微调高 1.26 dB。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2611.07850.html", "labs": [], "code_license": "" }, { "id": "arxiv-2611.02600", "title": "W2A: Distilling World Priors into Action Models for Real-Time Robotic Control", "type": "paper", "arxiv": "2611.02600", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "world-action-model", "distillation", "video-diffusion", "libero" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Distills intermediate denoising features of a video diffusion model into an action model during training and drops the video model at test time. Reports 95.9% on LIBERO and about 21x faster inference than a WAM baseline.", "takeaway_zh": "训练时把视频扩散模型的中间去噪表示蒸馏进动作模型，测试时完全丢弃视频模型。在 LIBERO 上报告 95.9% 平均成功率，推理比代表性世界动作模型快约 21 倍。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.02600.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.08555", "title": "FAWAM: Force-Aware World Action Models for Closed-Loop Contact-Rich Manipulation", "type": "paper", "arxiv": "2606.08555", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "world-action-model", "force-torque", "contact-rich", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "FAWAM uses 6-axis force/torque in perception, jointly predicts future actions and wrenches, and corrects actions online against the predicted wrench. Reports +36.25 pts over vision-only baselines on real tasks.", "takeaway_zh": "FAWAM 在感知、预测与闭环执行三层使用六维力/力矩：联合预测未来动作与末端力旋量，并以预测的力轨迹为参考在线修正动作。真机任务上比纯视觉基线成功率高 36.25 个百分点。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.08555.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.13515", "title": "MaskWAM: Unifying Mask Prompting and Prediction for World-Action Models", "type": "paper", "arxiv": "2606.13515", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "world-action-model", "masks", "object-centric", "mixture-of-transformers" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Object-centric world-action model that both takes object masks as prompts and predicts future masks, reducing language ambiguity in clutter and background bias in RGB prediction.", "takeaway_zh": "以物体为中心的世界动作模型：既把目标物体掩码作为提示输入，又预测未来掩码，从而减少杂乱场景中的语言歧义和 RGB 预测中的背景偏差。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.13515.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.23156", "title": "ORD-WM: Learning Object-Centric Rigid Dynamics for 3D World Modeling in Robotic Manipulation", "type": "paper", "arxiv": "2610.23156", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "3d-world-model", "rigid-dynamics", "point-cloud", "se3" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "3D world model that encodes each object's point cloud with an SDF-supervised vector-neuron VAE and predicts per-object SE(3) increments applied analytically, so shapes do not drift over long rollouts.", "takeaway_zh": "3D 世界模型：用 SDF 监督的向量神经元 VAE 编码每个物体的点云，并预测逐物体的 SE(3) 运动增量后解析地施加，长时推演中物体形状不会漂移。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.23156.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.08127", "title": "Understanding and Mitigating the Video-Action Generalization Gap via Temporal Ratio", "type": "paper", "arxiv": "2607.08127", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "world-action-model", "video-action-model", "generalization", "analysis" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Defines Temporal Ratio, how much an action head attends to future latents vs the current frame; it predicts compositional generalization of video-action models and drives an inference-time adaptive guidance method.", "takeaway_zh": "提出 Temporal Ratio：动作头对未来潜变量相对当前帧的注意力依赖程度；它能预测视频动作模型的组合泛化能力，并据此设计推理时的自适应引导方法。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.08127.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.06988", "title": "WAM-TTT: Steering World-Action Models by Watching Human Play at Test Time", "type": "paper", "arxiv": "2607.06988", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "world-action-model", "test-time-training", "human-video", "steering" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Steers a frozen world-action model with unlabeled human videos at test time: a small memory inside the model adapts via self-supervised video prediction, after meta-training on paired human-robot data.", "takeaway_zh": "测试时用无标注的人类视频引导冻结的世界动作模型：模型内部的小型记忆通过自监督视频预测进行适配；此前用成对人-机数据做元训练使其对控制有用。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.06988.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.00664", "title": "SKIP: Sparse Keyframe Interpolation Paradigm for Efficient Embodied World Models", "type": "paper", "arxiv": "2606.00664", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "world-model", "video-generation", "keyframes", "efficiency", "libero" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "SKIP generates only task-relevant keyframes (approach, contact, grasp, release) with a sparse video diffusion model, then fills the gaps with an action-conditioned interpolator. 4.16x faster dense rollouts on LIBERO.", "takeaway_zh": "SKIP 只用稀疏视频扩散模型生成与任务相关的关键帧（接近、接触、抓取、释放），再用动作条件插值器补全中间帧。在 LIBERO 上生成稠密推演快 4.16 倍。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.00664.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.14585", "title": "Sensitivity Shaping for Latent Modeling", "type": "paper", "arxiv": "2606.14585", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "world-model", "ood-detection", "safety", "regularization" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learned dynamics that ignore control changes can hide out-of-distribution actions. A support-conditioned control-sensitivity regularizer keeps predictions responsive, improving OOD detection and safer planning.", "takeaway_zh": "对控制变化不敏感的学习动力学会掩盖分布外动作。基于数据支撑的控制敏感性正则保持模型对控制的响应，从而提升分布外检测并使闭环规划更安全。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.14585.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.32742", "title": "Adaptive World-Models for Anomaly Recognition (AWARE): A Framework for Robot Dynamics Prediction and Introspection", "type": "paper", "arxiv": "2610.32742", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "anomaly-detection", "world-model", "camera-only", "system-identification" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Detects robot anomalies from external cameras only by estimating dynamics and predicting states. On 240 real crane trajectories: +26% anomaly classification, and 88% accuracy locating the degrading motor.", "takeaway_zh": "仅凭外部相机，通过估计动力学参数与预测状态来检测机器人异常。在 240 条真实起重机轨迹上异常分类提升 26%，定位退化电机的准确率 88%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.32742.html", "labs": [], "code_license": null }, { "id": "arxiv-2510.06492", "title": "How Well Do Latent World Models Understand Partially Observable Safety Constraints?", "type": "paper", "arxiv": "2510.06492", "year": 2025, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "latent-world-model", "safety", "partial-observability", "analysis" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Names two failure modes of latent safe control, estimation gaps (safety facts not visible) and prediction gaps (failures not foreseeable), gives a diagnostic for each and mitigations.", "takeaway_zh": "指出潜空间安全控制的两种失效：估计缺口（当前观测看不到安全关键量）与预测缺口（失败无法提前预见），并分别给出诊断指标和缓解方法。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2510.06492.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.08962", "title": "C$^3$ache: Accelerating World Action Models with Cross Inference Chunk Cache", "type": "paper", "arxiv": "2606.08962", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "world-models" ], "tags": [ "world-action-model", "inference-acceleration", "caching", "training-free" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Training-free speedup for world-action models: reuses denoising residuals across consecutive inference chunks at the same step, since they correlate during smooth motion. Up to 2.5x with a Fast-WAM backbone.", "takeaway_zh": "无需训练的世界动作模型加速方法：平滑运动时相邻推理块在同一去噪步的残差高度相关，因此跨块缓存复用。以 Fast-WAM 为骨干最高提速 2.5 倍。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.08962.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.04527", "title": "Future Representation Distillation for Imagination-free World Action Models", "type": "paper", "arxiv": "2611.04527", "year": 2026, "venue": null, "org": "KAIST AI", "url": null, "code": null, "project": "https://harryjo97.github.io/dreamfree/", "topics": [ "world-models" ], "tags": [ "world-action-model", "video-diffusion", "distillation", "real-time" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-08", "takeaway_en": "DreamFree: a WAM that skips future-video generation. An encoder learns to predict a frozen video diffusion model's features from the current frame; actions come from those. Reports 11 Hz control with a 14B backbone.", "takeaway_zh": "DreamFree：无需生成未来视频的世界动作模型。编码器从当前观测预测冻结视频扩散模型的表征，动作模块据此出动作；报告以 14B 视频扩散骨干实现 11Hz 实时闭环控制。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.04527.html", "labs": [ "kaist" ], "code_license": null }, { "id": "arxiv-2611.11085", "title": "ObjectWorld: Scaling Object-Centric World Models with A Large-Scale 3D Full-Object Flow Dataset", "type": "paper", "arxiv": "2611.11085", "year": 2026, "venue": null, "org": "National University of Singapore", "url": null, "code": null, "project": "https://nus-lins-lab.github.io/ObjectWorld/", "topics": [ "world-models", "datasets", "manipulation" ], "tags": [ "object-flow", "3d", "dataset", "world-model", "deformable" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-10", "takeaway_en": "Represents object motion as dense 3D trajectories over the whole surface, occluded parts included, for rigid and deformable objects. Adds a 1,210-hour flow dataset and a world model predicting it from image + task.", "takeaway_zh": "把物体运动表示为覆盖整个表面（含遮挡部分）的稠密 3D 轨迹，适用于刚体与可形变物体。发布 1210 小时的全物体流数据集，并训练从图像和任务描述预测该流的世界模型。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.11085.html", "labs": [ "nus" ], "code_license": null }, { "id": "arxiv-2204.01691", "title": "Do As I Can, Not As I Say: Grounding Language in Robotic Affordances", "type": "paper", "arxiv": "2204.01691", "year": 2022, "venue": null, "org": "Google", "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "real", "mobile", "llm", "planning" ], "robot_types": [], "highlight": true, "rating": 3, "added": null, "takeaway_en": "SayCan — an LLM proposes steps and learned value functions score what the robot can actually do right now.", "takeaway_zh": "SayCan：大模型提出步骤，学习到的价值函数评估机器人当下实际能做什么。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2204.01691.html", "labs": [ "google-deepmind" ], "code_license": null }, { "id": "arxiv-2209.07753", "title": "Code as Policies: Language Model Programs for Embodied Control", "type": "paper", "arxiv": "2209.07753", "year": 2022, "venue": null, "org": "Google", "url": null, "code": null, "project": null, "topics": [ "navigation", "manipulation" ], "tags": [ "real", "llm", "code-generation" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "LLMs write Python that calls perception and control APIs, turning instructions into reactive robot programs.", "takeaway_zh": "大模型编写调用感知与控制 API 的 Python 代码，把指令变成可响应的机器人程序。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2209.07753.html", "labs": [ "google-deepmind" ], "code_license": null }, { "id": "arxiv-2207.05608", "title": "Inner Monologue: Embodied Reasoning through Planning with Language Models", "type": "paper", "arxiv": "2207.05608", "year": 2022, "venue": null, "org": "Google", "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "real", "llm", "planning", "feedback" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Feeding success detection and scene descriptions back to the LLM planner lets it replan after failures.", "takeaway_zh": "将成功检测与场景描述反馈给大模型规划器，使其在失败后重新规划。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2207.05608.html", "labs": [ "google-deepmind" ], "code_license": null }, { "id": "arxiv-1711.07280", "title": "Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments", "type": "paper", "arxiv": "1711.07280", "year": 2017, "venue": null, "org": "Australian National University", "url": null, "code": null, "project": null, "topics": [ "navigation", "datasets" ], "tags": [ "sim", "benchmark", "vln" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Introduced the VLN task and the Room-to-Room (R2R) benchmark in Matterport3D homes.", "takeaway_zh": "提出视觉语言导航任务与基于 Matterport3D 的 Room-to-Room (R2R) 基准。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1711.07280.html", "labs": [], "code_license": null }, { "id": "arxiv-2210.05714", "title": "Visual Language Maps for Robot Navigation", "type": "paper", "arxiv": "2210.05714", "year": 2022, "venue": null, "org": "Freiburg, Google", "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "real", "mapping", "open-vocabulary" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "VLMaps fuses visual-language features into a 3D map so robots can navigate to open-vocabulary spatial goals.", "takeaway_zh": "VLMaps 将视觉语言特征融合进 3D 地图，使机器人可按开放词汇空间目标导航。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2210.05714.html", "labs": [ "google-deepmind", "freiburg" ], "code_license": null }, { "id": "arxiv-2310.07896", "title": "NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration", "type": "paper", "arxiv": "2310.07896", "year": 2023, "venue": null, "org": "UC Berkeley", "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "real", "diffusion", "exploration" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "One diffusion policy handles both goal-directed navigation and exploration by masking the goal.", "takeaway_zh": "通过对目标做掩码，单一扩散策略同时完成目标导航与探索。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2310.07896.html", "labs": [ "berkeley" ], "code_license": null }, { "id": "arxiv-2401.12202", "title": "OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics", "type": "paper", "arxiv": "2401.12202", "year": 2024, "venue": null, "org": "NYU, Meta AI", "url": null, "code": null, "project": null, "topics": [ "navigation", "manipulation" ], "tags": [ "real", "mobile", "open-vocabulary", "zero-shot" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Combines off-the-shelf open-vocabulary models into a pick-and-drop system that works in real homes without training.", "takeaway_zh": "组合现成的开放词汇模型，构建无需训练即可在真实家庭中抓取放置的系统。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2401.12202.html", "labs": [ "meta", "nyu" ], "code_license": null }, { "id": "arxiv-2610.19600", "title": "Cracking Mobile Manipulation: From Failure Diagnosis to Agentic Robot System", "type": "paper", "arxiv": "2610.19600", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/PKU-VaLuE-Lab/Cracking-Mobile-Manipulation", "project": null, "topics": [ "navigation", "manipulation", "vla" ], "tags": [ "real", "mobile", "failure-analysis", "agentic" ], "robot_types": [ "mobile" ], "highlight": false, "rating": 0, "added": "2026-10-19", "takeaway_en": "Finds a VLA at 80% on atomic skills drops to 19% when composed into mobile manipulation, diagnoses why, and builds an agentic system around it.", "takeaway_zh": "发现 VLA 在原子技能上 80% 成功率、组合成移动操作后仅 19%，分析失败原因并据此构建智能体式系统。", "github_stars": 8, "page": "https://embodied-ai-radar.com/e/arxiv-2610.19600.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.20607", "title": "Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance", "type": "paper", "arxiv": "2610.20607", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/tasl-lab/HiRe-MoMa", "project": "https://hire-moma.github.io/", "topics": [ "navigation", "manipulation" ], "tags": [ "real", "mobile-manipulation", "residual-rl", "fine-tuning" ], "robot_types": [ "mobile" ], "highlight": false, "rating": 0, "added": "2026-10-19", "takeaway_en": "HiRe-MoMa fine-tunes a frozen mobile-manipulation policy in the real world by learning residual corrections, with only occasional human guidance.", "takeaway_zh": "HiRe-MoMa 在真实环境中通过学习残差修正来微调冻结的移动操作策略，只需偶尔的人类引导。", "github_stars": 5, "page": "https://embodied-ai-radar.com/e/arxiv-2610.20607.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2306.14846", "title": "ViNT: A Foundation Model for Visual Navigation", "type": "paper", "arxiv": "2306.14846", "year": 2023, "venue": "CoRL 2023", "org": "UC Berkeley", "url": null, "code": "https://github.com/robodhruv/visualnav-transformer", "project": null, "topics": [ "navigation" ], "tags": [ "foundation-model", "image-goal", "cross-embodiment", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "A Transformer navigation policy trained on many robots' driving data that adapts to new goal types and robots with light fine-tuning.", "takeaway_zh": "在多种机器人的导航数据上训练的 Transformer 导航策略，经少量微调即可适配新的目标形式和新机器人。", "github_stars": 1342, "page": "https://embodied-ai-radar.com/e/arxiv-2306.14846.html", "labs": [ "berkeley" ], "code_license": "MIT" }, { "id": "arxiv-2210.03370", "title": "GNM: A General Navigation Model to Drive Any Robot", "type": "paper", "arxiv": "2210.03370", "year": 2022, "venue": "ICRA 2023", "org": "UC Berkeley", "url": null, "code": "https://github.com/robodhruv/visualnav-transformer", "project": null, "topics": [ "navigation" ], "tags": [ "foundation-model", "image-goal", "cross-embodiment", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "The predecessor of ViNT — one goal-conditioned navigation policy trained on pooled data from several different robots, which transfers to new robots including ones not seen in training.", "takeaway_zh": "ViNT 的前作：在多种不同机器人汇集的数据上训练一个目标条件导航策略，可迁移到包括训练中未见过的新机器人。", "github_stars": 1342, "page": "https://embodied-ai-radar.com/e/arxiv-2210.03370.html", "labs": [ "berkeley" ], "code_license": "MIT" }, { "id": "arxiv-2312.03275", "title": "VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation", "type": "paper", "arxiv": "2312.03275", "year": 2023, "venue": "ICRA 2024", "org": "Boston Dynamics AI Institute, Georgia Tech", "url": null, "code": "https://github.com/rai-opensource/vlfm", "project": null, "topics": [ "navigation" ], "tags": [ "zero-shot", "object-goal", "vlm", "frontier-exploration", "real", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Zero-shot object-goal navigation that scores exploration frontiers by vision-language similarity to the target, with no task-specific training; tested in Habitat and on a Spot robot.", "takeaway_zh": "零样本目标物体导航：用视觉-语言模型对探索前沿与目标的相关度打分来决定往哪走，无需针对任务训练；在 Habitat 和 Spot 机器人上验证。", "github_stars": 834, "page": "https://embodied-ai-radar.com/e/arxiv-2312.03275.html", "labs": [ "georgia-tech" ], "code_license": "MIT" }, { "id": "arxiv-2402.15852", "title": "NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation", "type": "paper", "arxiv": "2402.15852", "year": 2024, "venue": "RSS 2024", "org": "Peking University", "url": null, "code": "https://github.com/jzhzhang/NaVid-VLN-CE", "project": null, "topics": [ "navigation" ], "tags": [ "vln", "video-vlm", "monocular", "sim-to-real" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A video VLM that reads the monocular RGB history plus the instruction and outputs the next navigation action directly, without maps, odometry or depth.", "takeaway_zh": "基于视频的视觉语言模型：只看单目 RGB 历史帧和语言指令，直接输出下一步导航动作，不依赖地图、里程计或深度。", "github_stars": 460, "page": "https://embodied-ai-radar.com/e/arxiv-2402.15852.html", "labs": [ "pku" ], "code_license": "MIT" }, { "id": "arxiv-2412.06224", "title": "Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks", "type": "paper", "arxiv": "2412.06224", "year": 2024, "venue": "RSS 2025", "org": "Peking University", "url": null, "code": "https://github.com/jzhzhang/NaVid-VLN-CE", "project": null, "topics": [ "navigation", "vla" ], "tags": [ "vln", "object-goal", "following", "video-vlm" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Extends NaVid into one video VLA model for several navigation tasks (instruction following, object search, question answering, human following).", "takeaway_zh": "把 NaVid 扩展为单一视频 VLA 模型，统一处理多种导航任务（指令导航、目标搜索、具身问答、跟随人）。", "github_stars": 460, "page": "https://embodied-ai-radar.com/e/arxiv-2412.06224.html", "labs": [ "pku" ], "code_license": "MIT" }, { "id": "arxiv-2003.00368", "title": "The Marathon 2: A Navigation System", "type": "paper", "arxiv": "2003.00368", "year": 2020, "venue": "IROS 2020", "org": null, "url": null, "code": "https://github.com/ros-navigation/navigation2", "project": null, "topics": [ "navigation" ], "tags": [ "ros2", "nav2", "behavior-trees", "open-source", "mobile" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "The paper behind Nav2, the standard ROS 2 navigation stack - behavior-tree orchestration of planners, controllers and recovery behaviors, validated by long marathon runs on real robots.", "takeaway_zh": "ROS 2 标准导航栈 Nav2 的论文：用行为树编排规划器、控制器和恢复行为，并在真实机器人上长距离“马拉松”测试验证。", "github_stars": 5030, "page": "https://embodied-ai-radar.com/e/arxiv-2003.00368.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2412.04453", "title": "NaVILA: Legged Robot Vision-Language-Action Model for Navigation", "type": "paper", "arxiv": "2412.04453", "year": 2024, "venue": "RSS 2025", "org": "UC San Diego, USC, NVIDIA", "url": null, "code": "https://github.com/AnjieCheng/NaVILA", "project": "https://navila-bot.github.io/", "topics": [ "navigation", "vla", "locomotion" ], "tags": [ "vln", "legged", "hierarchical" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A VLA outputs mid-level navigation commands in language that an RL locomotion policy executes, so legged robots follow natural-language routes; adds Isaac Lab VLN benchmarks.", "takeaway_zh": "VLA 先用语言输出中层导航指令，再由强化学习运动策略执行，让腿式机器人按自然语言指令导航；并提供 Isaac Lab 上的 VLN 基准。", "github_stars": 734, "page": "https://embodied-ai-radar.com/e/arxiv-2412.04453.html", "labs": [ "nvidia", "ucsd", "usc" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2004.02857", "title": "Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments", "type": "benchmark", "arxiv": "2004.02857", "year": 2020, "venue": "ECCV 2020", "org": "Oregon State, Georgia Tech, Meta AI", "url": null, "code": "https://github.com/jacobkrantz/VLN-CE", "project": null, "topics": [ "navigation", "datasets" ], "tags": [ "vln", "habitat", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "VLN-CE moves Room-to-Room instructions from a fixed navigation graph into continuous Habitat scenes with low-level actions; the standard setting for realistic VLN.", "takeaway_zh": "VLN-CE 把 Room-to-Room 指令从固定导航图搬到 Habitat 连续环境中、使用底层动作执行，是更贴近真实的 VLN 标准设定。", "github_stars": 903, "page": "https://embodied-ai-radar.com/e/arxiv-2004.02857.html", "labs": [ "meta", "georgia-tech" ], "code_license": "MIT" }, { "id": "arxiv-2207.04429", "title": "LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action", "type": "paper", "arxiv": "2207.04429", "year": 2022, "venue": "CoRL 2022", "org": "UC Berkeley, Google", "url": null, "code": "https://github.com/blazejosinski/lm_nav", "project": null, "topics": [ "navigation", "vla" ], "tags": [ "real", "wheeled", "language", "foundation-models", "zero-shot" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Chains an LLM (parses landmarks from the instruction), CLIP (finds them in images) and a visual navigation model to follow route instructions on a real robot without language-annotated robot data.", "takeaway_zh": "将大语言模型（从指令中解析地标）、CLIP（在图像中找到地标）与视觉导航模型串联，无需带语言标注的机器人数据即可在真实机器人上按路线指令导航。", "github_stars": 272, "page": "https://embodied-ai-radar.com/e/arxiv-2207.04429.html", "labs": [ "berkeley", "google-deepmind" ], "code_license": "MIT" }, { "id": "arxiv-2007.00643", "title": "Object Goal Navigation using Goal-Oriented Semantic Exploration", "type": "paper", "arxiv": "2007.00643", "year": 2020, "venue": "NeurIPS 2020", "org": "CMU, Meta AI", "url": null, "code": "https://github.com/devendrachaplot/Object-Goal-Navigation", "project": null, "topics": [ "navigation", "perception" ], "tags": [ "sim", "objectnav", "semantic-map", "modular", "rl" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "SemExp builds an episodic semantic map and learns a policy that picks long-term goals on it, a modular recipe that became a standard ObjectNav baseline in Habitat.", "takeaway_zh": "SemExp 构建情景语义地图，并学习在地图上选择长期目标的策略；这一模块化方案成为 Habitat 中 ObjectNav 的常用基线。", "github_stars": 464, "page": "https://embodied-ai-radar.com/e/arxiv-2007.00643.html", "labs": [ "meta", "cmu" ], "code_license": "MIT" }, { "id": "arxiv-2004.05155", "title": "Learning to Explore using Active Neural SLAM", "type": "paper", "arxiv": "2004.05155", "year": 2020, "venue": "ICLR 2020", "org": "CMU, Meta AI", "url": null, "code": "https://github.com/devendrachaplot/Neural-SLAM", "project": null, "topics": [ "navigation", "perception" ], "tags": [ "sim", "exploration", "slam", "modular", "rl" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Splits exploration into a learned neural SLAM module, a global policy that chooses long-term goals and a local policy that reaches them, which is far more sample-efficient than end-to-end RL.", "takeaway_zh": "将探索拆为可学习的神经 SLAM 模块、选择长期目标的全局策略和到达目标的局部策略，比端到端强化学习样本效率高得多。", "github_stars": 854, "page": "https://embodied-ai-radar.com/e/arxiv-2004.05155.html", "labs": [ "meta", "cmu" ], "code_license": "MIT" }, { "id": "arxiv-2203.10421", "title": "CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation", "type": "paper", "arxiv": "2203.10421", "year": 2022, "venue": "CVPR 2023", "org": "Columbia University", "url": null, "code": "https://github.com/real-stanford/cow", "project": "https://cow.cs.columbia.edu/", "topics": [ "navigation" ], "tags": [ "zero-shot", "object-goal", "clip", "benchmark", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "CLIP on Wheels pairs open-vocabulary localization (CLIP-style models, no fine-tuning) with classical exploration for zero-shot object navigation; the Pasture benchmark adds rare, attribute-described and hidden objects.", "takeaway_zh": "CoWs on Pasture：CLIP on Wheels 把开放词汇定位（CLIP 等模型，无需微调）与经典探索结合，实现零样本目标物体导航；配套的 Pasture 基准加入了少见物体、按属性描述的物体和被遮挡物体。", "github_stars": 158, "page": "https://embodied-ai-radar.com/e/arxiv-2203.10421.html", "labs": [ "columbia" ], "code_license": "" }, { "id": "arxiv-2301.07302", "title": "PIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNav", "type": "paper", "arxiv": "2301.07302", "year": 2023, "venue": "CVPR 2023", "org": "Georgia Tech, Meta AI", "url": null, "code": "https://github.com/Ram81/pirlnav", "project": null, "topics": [ "navigation", "learning" ], "tags": [ "object-goal", "imitation", "rl-finetuning", "habitat", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "PIRLNav — behavior cloning on human ObjectNav demonstrations, then RL fine-tuning of the same policy; the paper reports 65.0% success on Habitat ObjectNav, 5 points above the previous best.", "takeaway_zh": "PIRLNav：先在人类 ObjectNav 演示上做行为克隆，再用强化学习微调同一策略；论文报告在 Habitat ObjectNav 上成功率 65.0%，比此前最佳高 5 个百分点。", "github_stars": 93, "page": "https://embodied-ai-radar.com/e/arxiv-2301.07302.html", "labs": [ "meta", "georgia-tech" ], "code_license": "" }, { "id": "arxiv-2610.25690", "title": "ScaleNav: Scalable Internet Video Pretraining for Language-Conditioned Local Navigation", "type": "paper", "arxiv": "2610.25690", "year": 2026, "venue": null, "org": "Zhejiang University", "url": null, "code": "https://github.com/MachEmbodied/ScaleNav", "project": "https://zju3dv.github.io/ScaleNav/", "topics": [ "navigation" ], "tags": [ "internet-video", "language-navigation", "quadruped", "pretraining" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-10-23", "takeaway_en": "Pretrains on internet egocentric video to learn how the view changes when approaching a described target, then aligns to actions in sim; reports 94.9% success on its HabitatGS-LN benchmark plus a real quadruped.", "takeaway_zh": "先在互联网第一视角视频上预训练，学习\"朝描述的目标走近时画面如何变化\"，再用仿真数据对齐到动作；论文报告在自建 HabitatGS-LN 基准上成功率 94.9%，并在真实四足机器人上部署。", "github_stars": 54, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25690.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2305.16986", "title": "NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models", "type": "paper", "arxiv": "2305.16986", "year": 2023, "venue": "AAAI 2024", "org": "University of Adelaide", "url": null, "code": "https://github.com/GengzeZhou/NavGPT", "project": null, "topics": [ "navigation" ], "tags": [ "vln", "llm", "zero-shot", "reasoning", "r2r" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A purely LLM-based VLN agent — GPT models read text descriptions of what the robot sees, its history and possible directions, then reason step by step about where to go; zero-shot on R2R, still behind trained models.", "takeaway_zh": "纯大语言模型的视觉语言导航智能体：GPT 读取视觉观测的文字描述、历史轨迹和可走方向，逐步显式推理下一步去哪；在 R2R 上零样本运行，但仍落后于专门训练的模型。", "github_stars": 357, "page": "https://embodied-ai-radar.com/e/arxiv-2305.16986.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2304.03047", "title": "ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments", "type": "paper", "arxiv": "2304.03047", "year": 2023, "venue": "TPAMI 2024", "org": null, "url": null, "code": "https://github.com/MarSaKi/ETPNav", "project": null, "topics": [ "navigation" ], "tags": [ "vln-ce", "topological-map", "planning", "r2r-ce", "rxr-ce" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "For VLN in continuous spaces, builds a topological map online from predicted waypoints, plans on it with a cross-modal transformer and moves with an obstacle-avoiding controller; reports >10%/>20% gains on R2R-CE/RxR-CE.", "takeaway_zh": "面向连续环境视觉语言导航：根据预测的路点在线构建拓扑地图，用跨模态 Transformer 在图上规划，再由避障控制器执行；报告在 R2R-CE、RxR-CE 上分别比此前最好方法提升 10% 和 20% 以上。", "github_stars": 499, "page": "https://embodied-ai-radar.com/e/arxiv-2304.03047.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.30043", "title": "TARSphere: A Robot-Oriented Full-Stack Framework for Long-Horizon Embodied Agents in Evolving Physical and Social Environments", "type": "paper", "arxiv": "2610.30043", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/xuyicheng-zju/TARSphere", "project": "https://xuyicheng-zju.github.io/TARSphere/", "topics": [ "navigation", "datasets" ], "tags": [ "benchmark", "mllm-agent", "long-horizon", "hri", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-25", "takeaway_en": "Simulator with embodied NPCs, dialogue and event-driven task states, plus a skill harness for frozen MLLMs; its 360-episode bench reports general MLLMs can beat embodied ones; task updates and HRI stay hard.", "takeaway_zh": "带具身 NPC、受限对话和事件驱动任务状态机的物理仿真框架，并提供让冻结多模态大模型调用多级技能的执行框架；基于 30 个室内场景构建 360 回合基准，评测 8 个模型，报告通用模型可胜过具身专用模型，任务变更和人机协作仍是难点。", "github_stars": 31, "page": "https://embodied-ai-radar.com/e/arxiv-2610.30043.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.25959", "title": "EmbodiedWorld-200K: Toward Embodied World Agents via Embodied-Planning Dataset and Interactive World Models", "type": "paper", "arxiv": "2610.25959", "year": 2026, "venue": "NeurIPS 2026", "org": null, "url": null, "code": "https://github.com/XiaokunFeng/EmbodiedWorld-200K", "project": "https://xiaokunfeng.github.io/EmbodiedWorld-200K/", "topics": [ "navigation", "datasets", "world-models" ], "tags": [ "dataset", "navigation-planning", "video-world-model", "rl-finetuning", "open-world" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "200K+ navigation-planning samples annotated from camera trajectories up to instructions; RL post-training inside a video world model, used as a cheap stand-in environment, reportedly helps on unseen scenes.", "takeaway_zh": "包含 20 万余条导航规划样本的数据集，场景多样，标注从相机运动轨迹到高层语义指令；并把视频世界模型当作低成本交互环境做强化学习后训练，报告称在未见场景上有明显提升。", "github_stars": 83, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25959.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.23896", "title": "InJePA: Intention-First Latent Planning with Coupled JEPA World Models for Visual Navigation", "type": "paper", "arxiv": "2610.23896", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/zhuxun132/InJepa", "project": null, "topics": [ "navigation", "world-models" ], "tags": [ "jepa", "latent-planning", "image-goal", "habitat", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Two coupled JEPAs share a latent space: one proposes goal-conditioned latent intentions, the other predicts consequences, closing an intention-action-consequence loop. Evaluated on image-goal navigation in Habitat MP3D.", "takeaway_zh": "两个耦合的 JEPA 共享隐空间：一个按目标提出隐式“意图”，另一个预测动作后果，形成意图-动作-后果闭环，让世界模型直接参与构造规划；在 Habitat MP3D 图像目标导航上评测。", "github_stars": 53, "page": "https://embodied-ai-radar.com/e/arxiv-2610.23896.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.24122", "title": "HAVANA: A Hierarchical Video-Action Model for Vision-Language Navigation", "type": "paper", "arxiv": "2610.24122", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Anastasiawd/HAVANA", "project": "https://github.com/Anastasiawd/HAVANA", "topics": [ "navigation", "world-models" ], "tags": [ "vln", "video-action-model", "dual-system", "sim", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Dual-system video-action model for VLN: a slow system turns history and instruction into a compact latent plan, a fast one turns plan + latest view into local trajectories; no video is generated at inference.", "takeaway_zh": "面向视觉语言导航的双系统视频-动作模型：慢系统根据历史观测与指令生成紧凑的隐式规划，快系统结合该规划与最新观测输出局部轨迹；推理时不生成未来视频。代码与权重称将在该仓库发布。", "github_stars": 4, "page": "https://embodied-ai-radar.com/e/arxiv-2610.24122.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.33546", "title": "MOON: Learning When to Forget and How to Move On in Vision-and-Language Navigation", "type": "paper", "arxiv": "2610.33546", "year": 2026, "venue": null, "org": "SJTU", "url": null, "code": "https://github.com/AutoLab-SAI-SJTU/MOON", "project": null, "topics": [ "navigation", "learning" ], "tags": [ "vln", "recovery", "memory", "grpo", "r2r-ce" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Off-route VLN agents keep following their stale history ('behavioral inertia'). MOON adds a deviation judge that clears it and a GRPO-trained recovery policy (<1% params); reports +5.2 / +6.0 points on R2R-CE / RxR-CE.", "takeaway_zh": "发现 VLN 智能体偏离路线后会被历史记忆“惯性”拖住。MOON 给冻结的导航策略加上偏航判断器（判断何时清空历史）和 GRPO 训练的恢复策略，参数增加不到 1%；报告在 R2R-CE / RxR-CE 上分别提升 5.2 / 6.0 个百分点。", "github_stars": 12, "page": "https://embodied-ai-radar.com/e/arxiv-2610.33546.html", "labs": [ "sjtu" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.33794", "title": "One Scan is Enough: Demonstration-Free Adaptation for Language-Guided Navigation", "type": "paper", "arxiv": "2610.33794", "year": 2026, "venue": "NeurIPS 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation", "vla" ], "tags": [ "vln", "scene-adaptation", "real-to-sim", "iphone-scan", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Adapts a language-guided navigation policy to a new building from one iPhone scan, compiled into a training environment with synthetic instructed episodes. Reports 85% real success vs 49% for a demo-fine-tuned baseline.", "takeaway_zh": "只需用 iPhone 扫描一次新建筑：扫描被编译成训练环境并自动合成带语言指令的导航回合，再用无评论家的闭式 Pareto 效用适配策略；报告真机成功率 85%，用人类示教后训练的基线为 49%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.33794.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.31762", "title": "See What I Need, Dream What I Want: A Navigation Foundation Model on a Self-Constructed Representation Space", "type": "paper", "arxiv": "2610.31762", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://wang-yinxi.github.io/representnav/", "topics": [ "navigation", "world-models" ], "tags": [ "vln", "foundation-model", "jepa", "latent-prediction", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "RepresentNav fuses several vision foundation models into a policy-learned space and predicts the next latent before acting (JEPA-style); reports 83.8% on VLNVerse fine-grained (+20.05 pts), real-robot tests.", "takeaway_zh": "RepresentNav 用一个小型 Transformer 融合多个视觉基础模型各层特征，构建由导航目标自行塑造的表示空间，并在每步动作前预测下一时刻的潜在表示（JEPA 式）；报告 VLNVerse 细粒度成功率 83.8%（领先 20.05 个百分点），并在真机上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31762.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.33158", "title": "Gaussian Episodic Memory for Long-Term Spatiotemporal Reasoning", "type": "paper", "arxiv": "2610.33158", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://abwerby.github.io/GEMS/", "topics": [ "navigation", "perception", "datasets" ], "tags": [ "3dgs", "spatiotemporal-memory", "scene-change", "question-answering", "benchmark", "real", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-29", "takeaway_en": "GEMS keeps a 3D Gaussian Splatting map that is updated only where the scene changes, and an agent renders views on demand to answer when/where questions. Introduces GEMS-Bench (15 scenes, 473 QA pairs).", "takeaway_zh": "GEMS 维护一张只在场景变化处增量更新的 3DGS 地图，智能体按需渲染新视角来回答时空问题和定位物体；同时发布 GEMS-Bench（3 个真实 + 12 个合成环境，473 个问答对），报告在物体定位和时空问答上优于近期基线。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.33158.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.34674", "title": "Benchmarking Obstacle-Conditioned Safety in Household Robot Navigation", "type": "paper", "arxiv": "2610.34674", "year": 2026, "venue": null, "org": "Seoul National University", "url": null, "code": "https://github.com/OSMA-Bench/OSMA-Bench", "project": "https://osma-bench.github.io/", "topics": [ "navigation", "datasets" ], "tags": [ "safety", "benchmark", "household", "vlm", "vla", "evaluation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "OSMA-Bench: matched scenes that differ only in obstacle type test whether navigation policies slow down and keep clearance by how risky an obstacle is. VLM and VLA policies recognise the risk but rarely act on it.", "takeaway_zh": "OSMA-Bench 用仅障碍物类型不同的配对场景，检验导航策略能否按障碍物危险程度调整间距与速度，并提出语义敏感度指数 SSI。结果显示 VLM 与 VLA 策略能识别哪些障碍更危险，却很少把这种判断体现在实际运动中；在 VLM 方案中加入显式参数推断步骤有所改善。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2610.34674.html", "labs": [], "code_license": "" }, { "id": "arxiv-2606.16953", "title": "SidewalkBench: Benchmarking Visual Navigation on Urban Sidewalks", "type": "paper", "arxiv": "2606.16953", "year": 2026, "venue": "CoRL 2026", "org": "UCLA", "url": null, "code": "https://github.com/VAIL-UCLA/SidewalkBench", "project": "https://vail-ucla.github.io/SidewalkBench/", "topics": [ "navigation" ], "tags": [ "benchmark", "sidewalk", "isaac-sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Benchmark for visual navigation on urban sidewalks, built on Isaac Sim with procedurally generated and real-world environments.", "takeaway_zh": "CoRL 2026 Spotlight。城市人行道视觉导航基准，基于 Isaac Sim，包含程序生成与真实世界环境。", "github_stars": 26, "page": "https://embodied-ai-radar.com/e/arxiv-2606.16953.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.00990", "title": "OSCAR: Obstacle Survival Curves for Adaptive Robot Navigation", "type": "paper", "arxiv": "2606.00990", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "survival-analysis", "obstacles" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Decides whether to wait or reroute at a blocked route by modeling how long each obstacle class tends to persist.", "takeaway_zh": "CoRL 2026 Spotlight。通过建模各类障碍物通常停留多久，决定在路线受阻时等待还是改道。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.00990.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.39287", "title": "OpenFlyV2: An onboard vision-language-action model for aerial perception and navigation", "type": "paper", "arxiv": "2610.39287", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Ideal-ljl/AeroBrain", "project": "https://ideal-ljl.github.io/AeroBrain/", "topics": [ "navigation" ], "tags": [ "aerial", "vla", "onboard" ], "robot_types": [ "aerial" ], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "A single onboard VLA for drones: one shared vision-language representation serves semantic understanding, spatial reasoning and closed-loop waypoint control.", "takeaway_zh": "面向无人机的机载 VLA：同一视觉-语言表征同时支撑语义理解、空间推理与闭环航点控制。", "github_stars": 26, "page": "https://embodied-ai-radar.com/e/arxiv-2610.39287.html", "labs": [], "code_license": "" }, { "id": "arxiv-2611.05129", "title": "WayTrace: A Map-to-Language-to-Trace Hierarchy for Embodied Urban Navigation", "type": "paper", "arxiv": "2611.05129", "year": 2026, "venue": null, "org": "ETH Zurich", "url": null, "code": null, "project": "https://way-trace.github.io", "topics": [ "navigation" ], "tags": [ "real", "legged", "outdoor", "language" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A vision-language model turns web-map directions into one atomic command at a time, a predictor draws the path in the image, and RL tracks it; a wheeled-legged robot completes real missions of up to 1.37 km.", "takeaway_zh": "视觉语言模型把网络地图导航拆成一条条原子指令，预测器在图像上画出路径，强化学习负责跟踪；轮腿机器人完成最长 1.37 公里的真实任务。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05129.html", "labs": [ "eth" ], "code_license": null }, { "id": "arxiv-2611.04392", "title": "Aero-VPA: Few-Step Flow-Matching Control over Non-Reconstructive JEPA Latents for Agile Flight on Edge Hardware", "type": "paper", "arxiv": "2611.04392", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/ylee-sling/Aero-VPA", "project": "https://huggingface.co/ylee-sling/aero-vpa", "topics": [ "navigation" ], "tags": [ "sim", "drone", "jepa", "flow-matching" ], "robot_types": [ "aerial" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A compact JEPA-plus-flow-matching quadrotor policy with an unusually candid report: cloning failed all 60 missions, DAgger plateaued, and a runtime shield raised simulated forest success from 21.1% to 52.2%.", "takeaway_zh": "紧凑的 JEPA 加流匹配四旋翼策略，报告异常坦率：行为克隆 60 次任务全败，DAgger 进入瓶颈，运行时安全层将仿真森林任务成功率从 21.1% 提至 52.2%。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2611.04392.html", "labs": [], "code_license": "GPL-3.0" }, { "id": "arxiv-2607.03792", "title": "From Region Arrival to Instance-Level Grounding in Vision-and-Language Navigation", "type": "paper", "arxiv": "2607.03792", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation", "vla" ], "tags": [ "vln", "grounding", "metrics" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Argues VLN success metrics hide a 'last-3-meter grounding gap': agents stop nearby without facing the target. Adds instance-level metrics and REALM, a refinement module for any backbone.", "takeaway_zh": "指出 VLN 成功率掩盖了“最后 3 米定位缺口”：智能体停在附近却未对准目标。提出实例级指标和可接入任意主干的修正模块 REALM。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.03792.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.15251", "title": "Driving, Fast or Slow? Neuro-Symbolic Guidance for Motion Prediction in Multi-Modal Ground Mobility", "type": "paper", "arxiv": "2606.15251", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "motion-prediction", "neuro-symbolic", "driving" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "TraCS adds interpretable, probabilistic traffic-rule logic to black-box motion predictors, generated from natural-language regulations; reports consistent gains on Argoverse 2. Oral.", "takeaway_zh": "TraCS 将由自然语言交规生成的可解释概率逻辑加到黑盒运动预测模型上；报告在 Argoverse 2 上稳定提升。Oral 论文。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.15251.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.18850", "title": "Collaborative Planning for UAV-UGV Teams under Operational Constraints via Masked Error-bounded Neuro-Symbolic Diffusion", "type": "paper", "arxiv": "2610.18850", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation", "learning" ], "tags": [ "multi-robot", "diffusion", "neuro-symbolic" ], "robot_types": [ "mobile", "aerial" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "MEND plans UAV-UGV recharge teamwork with masked diffusion, enforcing operational rules at execution through a logic-based action mask; tested on real maps and physical robot teams.", "takeaway_zh": "MEND 用掩码扩散规划无人机与地面车协同充电任务，在执行时通过逻辑动作掩码强制满足运行约束；在真实地图和实体机器人队伍上测试。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.18850.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.05738", "title": "OdorSpaces: Visual-Olfactory Embodied Navigation in 3D Environments", "type": "paper", "arxiv": "2611.05738", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation", "sim", "datasets" ], "tags": [ "olfaction", "habitat", "benchmark", "rl", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Adds simulated odour transport to Habitat-Sim using Fire Dynamics Simulator, giving a smell-and-vision navigation benchmark for finding invisible gas leaks and early-stage fires, with a multimodal RL baseline.", "takeaway_zh": "用 Fire Dynamics Simulator 为 Habitat-Sim 加入气味传输仿真，构成视觉加嗅觉的导航基准，任务是定位不可见的气体泄漏和早期火源，并提供多模态强化学习基线。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05738.html", "labs": [], "code_license": null }, { "id": "arxiv-2505.08712", "title": "NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance", "type": "paper", "arxiv": "2505.08712", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/InternRobotics/NavDP", "project": "https://wzcai99.github.io/navigation-diffusion-policy.github.io", "topics": [ "navigation" ], "tags": [ "diffusion-policy", "sim2real", "navigation" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "NavDP trains a navigation diffusion policy in simulation with privileged-information guidance, for sim-to-real use in dynamic open-world scenes.", "takeaway_zh": "NavDP 在仿真中借助特权信息引导训练导航扩散策略，用于动态开放环境的仿真到真实迁移。", "github_stars": 831, "page": "https://embodied-ai-radar.com/e/arxiv-2505.08712.html", "labs": [], "code_license": "" }, { "id": "arxiv-2410.01273", "title": "CANVAS: Commonsense-Aware Navigation System for Intuitive Human-Robot Interaction", "type": "paper", "arxiv": "2410.01273", "year": 2024, "venue": "ICRA 2025", "org": null, "url": null, "code": null, "project": "https://worv-ai.github.io/canvas", "topics": [ "navigation" ], "tags": [ "commonsense", "imitation-learning", "hri" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "CANVAS lets people guide navigation with abstract cues such as verbal commands or rough sketches, even when they are noisy.", "takeaway_zh": "CANVAS 让人用语音指令或粗略草图等抽象线索引导导航，即使线索不精确。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2410.01273.html", "labs": [], "code_license": null }, { "id": "arxiv-2403.08605", "title": "Language-Grounded Dynamic Scene Graphs for Interactive Object Search with Mobile Manipulation", "type": "paper", "arxiv": "2403.08605", "year": 2024, "venue": null, "org": null, "url": null, "code": "https://github.com/robot-learning-freiburg/MoMa-LLM", "project": "https://moma-llm.cs.uni-freiburg.de", "topics": [ "navigation" ], "tags": [ "scene-graph", "llm", "mobile-manipulation" ], "robot_types": [ "arm", "mobile" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "MoMa-LLM grounds LLMs in dynamic scene graphs for long-horizon object search with a mobile manipulator in unexplored spaces.", "takeaway_zh": "MoMa-LLM 用动态场景图为大模型提供依据，让移动操作机器人在未探索环境中长程搜索物体。", "github_stars": 111, "page": "https://embodied-ai-radar.com/e/arxiv-2403.08605.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2511.20216", "title": "CostNav: A Navigation Benchmark for Real-World Economic-Cost Evaluation of Physical AI Agents", "type": "paper", "arxiv": "2511.20216", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/worv-ai/CostNav", "project": null, "topics": [ "navigation", "datasets", "sim" ], "tags": [ "benchmark", "delivery-robot", "economics", "isaac-sim", "sidewalk" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Scores delivery-robot navigation by money: cost, revenue and break-even, using Isaac Sim plus public filings and injury data. Reports that none of seven baselines is economically viable yet.", "takeaway_zh": "以经济账评估配送机器人导航：在 Isaac Sim 中结合公开财报与伤害数据计算成本、收入与盈亏平衡。报告七种基线方法目前都无法盈利。", "github_stars": 26, "page": "https://embodied-ai-radar.com/e/arxiv-2511.20216.html", "labs": [], "code_license": "" }, { "id": "arxiv-2508.01415", "title": "RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems", "type": "paper", "arxiv": "2508.01415", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/SP4595/RoboMemory-Code", "project": null, "topics": [ "navigation" ], "tags": [ "memory", "agent", "vlm", "knowledge-graph", "long-horizon" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Brain-inspired memory for embodied agents (temporal, spatial, semantic, episodic) with a spatial knowledge graph. Reports +26.5 points for Qwen2.5-VL-72B on EmbodiedBench.", "takeaway_zh": "受大脑启发的具身智能体记忆框架（时间、空间、语义、情景记忆），配合动态空间知识图谱。报告让 Qwen2.5-VL-72B 在 EmbodiedBench 上平均成功率提高 26.5 个百分点。", "github_stars": 16, "page": "https://embodied-ai-radar.com/e/arxiv-2508.01415.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2607.02222", "title": "CoFL-S: Spatially Queryable Sector Flow Fields for Local Language-Conditioned Navigation", "type": "paper", "arxiv": "2607.02222", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/ut-dragon-lab/CoFL", "project": null, "topics": [ "navigation" ], "tags": [ "vln", "flow-field", "low-level-control", "habitat", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Low-level VLN action head that predicts a language-conditioned flow field over the visible sector and rolls it out into trajectories. Adds a continuous-time Habitat benchmark.", "takeaway_zh": "低层视觉语言导航动作头：在可见扇区上预测语言条件的流场并展开为连续轨迹；并提供连续时间的 Habitat 评测基准。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2607.02222.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2607.16956", "title": "G2-Nav: Grounded and Guarded Vision-Language Costmaps for Robot Social Navigation", "type": "paper", "arxiv": "2607.16956", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/centiLinda/G2-Nav", "project": null, "topics": [ "navigation" ], "tags": [ "social-navigation", "vlm", "costmap", "safety", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Social navigation where the VLM writes a costmap instead of choosing actions, checks upstream tracking, and a fast safety check guards against latency. Real-world tests.", "takeaway_zh": "社交导航：让 VLM 生成代价地图而非直接决策，并校验上游跟踪，另加高频安全检查应对延迟；真机验证。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2607.16956.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.07417", "title": "MessyNav: Zero-Shot Navigation via Manipulation in Messy Environments", "type": "paper", "arxiv": "2611.07417", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation", "manipulation" ], "tags": [ "interactive-navigation", "mobile-manipulation", "vlm", "affordance", "zero-shot", "real" ], "robot_types": [ "arm", "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Navigates cluttered spaces by deciding, with VLM affordance reasoning, whether to swipe, pick up or avoid each blocking object. Zero-shot; reports +27 points over the best baseline on a real mobile manipulator.", "takeaway_zh": "在杂乱环境中导航：用 VLM 推理每个挡路物体该推开、拿起还是绕开。零样本，报告在真实移动操作机器人上比最强基线高 27 个百分点。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.07417.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.15976", "title": "MessyMem: Learning-from-Doing Memory for Mobile Manipulation", "type": "paper", "arxiv": "2609.15976", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://messymem.github.io", "topics": [ "navigation", "manipulation" ], "tags": [ "memory", "mobile-manipulation", "scene-graph", "long-horizon", "real" ], "robot_types": [ "arm", "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Persistent memory for mobile manipulators: a 3D scene graph updated with what the robot learns by acting (locked cabinets, object locations). Reports 80% progress over a 25-task, 3-hour run.", "takeaway_zh": "移动操作机器人的持久记忆：在三维场景图中记录通过交互学到的知识（如柜子上锁、物体位置）。报告在连续 25 个任务、3 小时的仿真中任务进度达 80%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.15976.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.08444", "title": "Safe Task Planning with Long-Term Graph Memory for Embodied Agents", "type": "paper", "arxiv": "2609.08444", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/lty759/SafeMem", "project": "https://sites.google.com/view/safemem", "topics": [ "navigation" ], "tags": [ "safety", "task-planning", "graph-memory", "llm", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Safe task planning for embodied agents: keeps a long-term semantic graph of the scene so an LLM risk predictor can catch hazards outside the current view and trigger replanning. IS-Bench + real robot.", "takeaway_zh": "具身智能体的安全任务规划：持续维护场景的长期语义图记忆，让 LLM 风险预测器发现视野外的隐患并触发重规划。在 IS-Bench 与真机上验证。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2609.08444.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2609.40245", "title": "STARS: From Spatiotemporal Dynamics to Social Representations in Human-Robot Interaction", "type": "paper", "arxiv": "2609.40245", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/ut-amrl/stars", "project": "https://larg.github.io/stars/", "topics": [ "navigation" ], "tags": [ "social-navigation", "benchmark", "vlm", "vqa", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "SocialNav-SUB: a VQA benchmark testing whether VLMs understand real social navigation scenes (spatial, temporal, social reasoning). The best VLM still trails a rule-based baseline and human consensus.", "takeaway_zh": "SocialNav-SUB：检验 VLM 能否理解真实社交导航场景（空间、时序与社会推理）的视觉问答基准。最好的 VLM 仍落后于规则基线和人类共识。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2609.40245.html", "labs": [], "code_license": "" }, { "id": "arxiv-2602.23024", "title": "InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation", "type": "paper", "arxiv": "2602.23024", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/liujiahao2077/InCoM", "project": "https://liujiahao2077.github.io/InCoM.github.io", "topics": [ "navigation" ], "tags": [ "mobile-manipulation", "flow-matching", "attention", "base-arm-coordination", "real" ], "robot_types": [ "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Mobile manipulation policy that infers motion intent to reweight perception and decodes base and arm actions with a coupled flow-matching head. Reports +23.6 to +33.0 points over DSPv2 on three tasks.", "takeaway_zh": "移动操作策略：推断运动意图以动态分配感知注意力，并用显式建模底盘-机械臂交互的流匹配解码器输出动作。报告在三个任务上比 DSPv2 高 23.6–33.0 个百分点。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2602.23024.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.21211", "title": "An Agentic Framework for Proactive Robots with Procedural Assistance Memory", "type": "paper", "arxiv": "2610.21211", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/DAVIAN-Robotics/PAM", "project": "https://davian-robotics.github.io/PAM/", "topics": [ "navigation" ], "tags": [ "proactive-assistance", "memory", "vlm", "hri", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Proactive assistant robot: a VLM reads a state-machine memory of the user's routine to decide when to help, and a VLA executes. The memory is revised daily by reflection, without retraining.", "takeaway_zh": "主动协助机器人：VLM 读取以有限状态机表示的用户日常流程记忆，决定何时提供何种帮助，再由 VLA 执行；记忆每天通过反思更新，无需重新训练。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2610.21211.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.15572", "title": "Look Before You Move: Agent-Interface-Aligned Supervision for Open-World Mobile Manipulation", "type": "paper", "arxiv": "2610.15572", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://connerqiu.github.io/look-before-you-move/", "topics": [ "navigation" ], "tags": [ "mobile-manipulation", "active-vision", "vlm-agent", "data-collection", "real" ], "robot_types": [ "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Collects VLM-agent data by having teachers act through the agent's own skill interface, including where to look. Reports 71.76% on 170 held-out open-world mobile-manipulation episodes and 38% zero-shot on a real robot.", "takeaway_zh": "让示教者直接通过智能体自身的技能接口（包括“往哪看”）执行动作来采集 VLM 智能体训练数据。报告在 170 个开放世界移动操作测试集上成功率 71.76%，零样本真机 38%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.15572.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.15154", "title": "Task-Aware Environment Augmentation for Reliable Navigation via Shielded Conditional Diffusion", "type": "paper", "arxiv": "2606.15154", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://scoda-diffusion.github.io/", "topics": [ "navigation" ], "tags": [ "localization", "fiducial-markers", "diffusion", "planning", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Flips the problem: instead of adapting the robot, decides where to place a few fiducial markers so a planned trajectory can be executed reliably, using a shielded conditional diffusion model. Sim + hardware.", "takeaway_zh": "反过来思考：不改机器人，而是决定在环境中哪里放少量视觉标记，使规划好的轨迹能可靠执行；方法为带安全约束采样的条件扩散模型。仿真与真机验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.15154.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.15550", "title": "Robots as Tokens: Unified Diffusion Transformer for Coordinated Multi-Robot Trajectory Generation", "type": "paper", "arxiv": "2606.15550", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://bairuofei.github.io/roken-project-page/", "topics": [ "navigation" ], "tags": [ "multi-robot", "diffusion-transformer", "trajectory-generation", "connectivity", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Diffusion transformer that treats each robot as a token and generates coordinated multi-robot trajectories in one pass, meeting safety and connectivity constraints; trained across team sizes.", "takeaway_zh": "扩散 Transformer 把每台机器人表示为一个 token，一次前向生成满足安全与连通约束的多机协同轨迹；可在不同团队规模的数据上混合训练。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.15550.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.18321", "title": "CoSMO: Conformal Semantic Mapping for Open-World Safe Robot Navigation", "type": "paper", "arxiv": "2610.18321", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Yangming911/CoSMO", "project": "https://yangming911.github.io/cosmo-page/", "topics": [ "navigation" ], "tags": [ "conformal-prediction", "semantic-mapping", "safety", "open-vocabulary", "quadruped" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Calibrates VLM semantic maps with online conformal prediction so each object class gets a safety margin with coverage guarantees as the vocabulary grows. Reports 61.6% fewer semantic collisions on MP3D.", "takeaway_zh": "用在线共形预测校准 VLM 语义地图，使不同类别物体获得有覆盖率保证的安全距离，且支持词表扩展。报告在 MP3D 上语义碰撞减少 61.6%，四足真机覆盖率 90.73%。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2610.18321.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2607.28560", "title": "X-NavDP: Generalizing Navigation Diffusion Policy to Novel Behavior and Embodiments with Group Q-score Reweighted Matching", "type": "paper", "arxiv": "2607.28560", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/InternRobotics/NavDP", "project": "https://yty-sky.github.io/x-navdp-project-page", "topics": [ "navigation" ], "tags": [ "diffusion-policy", "rl-finetuning", "cross-embodiment", "visual-navigation", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "RL post-training for navigation diffusion policies with perturbation-based exploration and group-normalized Q-scores, across embodiments. Reports sim success from 61.20% to 84.28% and real hard cases from 10% to 65%.", "takeaway_zh": "面向导航扩散策略的强化学习后训练：保留预训练先验的扰动探索 + 组归一化 Q 分重加权匹配，跨多种本体训练。报告仿真成功率从 61.20% 升到 84.28%，真机困难场景从 10% 升到 65%。", "github_stars": 831, "page": "https://embodied-ai-radar.com/e/arxiv-2607.28560.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.26852", "title": "CoopUQ: Robust Multi-Robot Social Navigation via Uncertainty-Aware Cooperative Forecasting", "type": "paper", "arxiv": "2610.26852", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/tasl-lab/CoopUQ", "project": "https://coopuq.github.io/", "topics": [ "navigation" ], "tags": [ "multi-robot", "social-navigation", "uncertainty", "marl", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Multi-robot social navigation: robots fuse features to forecast occupancy maps with uncertainty, and a constrained multi-agent RL policy avoids regions predicted occupied or uncertain. Unity sim + real robots.", "takeaway_zh": "多机器人社交导航：多机融合特征预测未来占据栅格及其不确定性，约束多智能体强化学习策略据此避开被预测占据或高度不确定的区域。Unity 仿真与真机验证。", "github_stars": 5, "page": "https://embodied-ai-radar.com/e/arxiv-2610.26852.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.09302", "title": "Adaptive Risk-Certified Event-Triggered Replanning for Dynamic Navigation", "type": "paper", "arxiv": "2610.09302", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://nail-uh.github.io/corl2026.github.io/", "topics": [ "navigation" ], "tags": [ "safety", "conformal-prediction", "mpc", "replanning", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Uses calibrated barrier risk (CVaR along the MPC rollout) as an early warning to switch to a safer planning mode. Reports 83.3% fewer collisions than a safety-filter-only baseline.", "takeaway_zh": "以校准后的障碍函数风险（MPC 预测轨迹上的 CVaR）作为预警信号，及时切换到更安全的规划模式。报告碰撞比仅用安全滤波器的基线减少 83.3%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.09302.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.09358", "title": "COOL: Curiosity-Driven Object Ownership Learning for Personalized Robotic Assistance", "type": "paper", "arxiv": "2610.09358", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "personalization", "object-ownership", "spatial-memory", "curiosity", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns who owns which objects from everyday observation and keeps a long-term spatial memory refreshed by curiosity-driven exploration, so commands like 'find my bottle' work.", "takeaway_zh": "从日常观察中学习“哪个物品属于谁”，并通过好奇心驱动的探索刷新长期空间记忆，从而执行“找我的水杯”这类个性化指令。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.09358.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.07730", "title": "Seeing Beyond the Next Step: World-Model-Guided Human-Like Navigation in Multi-Agent Scenes", "type": "paper", "arxiv": "2611.07730", "year": 2026, "venue": "NeurIPS 2026", "org": null, "url": null, "code": null, "project": "https://seoneun.github.io/-NeurIPS-2026-Seeing-Beyond-the-Next-Step/", "topics": [ "navigation" ], "tags": [ "social-navigation", "world-model", "preference-distillation", "crowds" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Uses two world models (social dynamics, scene semantics) only at training time to score candidate actions, distilling that foresight into a single-pass policy. Observers rated the motion more human-like than baselines.", "takeaway_zh": "仅在训练时用两个世界模型（社交动态、场景语义）为候选动作打分，再把这种前瞻能力蒸馏进单次前向的策略。观察者认为其运动比基线更像人。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.07730.html", "labs": [], "code_license": null }, { "id": "arxiv-2511.13207", "title": "PIGEON: VLM-Driven Object Navigation via Points of Interest Selection", "type": "paper", "arxiv": "2511.13207", "year": 2025, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "object-navigation", "vlm", "rlvr", "habitat" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Has a VLM pick among sparse 'points of interest' (frontiers, likely targets, stairs) tied to waypoints and raw views; planners do the motion. The checkable choices allow RLVR on local VLMs without CoT labels.", "takeaway_zh": "让视觉语言模型在稀疏的“兴趣点”（探索前沿、疑似目标、楼梯等，每个绑定航点与原始视角）中做选择，底层规划器负责运动。这种可验证的决策使其能用 RLVR 训练本地 VLM，无需人工思维链标注。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2511.13207.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.21138", "title": "Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems", "type": "paper", "arxiv": "2604.21138", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "multi-robot", "task-and-motion-planning", "rlvr", "llm" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Joint task and motion planning for up to nine robots among dense obstacles: waypoints parameterize motion, and a curriculum with modified RLVR passes motion-planner feasibility back to the task planner (BoxNet3D-OBS).", "takeaway_zh": "面向密集障碍中多达九台机器人的任务与运动联合规划：用航点参数化运动轨迹，并以课程式训练和改进的 RLVR 将运动规划器的可行性反馈传回任务规划器（BoxNet3D-OBS 基准）。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.21138.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.07629", "title": "LCLA: Language-Conditioned Latent Alignment for Vision-Language Navigation", "type": "paper", "arxiv": "2602.07629", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "vln", "privileged-learning", "latent-alignment", "vlm" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Trains a privileged-state expert, freezes its latent interface and action head, then fits a light adapter mapping frozen-VLM features of image + instruction into it: supervised alignment, not end-to-end RL.", "takeaway_zh": "先用特权状态训练专家策略并冻结其潜空间接口与动作头，再训练轻量适配器把冻结 VLM 提取的图像与指令特征映射到该潜空间：用监督式对齐代替端到端策略优化。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.07629.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.15400", "title": "One Agent to Guide Them All: Empowering MLLMs for Vision-and-Language Navigation via Explicit World Representation", "type": "paper", "arxiv": "2602.15400", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "vln", "mllm", "zero-shot", "metric-map", "drone", "real" ], "robot_types": [ "aerial" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Decouples spatial estimation from MLLM planning via an interactive metric world map plus counterfactual reasoning. Reports zero-shot 48.8% SR on R2R-CE and 42.2% on RxR-CE; runs on a TurtleBot 4 and a drone.", "takeaway_zh": "通过可交互的度量世界表示把空间状态估计与多模态大模型的语义规划解耦，并引入反事实推理。报告零样本 R2R-CE 成功率 48.8%、RxR-CE 42.2%，并在 TurtleBot 4 与自制无人机上实机部署。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.15400.html", "labs": [], "code_license": null }, { "id": "arxiv-2605.07496", "title": "PathPainter: Transferring the Generalization Ability of Image Generation Models to Embodied Navigation", "type": "paper", "arxiv": "2605.07496", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "bev", "image-generation", "language-navigation", "quadruped", "drone", "real" ], "robot_types": [ "legged", "aerial" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "An image generation model reads a language command and paints the goal and traversability masks onto a bird's-eye-view image; cross-view localization curbs drift. A quadruped finishes a 285 m outdoor route.", "takeaway_zh": "用图像生成模型理解语言指令，在鸟瞰图上标出目标与可通行区域，并以跨视角定位抑制里程计漂移。四足机器人借此完成 285 米的户外长距离导航。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2605.07496.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.13476", "title": "AsyncVLA: An Asynchronous VLA for Fast and Robust Navigation on the Edge", "type": "paper", "arxiv": "2602.13476", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "vla", "latency", "edge", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "A large foundation model on a remote workstation gives high-level guidance while a light onboard Edge Adapter refines actions at high rate. Reports 40% higher success than baselines with delays up to 6 s.", "takeaway_zh": "大模型在远端工作站提供高层指引，机载轻量 Edge Adapter 高频修正动作，两路异步运行并端到端微调。在通信延迟最高 6 秒的真实导航中，成功率比基线高 40%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.13476.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.12603", "title": "From Imitation to Alignment: Human-Preference Flow Policies for Long-Horizon Sidewalk Navigation", "type": "paper", "arxiv": "2606.12603", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "sidewalk", "flow-matching", "preference-learning", "monocular", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "FlowPilot: a mapless, monocular-RGB sidewalk navigation policy pre-trained with anchored flow matching on fleet data, then tuned on a small set of human interventions by preference learning.", "takeaway_zh": "FlowPilot：仅用单目 RGB、无需地图的人行道导航策略，先用锚定流匹配在大规模机器人车队数据上预训练，再用少量人工接管数据做偏好学习微调。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12603.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.16103", "title": "PathLift: Depth-Conditioned Metric Lifting for Visual-Navigation Priors", "type": "paper", "arxiv": "2610.16103", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "visual-navigation", "depth", "adapter", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Visual-navigation policies say where to go but not how far. PathLift reads sensor depth (RGB-D, stereo or LiDAR) and fits per-waypoint magnitude and lateral residuals, turning unitless paths metric in one pass.", "takeaway_zh": "视觉导航策略只给方向、不给距离。PathLift 读取传感器深度（RGB-D、双目或激光雷达），为每个航点拟合长度与横向残差，一次前向就把无单位轨迹变为度量路径。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.16103.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.08029", "title": "IntentNav: Learning Spatial-Visual Object Navigation from Human Demonstrations", "type": "paper", "arxiv": "2606.08029", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "object-navigation", "imitation-learning", "vlm", "humanoid", "quadruped" ], "robot_types": [ "humanoid", "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Labels which frontier explains a human demonstrator's next moves, then trains a VLM to choose among BEV-memory candidates. Candidate-level interface transfers zero-shot to wheeled, quadruped and humanoid robots.", "takeaway_zh": "通过前瞻人类示教，标注最能解释其后续搜索方向的前沿，再训练 VLM 在 BEV 记忆中的候选点之间做选择。这种候选级接口零样本迁移到轮式、四足和人形机器人。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.08029.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.04907", "title": "WAM-Nav: Asymmetric Latent World-Action Modeling for Unified Visual Navigation", "type": "paper", "arxiv": "2606.04907", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "world-action-model", "visual-navigation", "diffusion-transformer" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "One diffusion transformer jointly generates long-horizon actions and short-horizon latent visual foresight, avoiding multi-step rollouts. Supports image-goal, point-goal and other goal types in one model.", "takeaway_zh": "用同一个扩散 Transformer 同时生成长时域动作和短时域潜空间视觉预测，避免多步自回归推演；一个模型支持图像目标、点目标等多种目标类型。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.04907.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.17927", "title": "GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation", "type": "paper", "arxiv": "2610.17927", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "memory", "changing-goals", "rl", "quadruped", "real" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Navigation where goals change mid-episode without notice: memory is split into global/local spatial and long/short temporal beliefs, with goal-conditioned rewards. Tested on a Spot quadruped.", "takeaway_zh": "研究回合中目标会无提示地改变的导航：把记忆分解为全局/局部空间与长/短时序信念，并配合目标条件奖励。在 Spot 四足机器人上实机验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.17927.html", "labs": [], "code_license": null }, { "id": "arxiv-2512.21201", "title": "Schrödinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation", "type": "paper", "arxiv": "2512.21201", "year": 2025, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "object-navigation", "world-model", "zero-shot", "quadruped", "real" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "For each candidate path, a 3D world model imagines several possible scene layouts behind occlusions; a future-aware value map combines them to pick waypoints. Tested on a Go2 quadruped.", "takeaway_zh": "对每条候选路径，用 3D 世界模型想象遮挡后面多种可能的场景，再用面向未来的价值地图汇总这些想象来选择航点。在 Go2 四足机器人上测试。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2512.21201.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.06724", "title": "EvoPlan: Evolutionary Neuro-Symbolic Robot Planning with Spatio-Temporal Guarantees", "type": "paper", "arxiv": "2607.06724", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "neuro-symbolic", "stl", "pddl", "llm", "safety" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Mines a Signal Temporal Logic constraint from demos (e.g. social-navigation comfort) by evolutionary search to shield policies, and runs an evolutionary PDDL planner where a local LLM proposes and repairs plans.", "takeaway_zh": "通过进化搜索从示教数据中挖掘信号时序逻辑约束（如社交导航舒适度）来为策略加安全护盾，并用进化式 PDDL 规划器让本地部署的大模型提出和修复计划。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.06724.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.10166", "title": "Future-Aware Flow Planning for Safe UAV Target Following", "type": "paper", "arxiv": "2609.10166", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "drone", "target-following", "flow-matching", "safety" ], "robot_types": [ "aerial" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "UAV target following that conditions a flow-based trajectory generator on predicted target futures and repairs risky trajectory prefixes inside the sampling loop. Best safety-tracking trade-off; Future-MPC is smoother.", "takeaway_zh": "无人机目标跟随：用预测的目标未来轨迹作为条件引导流式轨迹生成，并在采样循环中修复高风险的可执行前段。安全与跟踪的权衡最好，但 Future-MPC 更平滑。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.10166.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.17593", "title": "Training Generalist Navigation Policies via Action Self-Distillation", "type": "paper", "arxiv": "2610.17593", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "navigation", "vlm", "self-distillation", "language-reasoning", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "One VLM acts as teacher (sees the whole trajectory, infers the goal and reasoning) and student (onboard view only), so language-conditioned navigation reasoning is learned with no human labels. 13 real tasks.", "takeaway_zh": "同一个 VLM 既当教师（看到完整轨迹，推断导航目标与推理过程）又当学生（只看机载观测），无需人工标注即可学会以语言为条件的导航推理。在 13 个真实任务上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.17593.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.37557", "title": "Learning Visuomotor Quadrotor Flight Through Dynamic Narrow Gaps via Differentiable Simulation", "type": "paper", "arxiv": "2610.37557", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "quadrotor", "agile-flight", "differentiable-simulation", "visuomotor" ], "robot_types": [ "aerial" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Visuomotor quadrotor policy flies through moving narrow gaps, trained in one stage from scratch by differentiable simulation; a signed-distance-gated objective turns the sparse pass-through event into a dense loss.", "takeaway_zh": "视觉运动四旋翼策略穿越移动的狭窄缝隙，借助可微仿真从零单阶段训练；以到时变缝隙平面的有符号距离门控各项损失，把稀疏的穿越事件变成稠密目标。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.37557.html", "labs": [], "code_license": null }, { "id": "arxiv-2605.24301", "title": "AcroRL: Learning Aggressive Quadrotor Inversion using Bidirectional Thrust", "type": "paper", "arxiv": "2605.24301", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "quadrotor", "aggressive-flight", "bidirectional-thrust", "rl" ], "robot_types": [ "aerial" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "RL policies modulate a reference trajectory for compact quadrotor flips with reversible thrust, one policy per direction. In JAX simulation: 32% lower position RMSE and 57% shorter settling than baselines.", "takeaway_zh": "强化学习策略调制参考轨迹，实现可反向推力四旋翼的紧凑翻转，正翻与回翻各训一个策略。JAX 仿真中位置 RMSE 降低 32%，稳定时间缩短 57%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2605.24301.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.07578", "title": "Approximate Imitation Learning for Event-based Quadrotor Flight in Cluttered Environments", "type": "paper", "arxiv": "2603.07578", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "navigation" ], "tags": [ "event-camera", "quadrotor", "imitation-learning", "agile-flight" ], "robot_types": [ "aerial" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Event-camera drone flight: learn the representation offline from a large dataset, then fine-tune the policy online on cheap state only, with no event rendering. Training drops from 52.44 h to 1.86 h.", "takeaway_zh": "事件相机无人机飞行：先在大规模离线数据上学习表示，再只用轻量状态信息在线微调策略，无需在训练中渲染事件。训练时间从 52.44 小时降到 1.86 小时。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.07578.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.10366", "title": "CPNav: Calibrated Vision-Language Navigation for Object-Goal Search in Unseen Environments", "type": "paper", "arxiv": "2611.10366", "year": 2026, "venue": null, "org": "UCLA", "url": null, "code": "https://github.com/practice-lab-ucla/CPnav", "project": null, "topics": [ "navigation" ], "tags": [ "object-goal-navigation", "vlm", "conformal-prediction", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-10", "takeaway_en": "Wraps VLM action scores for object-goal search in a conformal prediction layer with a user-set risk level, plus collision-free primitives and backtracking. Reports gains on HM3D/MP3D and on a small real robot.", "takeaway_zh": "为目标物体搜索中的 VLM 动作打分加上共形预测校准层（风险水平可设定），结合无碰撞运动基元与回溯探索。在 HM3D/MP3D 及小型真实机器人上报告提升。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2611.10366.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.11148", "title": "MOSU2: Long-range Navigation System with Multi-modal Scene Understanding", "type": "paper", "arxiv": "2611.11148", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://jinggm.github.io/mosu2/", "topics": [ "navigation" ], "tags": [ "outdoor-navigation", "vlm", "social-navigation", "traversability", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-10", "takeaway_en": "Outdoor long-range navigation where a VLM first reasons on context and only pulls in geometry and motion cues when an interaction stays unclear. Reports real-world success up from 61.8% to 94.2% over MOSU.", "takeaway_zh": "户外长距离导航：VLM 先基于场景上下文推理，只有交互情形仍不明确时才调取几何与运动信息。真实实验中导航成功率相对前作 MOSU 从 61.8% 提升到 94.2%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.11148.html", "labs": [], "code_license": null }, { "id": "arxiv-2007.11898", "title": "ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM", "type": "paper", "arxiv": "2007.11898", "year": 2020, "venue": null, "org": "Universidad de Zaragoza", "url": null, "code": "https://github.com/UZ-SLAMLab/ORB_SLAM3", "project": null, "topics": [ "perception" ], "tags": [ "slam", "visual-inertial", "open-source" ], "robot_types": [], "highlight": true, "rating": 3, "added": null, "takeaway_en": "The go-to feature-based SLAM library — monocular, stereo and RGB-D, with IMU fusion and multi-map relocalization.", "takeaway_zh": "最常用的特征点 SLAM 库：支持单目、双目与 RGB-D，可融合 IMU，并支持多地图重定位。", "github_stars": 9242, "page": "https://embodied-ai-radar.com/e/arxiv-2007.11898.html", "labs": [], "code_license": "GPL-3.0" }, { "id": "arxiv-2007.00258", "title": "LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping", "type": "paper", "arxiv": "2007.00258", "year": 2020, "venue": null, "org": "MIT", "url": null, "code": "https://github.com/TixiaoShan/LIO-SAM", "project": null, "topics": [ "perception" ], "tags": [ "lidar", "odometry", "factor-graph" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "LiDAR-inertial odometry built on a factor graph, so GPS and loop closures slot in as extra factors.", "takeaway_zh": "基于因子图的激光-惯性里程计，GPS 与回环可以作为额外因子直接加入。", "github_stars": 4976, "page": "https://embodied-ai-radar.com/e/arxiv-2007.00258.html", "labs": [ "mit" ], "code_license": "BSD-3-Clause" }, { "id": "arxiv-2107.06829", "title": "FAST-LIO2: Fast Direct LiDAR-inertial Odometry", "type": "paper", "arxiv": "2107.06829", "year": 2021, "venue": null, "org": "HKU MaRS Lab", "url": null, "code": "https://github.com/hku-mars/FAST_LIO", "project": null, "topics": [ "perception" ], "tags": [ "lidar", "odometry", "real-time" ], "robot_types": [ "aerial" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Registers raw LiDAR points directly to the map with an incremental k-d tree; fast enough for onboard drone and robot computers.", "takeaway_zh": "用增量 k-d 树把原始激光点直接配准到地图，速度足以在无人机和机器人机载计算机上运行。", "github_stars": 5363, "page": "https://embodied-ai-radar.com/e/arxiv-2107.06829.html", "labs": [ "hku" ], "code_license": "GPL-2.0" }, { "id": "arxiv-2108.10869", "title": "DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras", "type": "paper", "arxiv": "2108.10869", "year": 2021, "venue": null, "org": "Princeton", "url": null, "code": "https://github.com/princeton-vl/DROID-SLAM", "project": null, "topics": [ "perception" ], "tags": [ "slam", "learned", "visual" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "End-to-end learned visual SLAM — recurrent optical-flow updates plus a differentiable bundle-adjustment layer.", "takeaway_zh": "端到端学习的视觉 SLAM：循环光流更新加上可微分的光束法平差层。", "github_stars": 2729, "page": "https://embodied-ai-radar.com/e/arxiv-2108.10869.html", "labs": [ "princeton" ], "code_license": "BSD-3-Clause" }, { "id": "arxiv-2312.06741", "title": "Gaussian Splatting SLAM", "type": "paper", "arxiv": "2312.06741", "year": 2023, "venue": null, "org": "Imperial College London", "url": null, "code": "https://github.com/muskie82/MonoGS", "project": null, "topics": [ "perception" ], "tags": [ "slam", "gaussian-splatting", "dense" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "MonoGS — runs SLAM directly on a 3D Gaussian map, giving camera tracking and a photorealistic dense map from one camera.", "takeaway_zh": "MonoGS：直接在三维高斯地图上做 SLAM，仅用单个相机即可同时完成相机跟踪与照片级稠密建图。", "github_stars": 2191, "page": "https://embodied-ai-radar.com/e/arxiv-2312.06741.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2312.14132", "title": "DUSt3R: Geometric 3D Vision Made Easy", "type": "paper", "arxiv": "2312.14132", "year": 2023, "venue": null, "org": "NAVER LABS Europe", "url": null, "code": "https://github.com/naver/dust3r", "project": null, "topics": [ "perception" ], "tags": [ "3d-reconstruction", "foundation-model" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Regresses 3D point maps straight from image pairs without known camera intrinsics or poses; started the \"3D foundation model\" wave.", "takeaway_zh": "不需要已知相机内参或位姿，直接从图像对回归三维点图，开启了“三维基础模型”这一方向。", "github_stars": 7375, "page": "https://embodied-ai-radar.com/e/arxiv-2312.14132.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2503.11651", "title": "VGGT: Visual Geometry Grounded Transformer", "type": "paper", "arxiv": "2503.11651", "year": 2025, "venue": null, "org": "Oxford VGG · Meta AI", "url": null, "code": "https://github.com/facebookresearch/vggt", "project": null, "topics": [ "perception" ], "tags": [ "3d-reconstruction", "foundation-model", "feed-forward" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "One feed-forward transformer predicts cameras, depth, point maps and tracks for many views at once, in about a second.", "takeaway_zh": "单个前馈 Transformer 一次性为多视角图像预测相机参数、深度、点图与跟踪，约一秒完成。", "github_stars": 14617, "page": "https://embodied-ai-radar.com/e/arxiv-2503.11651.html", "labs": [ "meta", "oxford" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2312.08344", "title": "FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects", "type": "paper", "arxiv": "2312.08344", "year": 2023, "venue": null, "org": "NVIDIA", "url": null, "code": "https://github.com/NVlabs/FoundationPose", "project": null, "topics": [ "perception", "manipulation" ], "tags": [ "6d-pose", "tracking", "zero-shot" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "6D pose estimation and tracking of unseen objects from a CAD model or a few reference images, with no per-object training.", "takeaway_zh": "只需 CAD 模型或少量参考图像即可对未见过的物体做 6D 位姿估计与跟踪，无需逐物体训练。", "github_stars": 3694, "page": "https://embodied-ai-radar.com/e/arxiv-2312.08344.html", "labs": [ "nvidia" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2401.10891", "title": "Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data", "type": "paper", "arxiv": "2401.10891", "year": 2024, "venue": null, "org": "HKU · TikTok", "url": null, "code": "https://github.com/LiheYoung/Depth-Anything", "project": null, "topics": [ "perception" ], "tags": [ "depth", "monocular", "foundation-model" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Monocular depth model trained on tens of millions of pseudo-labelled images; a common off-the-shelf depth prior for robots.", "takeaway_zh": "在数千万张伪标注图像上训练的单目深度模型，常被机器人系统直接拿来作为深度先验。", "github_stars": 8253, "page": "https://embodied-ai-radar.com/e/arxiv-2401.10891.html", "labs": [ "hku" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2309.16650", "title": "ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning", "type": "paper", "arxiv": "2309.16650", "year": 2023, "venue": null, "org": "University of Toronto · MIT · Mila", "url": null, "code": "https://github.com/concept-graphs/concept-graphs", "project": "https://concept-graphs.github.io/", "topics": [ "perception", "navigation" ], "tags": [ "scene-graph", "open-vocabulary", "llm" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Builds an object-level 3D scene graph labelled by vision-language models, which an LLM planner can query in natural language.", "takeaway_zh": "用视觉语言模型构建带语义标注的物体级三维场景图，大模型规划器可以用自然语言查询。", "github_stars": 989, "page": "https://embodied-ai-radar.com/e/arxiv-2309.16650.html", "labs": [ "mit", "toronto" ], "code_license": "MIT" }, { "id": "arxiv-2304.02643", "title": "Segment Anything", "type": "paper", "arxiv": "2304.02643", "year": 2023, "venue": "ICCV 2023", "org": "Meta AI", "url": null, "code": "https://github.com/facebookresearch/segment-anything", "project": null, "topics": [ "perception" ], "tags": [ "segmentation", "foundation-model", "promptable" ], "robot_types": [], "highlight": true, "rating": 2, "added": null, "takeaway_en": "SAM — a promptable segmentation model (points, boxes, masks) trained on 1B+ masks; a standard building block for object masks in robot perception pipelines.", "takeaway_zh": "SAM：可用点、框、掩码提示的分割模型，在 10 亿级掩码上训练；已成为机器人感知流程中获取物体掩码的常用基础组件。", "github_stars": 55121, "page": "https://embodied-ai-radar.com/e/arxiv-2304.02643.html", "labs": [ "meta" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2408.00714", "title": "SAM 2: Segment Anything in Images and Videos", "type": "paper", "arxiv": "2408.00714", "year": 2024, "venue": "ICLR 2025", "org": "Meta AI", "url": null, "code": "https://github.com/facebookresearch/sam2", "project": null, "topics": [ "perception" ], "tags": [ "segmentation", "video", "tracking" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Extends SAM to video with a streaming memory, so a mask prompted once is tracked across frames — handy for following objects during manipulation.", "takeaway_zh": "用流式记忆把 SAM 扩展到视频：一次提示得到的掩码可在后续帧中持续跟踪，适合在操作过程中跟随目标物体。", "github_stars": 20133, "page": "https://embodied-ai-radar.com/e/arxiv-2408.00714.html", "labs": [ "meta" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2303.05499", "title": "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection", "type": "paper", "arxiv": "2303.05499", "year": 2023, "venue": "ECCV 2024", "org": "IDEA Research · Tsinghua", "url": null, "code": "https://github.com/IDEA-Research/GroundingDINO", "project": null, "topics": [ "perception" ], "tags": [ "open-vocabulary", "detection", "language-grounding" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Open-set detector that finds objects from a free-text prompt; often paired with SAM (\"Grounded-SAM\") to turn a language instruction into object masks.", "takeaway_zh": "开放集目标检测器，可根据任意文本提示找到物体；常与 SAM 组合（Grounded-SAM），把语言指令变成物体掩码。", "github_stars": 10742, "page": "https://embodied-ai-radar.com/e/arxiv-2303.05499.html", "labs": [ "tsinghua" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2501.09898", "title": "FoundationStereo: Zero-Shot Stereo Matching", "type": "paper", "arxiv": "2501.09898", "year": 2025, "venue": "CVPR 2025", "org": "NVIDIA", "url": null, "code": "https://github.com/NVlabs/FoundationStereo", "project": null, "topics": [ "perception" ], "tags": [ "stereo", "depth", "zero-shot" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Stereo depth model trained on large synthetic data that reports strong zero-shot accuracy on real scenes — a drop-in depth source for stereo-camera robots.", "takeaway_zh": "在大规模合成数据上训练的双目深度模型，报告称在真实场景上零样本精度很强；可直接作为双目相机机器人的深度来源。", "github_stars": 2990, "page": "https://embodied-ai-radar.com/e/arxiv-2501.09898.html", "labs": [ "nvidia" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.25665", "title": "SpaRV: Sparse Radiance Voxels for Scalable Geometrically Consistent LiDAR-Visual Mapping and Rendering", "type": "paper", "arxiv": "2610.25665", "year": 2026, "venue": null, "org": "HKU MaRS Lab", "url": null, "code": "https://github.com/hku-mars/SpaRV", "project": null, "topics": [ "perception" ], "tags": [ "mapping", "lidar", "radiance-field" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-20", "takeaway_en": "A sparse-voxel radiance map built from LiDAR and camera data, aimed at robot logs — few viewpoints, imperfect poses and large scenes — while keeping geometry accurate and GPU memory in check.", "takeaway_zh": "用激光雷达与相机数据构建稀疏体素辐射场地图，针对机器人数据视角少、位姿有误差、场景大的问题，在保证几何精度的同时控制显存占用。", "github_stars": 52, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25665.html", "labs": [ "hku" ], "code_license": "GPL-3.0" }, { "id": "arxiv-2610.25288", "title": "Vision as Proprioception: Estimating Humanoid Robot Pose from Video", "type": "paper", "arxiv": "2610.25288", "year": 2026, "venue": null, "org": "ETH Zurich", "url": null, "code": "https://github.com/eth-siplab/HumanoidPose", "project": null, "topics": [ "perception", "locomotion" ], "tags": [ "humanoid", "pose-estimation", "video" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-20", "takeaway_en": "HumanoidPose recovers a humanoid's full-body joint configuration from video alone — useful when onboard state is unavailable, and for turning online robot videos into training data.", "takeaway_zh": "HumanoidPose 仅凭视频恢复人形机器人的全身关节构型：适用于拿不到机载状态的场景，也能把网上的机器人视频变成训练数据。", "github_stars": 11, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25288.html", "labs": [ "eth" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.27199", "title": "Which Depth Estimator for Jetson AGX Orin? A 2026 Benchmark of Monocular and Stereo Methods", "type": "benchmark", "arxiv": "2610.27199", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/nodarhub/orin-depth-benchmark", "project": null, "topics": [ "perception" ], "tags": [ "depth", "stereo", "edge", "jetson", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-21", "takeaway_en": "Measured numbers for robot builders: nine monocular and stereo depth families (Depth Anything V2/3, Depth Pro, MoGe-2, Fast-FoundationStereo, ...) run on a Jetson AGX Orin, scored for metric accuracy and speed.", "takeaway_zh": "给机器人开发者的实测数据：在 Jetson AGX Orin 上用 TensorRT 运行九类单目与双目深度估计模型（Depth Anything V2/3、Depth Pro、MoGe-2、Fast-FoundationStereo 等），并评测度量精度与速度。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2610.27199.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2304.07193", "title": "DINOv2: Learning Robust Visual Features without Supervision", "type": "paper", "arxiv": "2304.07193", "year": 2023, "venue": "TMLR 2024", "org": "Meta AI", "url": null, "code": "https://github.com/facebookresearch/dinov2", "project": null, "topics": [ "perception", "vla" ], "tags": [ "self-supervised", "vision-backbone", "foundation-model" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Self-supervised image features that work frozen for depth, segmentation and retrieval; a common visual backbone in VLAs (e.g. OpenVLA) and latent world models.", "takeaway_zh": "自监督学习的图像特征，冻结即可用于深度估计、分割和检索；是 VLA（如 OpenVLA）和潜空间世界模型常用的视觉骨干。", "github_stars": 13523, "page": "https://embodied-ai-radar.com/e/arxiv-2304.07193.html", "labs": [ "meta" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2307.07635", "title": "CoTracker: It is Better to Track Together", "type": "paper", "arxiv": "2307.07635", "year": 2023, "venue": "ECCV 2024", "org": "Meta AI · University of Oxford (VGG)", "url": null, "code": "https://github.com/facebookresearch/co-tracker", "project": null, "topics": [ "perception" ], "tags": [ "point-tracking", "video", "transformer" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Transformer that tracks many video points jointly rather than one at a time, staying robust through occlusions; widely used to extract motion tracks from human or robot videos for policy learning.", "takeaway_zh": "联合跟踪视频中大量点（而非逐点独立跟踪）的 Transformer，遮挡下依然稳健；常用于从人类或机器人视频中提取运动轨迹来学习策略。", "github_stars": 5193, "page": "https://embodied-ai-radar.com/e/arxiv-2307.07635.html", "labs": [ "meta", "oxford" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2406.09756", "title": "Grounding Image Matching in 3D with MASt3R", "type": "paper", "arxiv": "2406.09756", "year": 2024, "venue": "ECCV 2024", "org": "NAVER LABS Europe", "url": null, "code": "https://github.com/naver/mast3r", "project": null, "topics": [ "perception" ], "tags": [ "3d-reconstruction", "image-matching", "pointmap" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Extends DUSt3R with a dense local-feature head, so one network gives both 3D pointmaps and accurate pixel matches between views — useful for calibration-free reconstruction and localization.", "takeaway_zh": "在 DUSt3R 基础上加入稠密局部特征头，同一网络既输出三维点图又给出视角间精确的像素匹配，适用于免标定的三维重建与定位。", "github_stars": 3162, "page": "https://embodied-ai-radar.com/e/arxiv-2406.09756.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2408.14035", "title": "FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry", "type": "paper", "arxiv": "2408.14035", "year": 2024, "venue": null, "org": "HKU MaRS Lab", "url": null, "code": "https://github.com/hku-mars/FAST-LIVO2", "project": null, "topics": [ "perception" ], "tags": [ "lidar", "visual", "odometry", "sensor-fusion", "real-time" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "FAST-LIVO2 — fuses LiDAR, camera and IMU in one filter with direct (feature-free) updates for both sensors, sharing a single voxel map where image patches are attached to LiDAR points.", "takeaway_zh": "FAST-LIVO2：在同一滤波器中融合激光雷达、相机与 IMU，两种传感器都采用直接法（无需提取特征）更新，并共用一张体素地图，图像块挂载在激光点上。", "github_stars": 4864, "page": "https://embodied-ai-radar.com/e/arxiv-2408.14035.html", "labs": [ "hku" ], "code_license": "GPL-2.0" }, { "id": "arxiv-2406.09414", "title": "Depth Anything V2", "type": "paper", "arxiv": "2406.09414", "year": 2024, "venue": "NeurIPS 2024", "org": "HKU, TikTok", "url": null, "code": "https://github.com/DepthAnything/Depth-Anything-V2", "project": null, "topics": [ "perception" ], "tags": [ "monocular-depth", "foundation-model", "synthetic-data" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Trains a large teacher only on synthetic images with precise depth, then pseudo-labels many real images to train smaller students; gives sharper, more robust monocular depth than V1.", "takeaway_zh": "先只用深度精确的合成图像训练大教师模型，再给大量真实图像打伪标签来训练小模型；单目深度比 V1 更锐利、更鲁棒。", "github_stars": 9131, "page": "https://embodied-ai-radar.com/e/arxiv-2406.09414.html", "labs": [ "hku" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2303.14158", "title": "BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects", "type": "paper", "arxiv": "2303.14158", "year": 2023, "venue": "CVPR 2023", "org": "NVIDIA", "url": null, "code": "https://github.com/NVlabs/BundleSDF", "project": null, "topics": [ "perception", "manipulation" ], "tags": [ "real", "6d-pose", "tracking", "reconstruction", "rgbd", "model-free" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Tracks the 6-DoF pose of a never-seen object in RGB-D video while building its neural 3D model online, with no CAD model needed; useful for manipulating novel objects.", "takeaway_zh": "在 RGB-D 视频中跟踪从未见过物体的六自由度位姿，同时在线构建其神经三维模型，无需 CAD 模型；适用于操作新物体。", "github_stars": 1428, "page": "https://embodied-ai-radar.com/e/arxiv-2303.14158.html", "labs": [ "nvidia" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.27040", "title": "ACE-Brain-0.6: Scaling Spatial Grounding for Physical Foundation Models", "type": "paper", "arxiv": "2610.27040", "year": 2026, "venue": null, "org": "ACE Robotics", "url": null, "code": "https://github.com/DAXIAORobotics/ACE-Brain-0.6", "project": "https://daxiaorobotics.github.io/ACE-Brain-0.6/", "topics": [ "perception", "vla", "manipulation" ], "tags": [ "visual-grounding", "vlm", "embodied-brain", "data-pipeline" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-24", "takeaway_en": "Fine-tunes Qwen3.5-9B on ~1.5M grounding samples auto-built from robot recordings plus other visual data; reports a 75.41 average over seven grounding benchmarks vs 73.68 for GPT-6 Astra.", "takeaway_zh": "用自动标注的机器人操作录像加多来源视觉数据构建约 150 万条可验证的定位监督，微调 Qwen3.5-9B；报告在七个公开视觉定位基准上平均 75.41 分，GPT-6 Astra 为 73.68。", "github_stars": 9, "page": "https://embodied-ai-radar.com/e/arxiv-2610.27040.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2511.16719", "title": "SAM 3: Segment Anything with Concepts", "type": "paper", "arxiv": "2511.16719", "year": 2025, "venue": null, "org": "Meta", "url": null, "code": "https://github.com/facebookresearch/sam3", "project": null, "topics": [ "perception" ], "tags": [ "segmentation", "tracking", "open-vocabulary", "foundation-model", "open-weights" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Segments and tracks every instance of a concept given a short noun phrase or image exemplar, in images and video; trained with a data engine covering 4M concept labels, with open weights and the SA-Co benchmark.", "takeaway_zh": "给定短名词短语或示例图像，即可在图像和视频中分割并跟踪某一概念的所有实例；数据引擎覆盖 400 万个概念标签，开放权重与 SA-Co 基准，可直接用作机器人开放词汇感知模块。", "github_stars": 12262, "page": "https://embodied-ai-radar.com/e/arxiv-2511.16719.html", "labs": [ "meta" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.41671", "title": "OpenVIO: A Versatile Open-Source Visual-Inertial Odometry System", "type": "paper", "arxiv": "2610.41671", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/93won/openvio", "project": null, "topics": [ "perception" ], "tags": [ "vio", "slam", "fisheye", "360-camera", "open-source" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "MIT-licensed visual-inertial odometry for research and products. Supports pinhole and fisheye cameras plus stitched 360° images, with benchmarks against other open-source VIO systems on public datasets.", "takeaway_zh": "MIT 许可的开源视觉惯性里程计，面向科研与实际应用。支持针孔、鱼眼相机及拼接 360° 图像，并在公开数据集上与其他开源 VIO 系统做了对比。", "github_stars": 395, "page": "https://embodied-ai-radar.com/e/arxiv-2610.41671.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.05035", "title": "Anchor3R: Streaming 3D Reconstruction with Transient Anchors for Long-Horizon Visual Mapping", "type": "paper", "arxiv": "2606.05035", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/polar-explorer/Anchor3R", "project": null, "topics": [ "perception" ], "tags": [ "3d-reconstruction", "streaming", "slam" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Current-centric streaming 3D reconstruction that predicts window-relative poses and local geometry in the current frame, to limit drift on long runs.", "takeaway_zh": "CoRL 2026 Spotlight。以当前帧为中心的流式三维重建：在当前帧坐标系预测窗口相对位姿与局部几何，以抑制长时漂移。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2606.05035.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2611.02884", "title": "AnchorHEC: Targetless Hand-Eye Calibration of Wrist and Fixed Cameras", "type": "paper", "arxiv": "2611.02884", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/HwangGB/AnchorHEC", "project": "https://hwanggb.github.io/AnchorHEC/", "topics": [ "perception" ], "tags": [ "calibration", "hand-eye", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Calibrates wrist and fixed cameras together with no target, from RGB, kinematics and a 3D foundation model; reports 86.4% lower translation and 71.6% lower rotation error in simulation than reconstruction baselines.", "takeaway_zh": "无需标定板，用 RGB、正运动学和三维基础模型同时标定腕部与固定相机；仿真中平移和旋转误差比基于重建的基线分别降低 86.4% 和 71.6%，并在真机上验证。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2611.02884.html", "labs": [], "code_license": "" }, { "id": "arxiv-1910.02490", "title": "Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping", "type": "paper", "arxiv": "1910.02490", "year": 2019, "venue": "ICRA 2020", "org": "MIT", "url": null, "code": "https://github.com/MIT-SPARK/Kimera", "project": null, "topics": [ "perception", "navigation" ], "tags": [ "slam", "semantic", "open-source", "visual-inertial" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "Open C++ library for real-time metric-semantic visual-inertial SLAM: VIO, robust pose graph, fast 3D mesher and dense semantic reconstruction as modular parts.", "takeaway_zh": "开源 C++ 实时度量-语义视觉惯性 SLAM 库，模块化包含 VIO、鲁棒位姿图、快速三维网格与稠密语义重建。", "github_stars": 2147, "page": "https://embodied-ai-radar.com/e/arxiv-1910.02490.html", "labs": [ "mit" ], "code_license": "BSD-2-Clause" }, { "id": "arxiv-2606.09355", "title": "MosaicIMU: Composing Carrier Experts for Generalizable Neural Inertial Odometry", "type": "paper", "arxiv": "2606.09355", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "perception", "navigation" ], "tags": [ "inertial-odometry", "mixture-of-experts" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Neural inertial odometry across carriers: a router composes carrier-specific experts, and a light residual branch adapts to new ones. Reports 40% lower ATE than learned baselines.", "takeaway_zh": "面向多种载体的神经惯性里程计：路由器组合各载体专家，新载体通过轻量残差分支适配。报告 ATE 比学习型基线降低 40%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.09355.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.16356", "title": "Articulated 3D Scene Graphs for Open-World Mobile Manipulation", "type": "paper", "arxiv": "2602.16356", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/robot-learning-freiburg/MoMa-SG", "project": "https://momasg.cs.uni-freiburg.de", "topics": [ "perception", "manipulation" ], "tags": [ "scene-graph", "articulation", "mobile-manipulation" ], "robot_types": [ "arm", "legged", "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "MoMa-SG builds 3D scene graphs that encode how objects articulate, from RGB-D sequences, and adds the Arti4D-Semantic dataset; demonstrated on a quadruped and a mobile manipulator.", "takeaway_zh": "MoMa-SG 从 RGB-D 序列构建包含物体铰接方式的 3D 场景图，并发布 Arti4D-Semantic 数据集；在四足和移动操作机器人上演示。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2602.16356.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.03932", "title": "CoReMatch: Recurrent Continuous Coordinate Refinement for Real-Time Robotic Feature Matching", "type": "paper", "arxiv": "2611.03932", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "perception" ], "tags": [ "feature-matching", "real-time", "edge" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Feature matcher that refines correspondences recurrently in continuous coordinates instead of on fixed grids; reports higher accuracy than Efficient LoFTR at over 10x its speed on embedded hardware.", "takeaway_zh": "在连续坐标中循环细化对应点、不再依赖固定网格的特征匹配方法；报告精度高于 Efficient LoFTR，在嵌入式硬件上速度超过其 10 倍。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03932.html", "labs": [], "code_license": null }, { "id": "arxiv-2210.05663", "title": "CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory", "type": "paper", "arxiv": "2210.05663", "year": 2022, "venue": null, "org": null, "url": null, "code": "https://github.com/notmahi/clip-fields", "project": "https://mahis.life/clip-fields", "topics": [ "perception" ], "tags": [ "semantic-field", "clip", "memory" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "CLIP-Fields learns a mapping from 3D points to semantic embeddings using only weak supervision, giving robots a searchable spatial memory.", "takeaway_zh": "CLIP-Fields 仅用弱监督学习三维点到语义嵌入的映射，为机器人提供可检索的空间记忆。", "github_stars": 193, "page": "https://embodied-ai-radar.com/e/arxiv-2210.05663.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2406.13642", "title": "SpatialBot: Precise Spatial Understanding with Vision Language Models", "type": "paper", "arxiv": "2406.13642", "year": 2024, "venue": null, "org": null, "url": null, "code": "https://github.com/BAAI-DCAI/SpatialBot", "project": null, "topics": [ "perception" ], "tags": [ "vlm", "depth", "spatial-understanding" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "SpatialBot feeds RGB and depth into a VLM for better spatial understanding, with the SpatialQA dataset.", "takeaway_zh": "SpatialBot 向 VLM 同时输入 RGB 和深度以提升空间理解，并提供 SpatialQA 数据集。", "github_stars": 358, "page": "https://embodied-ai-radar.com/e/arxiv-2406.13642.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2506.04308", "title": "RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics", "type": "paper", "arxiv": "2506.04308", "year": 2025, "venue": "NeurIPS 2025", "org": null, "url": null, "code": "https://github.com/Zhoues/RoboRefer", "project": "https://zhoues.github.io/RoboRefer/", "topics": [ "perception" ], "tags": [ "vlm", "spatial-reasoning", "3d" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "RoboRefer trains a VLM for spatial referring: reasoning about which 3D location an instruction points to.", "takeaway_zh": "RoboRefer 训练 VLM 做空间指代：推理指令所指的三维位置。", "github_stars": 270, "page": "https://embodied-ai-radar.com/e/arxiv-2506.04308.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2512.13660", "title": "Towards Spatial Trace with Reasoning in Vision-Language Models for Robotics", "type": "paper", "arxiv": "2512.13660", "year": 2025, "venue": "ECCV 2026", "org": null, "url": null, "code": "https://github.com/Zhoues/RoboTracer", "project": "https://zhoues.github.io/RoboTracer", "topics": [ "perception" ], "tags": [ "vlm", "spatial-reasoning", "metric-3d" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "RoboTracer is a 3D-aware VLM for spatial tracing, which needs multi-step metric reasoning about real-world measurements.", "takeaway_zh": "RoboTracer 是三维感知 VLM，用于空间轨迹推理，需要结合真实度量做多步推理。", "github_stars": 98, "page": "https://embodied-ai-radar.com/e/arxiv-2512.13660.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2609.07497", "title": "Functional-SLAM: Interaction-Aware Mapping with Online Functional Scene Graphs", "type": "paper", "arxiv": "2609.07497", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Hbelief1998/Functional-SLAM-CoRL_2026", "project": null, "topics": [ "perception" ], "tags": [ "slam", "scene-graph", "functional-relations", "online", "mapping" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "SLAM that keeps a functional scene graph (which handle opens which drawer) as online state, and uses that topology to help loop closure in repetitive scenes. Reported faster than offline methods at similar accuracy.", "takeaway_zh": "把功能场景图（如哪个把手开哪个抽屉）作为 SLAM 的在线状态持续维护，并利用其拓扑辅助重复场景的回环检测。报告比离线方法更快且精度相当。", "github_stars": 57, "page": "https://embodied-ai-radar.com/e/arxiv-2609.07497.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2608.16264", "title": "Cyclops: LiDAR as a Camera That Dreams in Color", "type": "paper", "arxiv": "2608.16264", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/IMRL/Cyclops", "project": null, "topics": [ "perception" ], "tags": [ "lidar", "image-translation", "low-light", "bridge-matching" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Turns sparse LiDAR intensity into RGB-like video so camera models work in the dark. Reports better segmentation and lane detection than LiDAR baselines and real cameras across lighting.", "takeaway_zh": "把稀疏 LiDAR 强度转换为类 RGB 视频，让相机模型在黑暗中也能用。报告在不同光照下，分割与车道线检测优于 LiDAR 基线和真实相机。", "github_stars": 13, "page": "https://embodied-ai-radar.com/e/arxiv-2608.16264.html", "labs": [], "code_license": "" }, { "id": "arxiv-2606.19088", "title": "ReSiReg: Towards Spatially Consistent Semantics in Language-Conditioned Robotic Tasks", "type": "paper", "arxiv": "2606.19088", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/SimonSchwaiger/resireg", "project": "https://resireg.github.io", "topics": [ "perception", "manipulation" ], "tags": [ "vlm-features", "open-vocabulary", "dense-features", "3d-mapping" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Makes dense VLM features spatially consistent by rebuilding each patch from language-labelled visual prototypes; improves open-vocabulary retrieval and 3D mapping. Also ships a compact 25M dense VLM for robots.", "takeaway_zh": "通过由带语言描述的视觉原型重建每个图像块，让稠密 VLM 特征在空间上更一致，提升开放词汇检索与三维建图；并提供 25M 参数的小型稠密 VLM。", "github_stars": 10, "page": "https://embodied-ai-radar.com/e/arxiv-2606.19088.html", "labs": [], "code_license": "GPL-3.0" }, { "id": "arxiv-2610.05011", "title": "FreeLoc: Online Floorplan Localization via Diffusion-Aided Pose Refinement", "type": "paper", "arxiv": "2610.05011", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://zju3dv.github.io/freeloc/", "topics": [ "perception" ], "tags": [ "localization", "floorplan", "diffusion", "indoor" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Localizes an RGB camera on a floorplan online, querying the map directly and refining poses with diffusion instead of building an offline database. Reports SOTA single-frame and sequential results in real time.", "takeaway_zh": "直接在平面图上在线定位 RGB 相机：实时查询地图并用扩散模型精化位姿，无需离线数据库。报告单帧与序列定位均达最优且实时运行。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.05011.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.05533", "title": "What Objects Enable, Not What They Are: Functional Latent Spaces for Affordance Reasoning", "type": "paper", "arxiv": "2606.05533", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://A4Dance-reasoning.github.io", "topics": [ "perception" ], "tags": [ "affordance", "latent-space", "planning", "uncertainty", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Maps observations into a latent space organised by affordances (e.g. 'movable') rather than appearance, and adds new affordances when uncertain. Reports 94% inference accuracy, 15+ points above prior work.", "takeaway_zh": "把视觉观测映射到按功能（如“可移动”）而非外观组织的潜在空间，并在不确定时自动发现新的可供性。报告推断准确率 94%，比已有方法高 15 个百分点以上。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.05533.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.29881", "title": "OptiGeo: Efficient Monocular Geometry for Embodied Perception in Optically Challenging Scenes", "type": "paper", "arxiv": "2608.29881", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/mx-liu6/OptiGeo", "project": "https://mx-liu6.github.io/OptiGeo-web/", "topics": [ "perception" ], "tags": [ "depth-estimation", "transparent-objects", "monocular", "efficient", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "30M-parameter monocular depth model for transparent and reflective scenes, trained to undo depth-sensor bias with a small rendered set. Reports beating 300M-scale and multi-view models on transparent benchmarks.", "takeaway_zh": "面向透明与反光场景的 3000 万参数单目深度模型，用少量渲染数据纠正真实深度传感器带来的监督偏差。报告在透明场景基准上超过 3 亿参数级和多视图模型。", "github_stars": 95, "page": "https://embodied-ai-radar.com/e/arxiv-2608.29881.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2606.08666", "title": "Language as a Sensor: Calibrated Spatial Belief Estimation in 3D Scenes from Natural Language", "type": "paper", "arxiv": "2606.08666", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/MIT-SPARK/Language-As-A-Sensor", "project": "https://aryan-naveen.github.io/language-as-a-sensor/", "topics": [ "perception" ], "tags": [ "language-grounding", "semantic-mapping", "uncertainty", "belief", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Treats spoken descriptions ('my bag is on the table') as sensor readings: a model outputs a calibrated spatial distribution that is fused with onboard perception in one belief map. VLA-3D benchmark + real robot.", "takeaway_zh": "把自然语言描述（如“包在桌上”）当作传感器观测：模型输出校准过的空间概率分布，并与机载感知融合到统一的信念地图中。在 VLA-3D 基准和真机上验证。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2606.08666.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2609.08493", "title": "AURORA: Active Uncertainty-Driven Re-Orientation for In-Hand Reconstruction", "type": "paper", "arxiv": "2609.08493", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://aurorahand.github.io/", "topics": [ "perception" ], "tags": [ "active-perception", "3d-reconstruction", "in-hand", "next-best-view", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Active in-hand 3D reconstruction: estimates per-ray reconstruction uncertainty to pick the next view, then an in-hand rotation policy turns the object to expose it. Better quality and efficiency than non-active rotation.", "takeaway_zh": "主动式手内三维重建：沿候选视线估计各方向的重建不确定性来选择下一最佳视角，再由手内旋转策略把物体转到该视角。重建质量与效率均优于非主动旋转。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.08493.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.07711", "title": "Fast and Accurate Hand Estimation in Pixels, Interaction and World Space", "type": "paper", "arxiv": "2611.07711", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/noahcao/FAH", "project": null, "topics": [ "perception" ], "tags": [ "hand-pose", "mano", "contact", "egocentric", "real-time" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "FAH builds on SAM 3D Body's hand branch: predict 2D/3D joints and surface points, then fit MANO or MHR-Hand, with optional contact and world-space tracking. The default ViT-L runs at 65 fps on an RTX 4070 Ti SUPER.", "takeaway_zh": "FAH 基于 SAM 3D Body 的手部分支：先预测 2D/3D 关节与表面点，再拟合 MANO 或 MHR-Hand，可选估计接触并在世界坐标系中跟踪。默认 ViT-L 模型在 RTX 4070 Ti SUPER 上达 65 fps。", "github_stars": 72, "page": "https://embodied-ai-radar.com/e/arxiv-2611.07711.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.08284", "title": "G2G: Exploiting Intra-Group Geometry for Inter-Group Pose Estimation", "type": "paper", "arxiv": "2606.08284", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/WeiYuFei0217/G2G", "project": null, "topics": [ "perception" ], "tags": [ "pose-estimation", "relocalization", "multi-camera", "foundation-model" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Relative 6-DoF pose between two image groups (relocalization, multi-camera rigs): keeps a multi-view foundation model frozen and adds ~32M trainable parameters (resampler, cross-group bridge, pose head).", "takeaway_zh": "估计两组图像之间的相对 6 自由度位姿（跨序列重定位、多相机里程计）：保持多视图基础模型冻结，只增加约 3200 万可训练参数（重采样器、跨组桥接模块、多帧位姿头）。", "github_stars": 24, "page": "https://embodied-ai-radar.com/e/arxiv-2606.08284.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2607.08735", "title": "Learning Adaptive Solvers for Distributed Factor Graph Optimization on Matrix Lie Groups", "type": "paper", "arxiv": "2607.08735", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "perception" ], "tags": [ "slam", "distributed-optimization", "multi-robot", "lie-groups" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "DeepCORD unrolls a parallel accelerated Riemannian optimizer and learns a self-supervised policy that tunes solver parameters by phase and communication status. Tested on SE(3) pose graphs and SL(4).", "takeaway_zh": "DeepCORD 把并行加速的黎曼优化器展开为可微迭代，并自监督学习一个依据优化阶段与通信状态调节求解参数的策略。在真实 SE(3) 位姿图与 SL(4) 子图对齐上测试。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.08735.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.25338", "title": "GINIO: A Geometric SO(3)-Equivariant Interface for Neural Inertial Odometry", "type": "paper", "arxiv": "2609.25338", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "perception" ], "tags": [ "inertial-odometry", "imu", "equivariance", "drone" ], "robot_types": [ "aerial" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Neural inertial odometry whose mean and covariance transform correctly under any IMU mounting rotation; Last-Frame Alignment makes sensor-frame training match world-frame. TLIO ATE: 2.018 m vs 76.389 m for EqNIO.", "takeaway_zh": "神经惯性里程计：输出的均值与协方差在任意 IMU 安装旋转下都按张量规律变换；Last-Frame Alignment 使传感器坐标系训练等价于世界坐标系训练。在 TLIO 上 ATE 为 2.018 米，EqNIO 为 76.389 米。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.25338.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.02417", "title": "LIME: Learning Intent-aware Camera Motion from Egocentric Video", "type": "paper", "arxiv": "2607.02417", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "perception" ], "tags": [ "active-perception", "camera-motion", "egocentric-video", "flow-matching" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Treats camera motion as an action: given an image and a language intent ('look behind the box'), predict the next relative camera pose. Supervision is mined from egocentric video by hindsight labeling.", "takeaway_zh": "把相机运动当作一种动作：给定当前图像和语言意图（如“看看箱子后面”），预测下一视角的相对相机位姿。监督信号通过事后标注从第一人称视频中挖掘。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.02417.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.25358", "title": "LateMatch: Breaking the Interaction Bottleneck via Cacheable Matching for Efficient Visual Localization", "type": "paper", "arxiv": "2610.25358", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "perception" ], "tags": [ "feature-matching", "localization", "edge", "efficiency" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Restructures deep feature matchers so cross-image interaction happens only in the last layers; per-image context blocks are cached offline. Reports over 2x lower compute, plus 10-14% from entropy-guided token reduction.", "takeaway_zh": "重构深度特征匹配器，把跨图像交互推迟到最后几层，单图上下文模块可离线缓存。报告计算量降低一半以上，熵引导的 token 裁剪再省 10–14%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25358.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.24605", "title": "RoMo-GS: Efficient Online 3D Gaussian Splatting for Robot Mapping via Exposure-Integrated Motion Blur", "type": "paper", "arxiv": "2610.24605", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "perception" ], "tags": [ "3dgs", "mapping", "motion-blur", "online" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Online 3DGS mapping that integrates each Gaussian over the exposure time analytically to handle motion blur, and prunes by a Hessian score. Matches offline deblurring 3DGS at 15-150x the speed.", "takeaway_zh": "在线 3DGS 建图：对每个高斯在曝光时间内解析积分以处理运动模糊，并用 Hessian 得分剪枝控制数量。渲染质量与离线去模糊 3DGS 相当，速度快 15–150 倍。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.24605.html", "labs": [], "code_license": null }, { "id": "arxiv-2605.01365", "title": "VoxAfford: Multi-Scale Voxel-Token Fusion for Open-Vocabulary 3D Affordance Detection", "type": "paper", "arxiv": "2605.01365", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "perception" ], "tags": [ "affordance", "point-cloud", "mllm", "open-vocabulary" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Open-vocabulary 3D affordance detection: MLLM output tokens pull multi-scale geometry from a frozen 3D VQ-VAE through gated cross-attention before mask decoding. Reports about +8% mIoU.", "takeaway_zh": "开放词汇 3D 可供性检测：多模态大模型的输出 token 在解码掩码前，通过门控交叉注意力从冻结的 3D VQ-VAE 中检索多尺度几何特征。报告 mIoU 提升约 8%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2605.01365.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.16569", "title": "PROSE: Training-Free Egocentric Scene Registration with Vision-Language Models", "type": "paper", "arxiv": "2606.16569", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "perception" ], "tags": [ "scene-graph", "registration", "egocentric", "vlm", "training-free" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Aligns two RGB-only egocentric captures of a room taken at different times: builds object-level 3D scene graphs with foundation models and lets a VLM match objects across them. No training or depth sensor.", "takeaway_zh": "对齐同一房间在不同时间的两段纯 RGB 第一人称录像：用基础模型构建物体级 3D 场景图，再让 VLM 跨两段录像匹配物体实例。无需训练或深度传感器。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.16569.html", "labs": [], "code_license": null }, { "id": "arxiv-2310.17274", "title": "cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation", "type": "paper", "arxiv": "2310.17274", "year": 2023, "venue": null, "org": "NVIDIA", "url": null, "code": "https://github.com/NVlabs/curobo", "project": null, "topics": [ "control", "manipulation" ], "tags": [ "motion-planning", "gpu", "trajectory-optimization" ], "robot_types": [], "highlight": true, "rating": 3, "added": null, "takeaway_en": "GPU-parallel motion generation for arms — many trajectory optimizations run at once, giving collision-free, smooth plans in milliseconds.", "takeaway_zh": "面向机械臂的 GPU 并行运动生成：同时运行大量轨迹优化，在毫秒级给出无碰撞且平滑的轨迹。", "github_stars": 1992, "page": "https://embodied-ai-radar.com/e/arxiv-2310.17274.html", "labs": [ "nvidia" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2210.12209", "title": "Motion Policy Networks", "type": "paper", "arxiv": "2210.12209", "year": 2022, "venue": null, "org": "NVIDIA · University of Washington", "url": null, "code": "https://github.com/NVlabs/motion-policy-networks", "project": null, "topics": [ "control", "manipulation" ], "tags": [ "motion-planning", "point-cloud", "imitation" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A neural policy trained on millions of planner-generated trajectories that moves an arm to goals straight from point clouds.", "takeaway_zh": "在数百万条规划器生成的轨迹上训练的神经策略，可直接根据点云把机械臂移动到目标位置。", "github_stars": 190, "page": "https://embodied-ai-radar.com/e/arxiv-2210.12209.html", "labs": [ "nvidia", "uw" ], "code_license": "MIT" }, { "id": "arxiv-2205.09991", "title": "Planning with Diffusion for Flexible Behavior Synthesis", "type": "paper", "arxiv": "2205.09991", "year": 2022, "venue": null, "org": "UC Berkeley", "url": null, "code": "https://github.com/jannerm/diffuser", "project": null, "topics": [ "control", "world-models" ], "tags": [ "diffusion", "planning", "offline-rl" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Diffuser — treats planning as denoising whole trajectories, so goals and rewards can steer sampling; an early link between diffusion and control.", "takeaway_zh": "Diffuser：把规划视为对整条轨迹去噪，目标与奖励可以引导采样；扩散模型与控制结合的早期代表作。", "github_stars": 1317, "page": "https://embodied-ai-radar.com/e/arxiv-2205.09991.html", "labs": [ "berkeley" ], "code_license": "MIT" }, { "id": "arxiv-1903.11199", "title": "Control Barrier Functions: Theory and Applications", "type": "paper", "arxiv": "1903.11199", "year": 2019, "venue": null, "org": "Caltech", "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "safety", "control-barrier-function", "tutorial" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Tutorial on control barrier functions — the standard way to wrap any controller in a minimal-change filter that keeps the system safe.", "takeaway_zh": "控制障碍函数教程：为任意控制器加一层“尽量少改动”的安全滤波器、保证系统安全的标准方法。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1903.11199.html", "labs": [], "code_license": null }, { "id": "arxiv-2108.06266", "title": "Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning", "type": "paper", "arxiv": "2108.06266", "year": 2021, "venue": null, "org": "University of Toronto", "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "safety", "survey", "safe-rl" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Survey connecting control-theory and RL views of safety, from learned dynamics with guarantees to constrained RL and safety filters.", "takeaway_zh": "综述，连接控制理论与强化学习两种安全视角：从带保证的学习动力学到约束强化学习与安全滤波器。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2108.06266.html", "labs": [ "toronto" ], "code_license": null }, { "id": "arxiv-2109.06325", "title": "safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics", "type": "benchmark", "arxiv": "2109.06325", "year": 2021, "venue": null, "org": "University of Toronto", "url": null, "code": "https://github.com/utiasDSL/safe-control-gym", "project": null, "topics": [ "control", "datasets" ], "tags": [ "safety", "benchmark", "safe-rl" ], "robot_types": [ "aerial" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Gym-style benchmark with cart-pole and quadrotor tasks, constraints and disturbances for comparing safe control and safe RL methods.", "takeaway_zh": "Gym 风格基准，含倒立摆与四旋翼任务、约束与扰动，用于比较安全控制与安全强化学习方法。", "github_stars": 934, "page": "https://embodied-ai-radar.com/e/arxiv-2109.06325.html", "labs": [ "toronto" ], "code_license": "MIT" }, { "id": "arxiv-1909.04947", "title": "Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control", "type": "paper", "arxiv": "1909.04947", "year": 2019, "venue": null, "org": "LAAS-CNRS, University of Edinburgh", "url": null, "code": "https://github.com/loco-3d/crocoddyl", "project": null, "topics": [ "control", "locomotion" ], "tags": [ "optimal-control", "ddp", "legged", "open-source" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "An open-source DDP-based solver for contact-rich trajectory optimization, widely used for legged and humanoid motion generation and MPC.", "takeaway_zh": "基于 DDP 的开源接触丰富轨迹优化求解器，广泛用于足式与人形机器人的运动生成和 MPC。", "github_stars": 1329, "page": "https://embodied-ai-radar.com/e/arxiv-1909.04947.html", "labs": [], "code_license": "BSD-3-Clause" }, { "id": "arxiv-1910.13753", "title": "acados: a modular open-source framework for fast embedded optimal control", "type": "paper", "arxiv": "1910.13753", "year": 2019, "venue": null, "org": "University of Freiburg", "url": null, "code": "https://github.com/acados/acados", "project": null, "topics": [ "control" ], "tags": [ "mpc", "optimal-control", "embedded", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Fast nonlinear MPC and estimation solvers with Python/MATLAB interfaces that generate C code for embedded targets.", "takeaway_zh": "高速非线性 MPC 与状态估计求解器，提供 Python/MATLAB 接口并可为嵌入式平台生成 C 代码。", "github_stars": 1518, "page": "https://embodied-ai-radar.com/e/arxiv-1910.13753.html", "labs": [ "freiburg" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.21913", "title": "Sub-Millisecond Reflexes for Continuous Physical Human-Robot Interaction", "type": "paper", "arxiv": "2610.21913", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/RoboRef/franka_reflex_engine", "project": null, "topics": [ "control", "manipulation" ], "tags": [ "real", "arm", "phri", "ros2", "safety" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-10-19", "takeaway_en": "An open-source ROS 2 multimode controller that switches control modes and triggers safety reflexes in under a millisecond, without stopping the task; shown on a Franka arm.", "takeaway_zh": "开源的 ROS 2 多模式控制器，可在亚毫秒内切换控制模式并触发安全反射而无需中断任务；在 Franka 机械臂上验证。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2610.21913.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.23884", "title": "PrismPlan: A Shared GPU Substrate for Motion Planning Across Paradigms", "type": "paper", "arxiv": "2610.23884", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/XiaoyubotMotionTeam/PrismPlan", "project": null, "topics": [ "control" ], "tags": [ "motion-planning", "gpu", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-20", "takeaway_en": "Builds batched GPU collision checking and forward kinematics once and runs sampling, search and optimization planners on top, so the three paradigms can be compared fairly.", "takeaway_zh": "一次性实现批量 GPU 碰撞检测与正运动学，在其上运行采样、搜索和优化三类规划器，使三种范式可以公平比较。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2610.23884.html", "labs": [], "code_license": "" }, { "id": "arxiv-2212.00541", "title": "Predictive Sampling: Real-time Behaviour Synthesis with MuJoCo", "type": "paper", "arxiv": "2212.00541", "year": 2022, "venue": null, "org": "Google DeepMind", "url": null, "code": "https://github.com/google-deepmind/mujoco_mpc", "project": null, "topics": [ "control" ], "tags": [ "sim", "mpc", "sampling-based", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Introduces MuJoCo MPC (MJPC), an interactive tool for real-time predictive control in MuJoCo, and shows that a simple sampling-based planner is a strong baseline for contact-rich behaviours.", "takeaway_zh": "推出 MuJoCo MPC（MJPC），可在 MuJoCo 中交互式地做实时预测控制，并表明简单的基于采样的规划器在接触丰富的行为上是很强的基线。", "github_stars": 1761, "page": "https://embodied-ai-radar.com/e/arxiv-2212.00541.html", "labs": [ "google-deepmind" ], "code_license": "Apache-2.0" }, { "id": "pinocchio", "title": "Pinocchio", "type": "code", "arxiv": null, "year": 2019, "venue": null, "org": "LAAS-CNRS, INRIA", "url": null, "code": "https://github.com/stack-of-tasks/pinocchio", "project": null, "topics": [ "control" ], "tags": [ "library", "dynamics", "kinematics", "open-source" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Fast C++/Python rigid-body dynamics library with analytical derivatives; a common backbone for kinematics, optimal control (e.g. Crocoddyl) and legged/humanoid control stacks.", "takeaway_zh": "高性能 C++/Python 刚体动力学库，提供解析导数；是运动学、最优控制（如 Crocoddyl）以及足式/人形机器人控制栈的常用底座。", "github_stars": 3856, "page": "https://embodied-ai-radar.com/e/pinocchio.html", "labs": [], "code_license": "BSD-2-Clause" }, { "id": "arxiv-2610.25476", "title": "CusADi2: Latency-Optimized Batch Computation of Robot Kinematics, Jacobians, and Hessians on the GPU", "type": "paper", "arxiv": "2610.25476", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/johnviljoen/cusadi2", "project": null, "topics": [ "control" ], "tags": [ "gpu", "kinematics", "trajectory-optimization" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-20", "takeaway_en": "Evaluates batches of kinematics, Jacobians and Hessians on the GPU with low wall-clock latency, targeting online sampling- and gradient-based trajectory optimization.", "takeaway_zh": "在 GPU 上以低延迟批量计算运动学、雅可比与海森矩阵，面向在线运行的采样式与梯度式轨迹优化。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25476.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-1711.08013", "title": "OSQP: An Operator Splitting Solver for Quadratic Programs", "type": "paper", "arxiv": "1711.08013", "year": 2020, "venue": null, "org": "University of Oxford, ETH Zurich, IMT Lucca, Stanford", "url": null, "code": "https://github.com/osqp/osqp", "project": null, "topics": [ "control" ], "tags": [ "optimization", "qp-solver", "mpc", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "A fast, robust ADMM-based QP solver with warm starting and embedded C code generation; widely used inside MPC and whole-body controllers.", "takeaway_zh": "基于 ADMM 的快速、鲁棒二次规划求解器，支持热启动与嵌入式 C 代码生成，广泛用于 MPC 与全身控制器。", "github_stars": 2226, "page": "https://embodied-ai-radar.com/e/arxiv-1711.08013.html", "labs": [ "stanford", "eth", "oxford" ], "code_license": "Apache-2.0" }, { "id": "ompl-the-open-motion-planning-library", "title": "OMPL: The Open Motion Planning Library", "type": "code", "arxiv": null, "year": 2012, "venue": null, "org": "Rice University (Kavraki Lab)", "url": "https://ompl.kavrakilab.org/", "code": "https://github.com/ompl/ompl", "project": null, "topics": [ "control" ], "tags": [ "library", "motion-planning", "sampling-based", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "The standard library of sampling-based motion planners (RRT, RRT-Connect, PRM, RRT* and many more); the default planning backend of MoveIt.", "takeaway_zh": "采样式运动规划器的标准库（RRT、RRT-Connect、PRM、RRT* 等众多算法），也是 MoveIt 默认的规划后端。", "github_stars": 2197, "page": "https://embodied-ai-radar.com/e/ompl-the-open-motion-planning-library.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "moveit-2", "title": "MoveIt 2", "type": "code", "arxiv": null, "year": 2019, "venue": null, "org": "PickNik Robotics and community", "url": "https://moveit.ai/", "code": "https://github.com/moveit/moveit2", "project": null, "topics": [ "control", "manipulation" ], "tags": [ "ros2", "motion-planning", "manipulation", "open-source" ], "robot_types": [ "arm" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "The ROS 2 manipulation framework - motion planning, inverse kinematics, collision checking and real-time servoing for robot arms.", "takeaway_zh": "ROS 2 的机械臂操作框架：提供运动规划、逆运动学、碰撞检测与实时伺服控制。", "github_stars": 2106, "page": "https://embodied-ai-radar.com/e/moveit-2.html", "labs": [], "code_license": "BSD-3-Clause" }, { "id": "drake", "title": "Drake", "type": "code", "arxiv": null, "year": 2014, "venue": null, "org": "MIT, Toyota Research Institute", "url": "https://drake.mit.edu/", "code": "https://github.com/RobotLocomotion/drake", "project": null, "topics": [ "control", "sim" ], "tags": [ "library", "optimization", "simulation", "trajectory-optimization", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Model-based toolbox for robotics - multibody dynamics with contact simulation, mathematical programming and trajectory optimization; used in the MIT manipulation and underactuated courses.", "takeaway_zh": "面向机器人的基于模型的工具箱：带接触的多体动力学仿真、数学规划与轨迹优化；MIT 操作与欠驱动课程即使用它。", "github_stars": 4248, "page": "https://embodied-ai-radar.com/e/drake.html", "labs": [ "mit", "tri" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2309.14545", "title": "Motions in Microseconds via Vectorized Sampling-Based Planning", "type": "paper", "arxiv": "2309.14545", "year": 2023, "venue": "ICRA 2024", "org": "Rice University (Kavraki Lab)", "url": null, "code": "https://github.com/KavrakiLab/vamp", "project": null, "topics": [ "control", "manipulation" ], "tags": [ "motion-planning", "simd", "sampling-based", "cpu" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "VAMP — vectorizes collision checking with CPU SIMD so sampling-based planners like RRT-Connect solve arm motion-planning queries in microseconds to milliseconds, no GPU needed.", "takeaway_zh": "VAMP：用 CPU SIMD 指令把碰撞检测向量化，使 RRT-Connect 等基于采样的规划器在微秒到毫秒级求解机械臂运动规划，无需 GPU。", "github_stars": 505, "page": "https://embodied-ai-radar.com/e/arxiv-2309.14545.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-1801.02854", "title": "Riemannian Motion Policies", "type": "paper", "arxiv": "1801.02854", "year": 2018, "venue": null, "org": "NVIDIA", "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "reactive-control", "motion-generation", "geometric-control" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "RMPs — describe each behaviour (reach, avoid obstacles, joint limits) as an acceleration policy plus a Riemannian metric, then combine them geometrically; the basis of later RMPflow and geometric fabrics controllers.", "takeaway_zh": "RMP：把每种行为（到达、避障、关节限位）表示为加速度策略加黎曼度量，再按几何方式组合；是后来 RMPflow 与几何织物（geometric fabrics）控制器的基础。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1801.02854.html", "labs": [ "nvidia" ], "code_license": null }, { "id": "arxiv-1709.07523", "title": "Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances", "type": "paper", "arxiv": "1709.07523", "year": 2017, "venue": "CDC 2017", "org": "UC Berkeley", "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "safety", "reachability", "tutorial" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Short tutorial on Hamilton-Jacobi reachability — computing the set of states from which a system can be kept safe under worst-case disturbances, and the tricks used to scale it up.", "takeaway_zh": "Hamilton-Jacobi 可达性分析简明教程：计算在最坏扰动下仍能保持系统安全的状态集合，以及将其扩展到更高维系统的方法。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1709.07523.html", "labs": [ "berkeley" ], "code_license": null }, { "id": "arxiv-2105.04830", "title": "Jerk-limited Real-time Trajectory Generation with Arbitrary Target States", "type": "paper", "arxiv": "2105.04830", "year": 2021, "venue": "RSS 2021", "org": "Karlsruhe Institute of Technology (KIT)", "url": null, "code": "https://github.com/pantor/ruckig", "project": null, "topics": [ "control", "manipulation" ], "tags": [ "trajectory-generation", "real-time", "jerk-limited", "library" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Ruckig — open-source C++/Python library for time-optimal, jerk-limited online trajectories to any target position, velocity and acceleration; a common drop-in for smooth arm motion on real controllers.", "takeaway_zh": "Ruckig：开源 C++/Python 库，可实时生成到任意目标位置、速度和加速度的时间最优、限加加速度（jerk）轨迹；常被直接用于真实控制器上的机械臂平滑运动。", "github_stars": 1443, "page": "https://embodied-ai-radar.com/e/arxiv-2105.04830.html", "labs": [], "code_license": "MIT" }, { "id": "ocs2-optimal-control-for-switched-systems", "title": "OCS2: Optimal Control for Switched Systems", "type": "code", "arxiv": null, "year": 2021, "venue": null, "org": "ETH Zurich (Robotic Systems Lab)", "url": null, "code": "https://github.com/leggedrobotics/ocs2", "project": null, "topics": [ "control", "locomotion" ], "tags": [ "library", "mpc", "optimal-control", "legged", "mobile-manipulation" ], "robot_types": [ "arm", "legged", "mobile" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "C++ toolbox for nonlinear MPC on switched systems (SLQ, DDP, SQP) with robot examples for legged robots and mobile manipulators; the base of many open-source legged MPC + whole-body control stacks.", "takeaway_zh": "面向切换系统的非线性 MPC C++ 工具箱（SLQ、DDP、SQP），附足式机器人与移动操作臂示例；许多开源足式 MPC + 全身控制方案都基于它。", "github_stars": 1537, "page": "https://embodied-ai-radar.com/e/ocs2-optimal-control-for-switched-systems.html", "labs": [ "eth" ], "code_license": "BSD-3-Clause" }, { "id": "dynamic-locomotion-in-the-mit-cheetah-3-through-convex-model", "title": "Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control", "type": "paper", "arxiv": null, "year": 2018, "venue": "IROS 2018", "org": "MIT (Biomimetic Robotics Lab)", "url": null, "code": "https://github.com/mit-biomimetics/Cheetah-Software", "project": null, "topics": [ "control", "locomotion" ], "tags": [ "mpc", "quadruped", "convex-optimization", "ground-reaction-forces" ], "robot_types": [ "legged" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Convex MPC for quadrupeds — simplified rigid-body dynamics turn ground-reaction-force planning into a small QP solved in real time; the classic model-based baseline behind many open-source quadruped controllers.", "takeaway_zh": "四足机器人的凸 MPC：用简化刚体动力学把足端地面反作用力规划变成可实时求解的小规模 QP；众多开源四足控制器背后的经典基于模型的基线。", "github_stars": 3336, "page": "https://embodied-ai-radar.com/e/dynamic-locomotion-in-the-mit-cheetah-3-through-convex-model.html", "labs": [ "mit" ], "code_license": "MIT" }, { "id": "legged-control", "title": "legged_control", "type": "code", "arxiv": null, "year": 2022, "venue": null, "org": "Qiayuan Liao (open-source project)", "url": null, "code": "https://github.com/qiayuanl/legged_control", "project": null, "topics": [ "control", "locomotion" ], "tags": [ "mpc", "whole-body-control", "state-estimation", "ros", "legged" ], "robot_types": [ "legged" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Open-source NMPC + whole-body control + state-estimation stack for quadrupeds built on OCS2 and ros-control, with a sim-to-real path; a popular starting point for model-based legged control.", "takeaway_zh": "基于 OCS2 与 ros-control 的开源四足控制栈：非线性 MPC + 全身控制 + 状态估计，并支持 sim2real；基于模型的足式控制常用的起点。", "github_stars": 1848, "page": "https://embodied-ai-radar.com/e/legged-control.html", "labs": [], "code_license": "BSD-3-Clause" }, { "id": "arxiv-2610.36961", "title": "Keeping Contact: Task-Adaptive Friction Margins for Bimanual Non-Prehensile Manipulation", "type": "paper", "arxiv": "2610.36961", "year": 2026, "venue": "RA-L", "org": null, "url": null, "code": null, "project": "https://keeping-contact.github.io/", "topics": [ "control", "manipulation" ], "tags": [ "contact", "friction", "socp", "bimanual", "non-prehensile", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Signed friction distance as a task-adaptive slip margin for bimanual tilting about a pivot; a sequence of SOCPs gives optimal contact forces. Real tests show adaptive margins beat fixed or no margins.", "takeaway_zh": "以带符号摩擦距离度量防滑裕度，并在双臂绕支点倾转物体时按任务需求自适应调整；一系列二阶锥规划给出全局最优接触力。真机实验显示自适应裕度优于固定裕度和无裕度。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36961.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.36535", "title": "A generalist policy for muscle-driven control across individual musculoskeletal models", "type": "paper", "arxiv": "2610.36535", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://woodater-thu.github.io/MyoAcross/", "topics": [ "control", "locomotion" ], "tags": [ "musculoskeletal", "morphology-conditioned", "motion-tracking", "biomechanics", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "MyoAcross trains one morphology-conditioned policy that tracks reference motions on unseen individual musculoskeletal models without fine-tuning. Reports 99.24% completion on 31 held-out models and 22.6% lower error.", "takeaway_zh": "MyoAcross 结合个体化肌骨模型构建、形态条件策略学习与虚拟模型增广，用一个策略驱动不同个体的肌骨模型跟踪参考动作而无需逐个微调；报告在 31 个未见个体模型上完成率 99.24%，跟踪误差比无条件 MLP 降低 22.6%，并与 120 人的地面反力和肌电数据做了对比。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36535.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.06218", "title": "TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation", "type": "paper", "arxiv": "2606.06218", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Dongwon-Son/TAM", "project": "https://dongwon-son.github.io/tam-project-page/", "topics": [ "control" ], "tags": [ "motion-transfer", "torque-adaptation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. A module for contact-rich motion transfer across sim-to-real gaps, payloads and robot instances, as an alternative to domain randomization or system ID.", "takeaway_zh": "CoRL 2026 Spotlight。面向富接触动作迁移的模块，应对仿真到现实、负载与个体差异，用于替代域随机化或系统辨识。", "github_stars": 23, "page": "https://embodied-ai-radar.com/e/arxiv-2606.06218.html", "labs": [], "code_license": "" }, { "id": "arxiv-2606.09108", "title": "RAM: Reachability Across Morphologies", "type": "paper", "arxiv": "2606.09108", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/TimWalter/ram", "project": "https://timwalter.github.io/ram", "topics": [ "control" ], "tags": [ "reachability", "morphology", "implicit-representation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. A morphology-conditioned implicit neural model of pose reachability, differentiable, self-collision aware and able to generalize to unseen morphologies.", "takeaway_zh": "CoRL 2026 Spotlight。以形态为条件的隐式神经位姿可达性模型，可微、考虑自碰撞，并能泛化到未见形态。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2606.09108.html", "labs": [], "code_license": "" }, { "id": "arxiv-2611.05372", "title": "PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning", "type": "paper", "arxiv": "2611.05372", "year": 2026, "venue": null, "org": "NYU", "url": null, "code": "https://github.com/Tinaliu0123/PolicyIK", "project": null, "topics": [ "control" ], "tags": [ "real", "sim", "arm", "inverse-kinematics", "rl" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Learned goal-conditioned inverse kinematics that can hold the offset a position controller needs at rest; reports errors within 1 mm and 0.2° on 93.5% of held-out simulated tasks and training directly on a real arm.", "takeaway_zh": "学习式目标条件逆运动学，能保持位置控制器静止时所需的偏置；报告在 93.5% 的留出仿真任务上误差低于 1 mm 和 0.2°，并可直接在真实机械臂上训练。", "github_stars": 70, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05372.html", "labs": [ "nyu" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.13825", "title": "Scalable Deep Unfolding of Conic Optimizers", "type": "paper", "arxiv": "2606.13825", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "control", "learning" ], "tags": [ "optimization", "deep-unfolding" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Makes deep unfolding work for large conic solvers via matrix-free implicit differentiation and a stable PSD-projection gradient; reports up to 50× speedups on SDP-based planning.", "takeaway_zh": "通过无矩阵隐式微分和稳定的半正定投影梯度，让深度展开适用于大规模锥优化求解器；报告在基于 SDP 的规划上最高提速 50 倍。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.13825.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.05493", "title": "cuRoboV2: Dynamics-Aware Motion Generation with Depth-Fused Distance Fields for High-DoF Robots", "type": "paper", "arxiv": "2603.05493", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/NVlabs/curobo", "project": null, "topics": [ "control", "manipulation" ], "tags": [ "motion-generation", "gpu" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "cuRoboV2 adds torque-aware B-spline trajectory optimization and GPU-native TSDF/ESDF perception for high-DoF robots.", "takeaway_zh": "cuRoboV2 新增考虑力矩限制的 B 样条轨迹优化和 GPU 原生 TSDF/ESDF 感知，面向高自由度机器人。", "github_stars": 1992, "page": "https://embodied-ai-radar.com/e/arxiv-2603.05493.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2505.03728", "title": "PyRoki: A Modular Toolkit for Robot Kinematic Optimization", "type": "paper", "arxiv": "2505.03728", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/chungmin99/pyroki", "project": "https://pyroki-toolkit.github.io", "topics": [ "control" ], "tags": [ "kinematics", "toolkit" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "PyRoki: a modular, cross-platform kinematic optimization toolkit that runs natively on CPU, GPU and TPU.", "takeaway_zh": "PyRoki：模块化、跨平台的运动学优化工具包，可原生运行在 CPU、GPU 和 TPU 上。", "github_stars": 1819, "page": "https://embodied-ai-radar.com/e/arxiv-2505.03728.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2609.25630", "title": "PAKT: Physically-Aligned Kinesthetic Teaching for Reinforcement Learning", "type": "paper", "arxiv": "2609.25630", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://pakt-website.github.io/pakt-website", "topics": [ "control" ], "tags": [ "kinesthetic-teaching", "real-world-rl", "impedance-control", "assembly", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Kinesthetic teaching for real-world RL that keeps human guidance within the policy's kinematic limits, plus an impedance control stack. Reports 23–48% shorter cycle time vs HIL-SERL on industrial insertion.", "takeaway_zh": "面向真实世界强化学习的示教框架：人手拖动示教时轨迹始终满足策略可执行的运动学限制，并配套阻抗控制栈。报告工业插装任务节拍时间比 HIL-SERL 缩短 23–48%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.25630.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.03132", "title": "Safe Streaming Flow Planning by Aligning Sampling Dynamics with Execution Dynamics", "type": "paper", "arxiv": "2610.03132", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Jang-seunghwan/SafeStreamingFlowPlanning", "project": "https://jang-seunghwan.github.io/SafeStreamingFlowPlanning/", "topics": [ "control" ], "tags": [ "flow-matching", "safety", "control-barrier-functions", "replanning", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Flow-matching planner whose sampling steps follow the execution dynamics, so safety (high-order CBFs) is enforced only on the executed step. Lower latency and better safety on navigation, racing and locomotion.", "takeaway_zh": "流匹配规划器让采样过程与系统执行动力学对齐，只需对实际执行的一步施加高阶控制障碍函数约束。在导航、竞速和运动任务上降低规划延迟并提升安全性。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2610.03132.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2606.26408", "title": "Exploring the Intrinsic Geometry of Diffusion Models with Constrained Inverse Kinematics", "type": "paper", "arxiv": "2606.26408", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/utiasSTARS/ConstraintIK", "project": "https://papers.starslab.ca/diffusion-ik-geometry/", "topics": [ "control" ], "tags": [ "diffusion", "inverse-kinematics", "manifolds", "analysis", "ur5" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Uses constrained IK, where solution manifolds are known exactly, to test what geometry diffusion models learn. On UR5 and Franka the intrinsic dimension read from the score matches the analytic degrees of freedom.", "takeaway_zh": "以解流形可精确知道的约束逆运动学为试验场，检验扩散模型学到的几何结构。在 UR5 和 Franka 上，从得分函数估计的内在维度与解析自由度一致。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2606.26408.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2609.21319", "title": "LEMCA: LLM-Guided Synthesis of Efficient Mode-Switching Control Architectures", "type": "paper", "arxiv": "2609.21319", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://lemca-robotics.github.io/", "topics": [ "control" ], "tags": [ "llm", "program-synthesis", "mode-switching", "efficiency", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "LLM-driven evolutionary search over mode-switching controllers written as programs, so cheap modes run in easy task phases. Reports designs beyond the Pareto front of one-size-fits-all controllers.", "takeaway_zh": "用 LLM 驱动的进化搜索，把多模式切换控制器写成可解释程序并迭代优化，使简单阶段使用低开销模式。报告找到的设计超越单一结构控制器的帕累托前沿。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.21319.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.23473", "title": "PRISM: Polynomial Representations for Interaction-Structured Motor Control", "type": "paper", "arxiv": "2607.23473", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/lsh3163/prism", "project": "https://lsh3163.github.io/prism/", "topics": [ "control" ], "tags": [ "policy-architecture", "polynomial-features", "humanoid", "contact-rich", "real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Replaces a policy MLP's first layer with learned multiplicative (bilinear) features, plug-and-play for RL and imitation. Reports humanoid survival up from 51.0% to 92.5%, holding under friction and payload shifts.", "takeaway_zh": "把策略 MLP 的第一层替换为可学习的乘性（双线性）特征，即插即用于强化学习与模仿学习。报告人形机器人存活率从 51.0% 提升到 92.5%，在摩擦、负载变化下依然有效。", "github_stars": 26, "page": "https://embodied-ai-radar.com/e/arxiv-2607.23473.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2602.14174", "title": "TDC: Sim-to-Real Transferable Directional Compliance for Contact-Rich Manipulation", "type": "paper", "arxiv": "2602.14174", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://yifei-y.github.io/project-pages/TDC/", "topics": [ "control" ], "tags": [ "compliance", "admittance-control", "sim-to-real", "vla", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Splits force regulation into a direction part learned in sim (task frame and control-mode outputs of a VLA-based policy) and a magnitude part set by hand in an admittance controller. Sim data only; four real tasks.", "takeaway_zh": "把力调节拆成两部分：方向分量（任务坐标系与控制模式，由基于 VLA 的策略在仿真数据上学习）和幅值分量（在导纳控制器中人工设定）。只用仿真数据，在四个真机任务上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.14174.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.18669", "title": "M$^3$P-R1: Reinforcement Learning for Large Language Model Guided Multi-Modal Motion Planning via MIP Code Generation", "type": "paper", "arxiv": "2609.18669", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "motion-planning", "mixed-integer-programming", "llm", "code-generation", "rl" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Fine-tunes an LLM with solver-based RL rewards to write executable mixed-integer programs for multi-modal motion planning (e.g. walk, then grasp), instead of answering directly.", "takeaway_zh": "用求解器结果作为奖励对 LLM 做强化学习微调，让它为多模态运动规划（如先走再抓）编写可执行的混合整数规划代码，而不是直接给出答案。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.18669.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.14087", "title": "Gradient-Free Neural Hamilton-Jacobi Reachability for Scalable Safety-Critical Control", "type": "paper", "arxiv": "2609.14087", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "hj-reachability", "safety", "neural-solver" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Discrete-time neural HJ reachability without PDE gradients: bang-bang structure and gradient-free value probes give teacher actions, turning adversarial actor learning supervised; a windowed curriculum adds stability.", "takeaway_zh": "无需 PDE 梯度的离散时间神经 HJ 可达性：利用最优安全干预的 bang-bang 结构和免梯度的价值探测构造教师动作，把对抗式策略学习变成监督学习；分窗口的时间课程稳定训练。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.14087.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.12831", "title": "VertexCBF: Improving Neural Control Barrier Functions via Vertex-Restricted Control Search", "type": "paper", "arxiv": "2609.12831", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "cbf", "safety", "hj-reachability", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns neural CBFs from an HJ value approximation; with control-affine dynamics and polytope inputs the optimum is at a vertex, so GPU vertex tree search makes labels. 15 systems + a robot among pedestrians.", "takeaway_zh": "通过近似 HJ 价值函数学习神经控制障碍函数；在控制仿射动力学和多面体输入集下最优解位于顶点，于是用 GPU 并行顶点树搜索生成监督点。在 15 个系统和行人避让真机实验中验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.12831.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.23896", "title": "BarrierFormer: Transformer-Guided Predictive Barrier Enforcement for Safe Robot Control", "type": "paper", "arxiv": "2609.23896", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "cbf", "safety", "transformer", "model-free" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "A causal transformer predicts a rollout (in place of a model) and a residual fix to a nominal controller; a barrier critic scores CBF violations along the rollout, so safety looks ahead like MPC.", "takeaway_zh": "因果 Transformer 预测未来推演（代替动力学模型）并给名义控制器输出残差修正；障碍评估器沿推演检查 CBF 约束违例，从而像 MPC 一样前瞻安全，却无需在线求解优化。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.23896.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.12027", "title": "Learning Unions of Convex Sets via Invertible Latent Decomposition for Path Planning", "type": "paper", "arxiv": "2606.12027", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "motion-planning", "convex-sets", "gcs", "invertible-network" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns an invertible map plus convex polytopes in its latent space, so convex-set planners work in high-DoF spaces and paths decode back feasible. Tested from 2D to 14-DoF; no false positives after refinement.", "takeaway_zh": "联合学习可逆映射及其潜空间中的凸多面体并集，使基于凸集的规划可用于高自由度空间，解码回原空间的路径仍可行。在 2D 至 14 自由度场景测试，细化后未观察到误报。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12027.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.16972", "title": "When Should a Robot Replan? Regret-Guided Update Scheduling in Time-Varying MDPs", "type": "paper", "arxiv": "2606.16972", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "replanning", "regret", "time-varying-mdp", "quadrotor" ], "robot_types": [ "aerial" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "With a limited budget for re-estimation and replanning under drifting dynamics, a dynamic-regret bound gives an online rule for when to update. Tested on simulated rover slip and a Crazyflie quadrotor.", "takeaway_zh": "在动力学漂移、重估与重规划次数有限时，用动态遗憾界推导出何时更新的在线规则。在模拟的打滑漫游车和 Crazyflie 四旋翼上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.16972.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.18606", "title": "On the Limits of Sampling-Based Reachability: Geometry, Dynamics, and Sample Complexity", "type": "paper", "arxiv": "2607.18606", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "reachability", "theory", "sample-complexity" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Theory: gives conditions under which sampled reachable sets are accurate in Hausdorff distance, and shows the sample need grows exponentially in both state dimension and horizon, with a matching lower bound.", "takeaway_zh": "理论工作：给出采样可达集在 Hausdorff 距离意义下准确的条件，并证明所需样本数随状态维度和时间跨度都呈指数增长，且给出相应下界。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.18606.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.05359", "title": "Optimal Control with Learned Critics under Unmodeled State Dependencies", "type": "paper", "arxiv": "2610.05359", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "mpc", "ilqr", "model-based-rl", "gpu" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Adds a residual dynamics network and a learned Q critic to iLQR MPC, with a GPU batched iLQR that solves thousands of problems in parallel, for model-based RL when contacts are poorly modeled.", "takeaway_zh": "在 iLQR 模型预测控制中加入残差动力学网络和学习得到的动作价值评估器，并实现 GPU 批量 iLQR 并行求解数千个问题，用于接触难以建模时的基于模型强化学习。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.05359.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.18760", "title": "Koopman DCM: Unstable Eigenfunctions as Data-driven Representations for Legged Balancing", "type": "paper", "arxiv": "2607.18760", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "legged", "balance", "koopman", "mpc" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Recasts divergent components of motion as unstable Koopman eigenfunctions learned from about an hour of real biped data; they improve walking tracking and give viability constraints for MPC.", "takeaway_zh": "把发散运动分量（DCM）重新表述为不稳定的 Koopman 特征函数，仅用约一小时真实双足数据学习得到；可改善行走跟踪，并为 MPC 提供可行性约束。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.18760.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.16022", "title": "$λ$-Reachability: Geometric-Horizon Safety Bellman Equations for Humanoid Safety", "type": "paper", "arxiv": "2606.16022", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "safety", "reachability", "humanoid", "rl" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "λ-Reachability learns Hamilton-Jacobi safety values with a TD(λ)-like multi-step estimator with a random horizon, proven to be a contraction; better safe-set estimates on simulated and real humanoids.", "takeaway_zh": "λ-Reachability 用类似 TD(λ)、随机视野的多步估计器学习 Hamilton-Jacobi 安全值函数，并证明其为压缩映射；在仿真与真实人形上安全集估计更准确。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.16022.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.26392", "title": "MPC-Injection: Biasing Off-Policy Locomotion RL Toward Controller-Induced Behavior Basins", "type": "paper", "arxiv": "2606.26392", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "rl", "mpc", "locomotion", "off-policy" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "MPC-Injection steers off-policy RL toward a usable gait by inserting MPC-generated transitions into training, with no reward shaping or discriminator; Go2 sim-to-real, and a barrel roll pure RL misses.", "takeaway_zh": "MPC-Injection 把 MPC 生成的状态转移注入离策略强化学习训练，在不改奖励、不加判别器的情况下把策略引向可用步态；在 Go2 上实现仿真到真实，并完成纯 RL 学不会的侧滚翻。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.26392.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.20333", "title": "VIGOR-MPC: Vision-Guided Objective Regulation MPC", "type": "paper", "arxiv": "2610.20333", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "control" ], "tags": [ "mpc", "vision", "distillation", "racing" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "VIGOR-MPC distills a privileged long-horizon MPC into a short-horizon MPC whose cost weights are inferred online from noisy vision; in vision-based racing it nears the teacher's lap times with 90%+ less compute per step.", "takeaway_zh": "VIGOR-MPC 将具有特权信息的长视野 MPC 蒸馏为短视野 MPC，其代价参数由含噪视觉在线推断；在视觉自主竞速中接近教师圈速，每步计算量减少 90% 以上。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.20333.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.10877", "title": "Sampling-based Model Predictive Control as Sequential Monte Carlo Inference", "type": "paper", "arxiv": "2611.10877", "year": 2026, "venue": null, "org": "Purdue University", "url": null, "code": "https://github.com/CoMMALab/PFMP", "project": null, "topics": [ "control" ], "tags": [ "mpc", "mppi", "sequential-monte-carlo", "jax", "mujoco" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-10", "takeaway_en": "Shows predictive sampling, MPPI, CEM and their variants are one SMC algorithm set by proposal, update rule and resampling threshold. Resampling cuts long-horizon collisions by 30%; programmable proposals lift push-T.", "takeaway_zh": "指出预测采样、MPPI、CEM 及其变体都是同一个序贯蒙特卡洛算法，只是提议分布、更新规则与重采样阈值不同。重采样使长时域碰撞率降低 30%，可编程提议显著提升 push-T 成功率。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2611.10877.html", "labs": [], "code_license": "" }, { "id": "arxiv-1707.06347", "title": "Proximal Policy Optimization Algorithms", "type": "paper", "arxiv": "1707.06347", "year": 2017, "venue": null, "org": "OpenAI", "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "rl", "on-policy", "sim" ], "robot_types": [], "highlight": true, "rating": 3, "added": null, "takeaway_en": "The clipped policy-gradient method behind most GPU-parallel sim-to-real locomotion and dexterity training.", "takeaway_zh": "采用截断目标的策略梯度方法，是大多数 GPU 并行仿真训练（运动控制、灵巧操作）背后的算法。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1707.06347.html", "labs": [ "openai" ], "code_license": null }, { "id": "arxiv-1801.01290", "title": "Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor", "type": "paper", "arxiv": "1801.01290", "year": 2018, "venue": null, "org": "UC Berkeley", "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "rl", "off-policy", "max-entropy" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Off-policy RL with an entropy bonus; sample-efficient enough to become the default for learning directly on real robots.", "takeaway_zh": "带熵奖励的离策略强化学习；样本效率较高，成为真机上直接学习的常用默认算法。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1801.01290.html", "labs": [ "berkeley" ], "code_license": null }, { "id": "arxiv-1011.0686", "title": "A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning", "type": "paper", "arxiv": "1011.0686", "year": 2010, "venue": null, "org": "CMU", "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "imitation", "dagger", "interactive" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "DAgger — query the expert on the states the learner itself visits, fixing the compounding errors of plain behavior cloning.", "takeaway_zh": "DAgger：在学习者自己访问到的状态上向专家查询标注，解决单纯行为克隆的误差累积问题。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1011.0686.html", "labs": [ "cmu" ], "code_license": null }, { "id": "arxiv-2302.02948", "title": "Efficient Online Reinforcement Learning with Offline Data", "type": "paper", "arxiv": "2302.02948", "year": 2023, "venue": null, "org": "UC Berkeley", "url": null, "code": "https://github.com/ikostrikov/rlpd", "project": null, "topics": [ "learning" ], "tags": [ "rl", "offline-to-online", "sample-efficiency" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "RLPD — a few simple changes to off-policy RL (symmetric sampling of offline and online data, layer norm, ensembles) make prior data speed up online learning.", "takeaway_zh": "RLPD：对离策略强化学习做几处简单改动（离线/在线数据对称采样、层归一化、集成），让已有数据加速在线学习。", "github_stars": 420, "page": "https://embodied-ai-radar.com/e/arxiv-2302.02948.html", "labs": [ "berkeley" ], "code_license": "MIT" }, { "id": "arxiv-2401.16013", "title": "SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning", "type": "paper", "arxiv": "2401.16013", "year": 2024, "venue": null, "org": "UC Berkeley", "url": null, "code": "https://github.com/rail-berkeley/serl", "project": null, "topics": [ "learning", "manipulation" ], "tags": [ "rl", "real", "arm", "open-source" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "An open-source stack for training manipulation policies with RL on a real arm — learned reward classifiers, reset-free training and an impedance controller.", "takeaway_zh": "在真实机械臂上用强化学习训练操作策略的开源软件栈：学习式奖励分类器、免重置训练与阻抗控制器。", "github_stars": 867, "page": "https://embodied-ai-radar.com/e/arxiv-2401.16013.html", "labs": [ "berkeley" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2410.21845", "title": "Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning", "type": "paper", "arxiv": "2410.21845", "year": 2024, "venue": null, "org": "UC Berkeley", "url": null, "code": "https://github.com/rail-berkeley/hil-serl", "project": null, "topics": [ "learning", "manipulation" ], "tags": [ "rl", "real", "arm", "human-in-the-loop" ], "robot_types": [ "hand" ], "highlight": true, "rating": 2, "added": null, "takeaway_en": "HIL-SERL — real-world RL with human corrections, reported to reach near-perfect success on precise assembly and dynamic tasks after a few hours of training.", "takeaway_zh": "HIL-SERL：结合人类纠正的真机强化学习，报告称在精密装配与动态任务上经数小时训练即可接近满分成功率。", "github_stars": 1582, "page": "https://embodied-ai-radar.com/e/arxiv-2410.21845.html", "labs": [ "berkeley" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2108.03298", "title": "What Matters in Learning from Offline Human Demonstrations for Robot Manipulation", "type": "paper", "arxiv": "2108.03298", "year": 2021, "venue": "CoRL 2021", "org": "Stanford", "url": null, "code": "https://github.com/ARISE-Initiative/robomimic", "project": null, "topics": [ "learning", "manipulation", "datasets" ], "tags": [ "imitation", "offline-rl", "benchmark", "human-demos" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "robomimic — a controlled study of imitation and offline RL on human-collected manipulation demos (demo quality, observation choices, history-aware BC), released with the framework and datasets.", "takeaway_zh": "robomimic：在人类采集的操作示教数据上系统比较模仿学习与离线强化学习（示教质量、观测选择、带历史的行为克隆），并开源框架与数据集。", "github_stars": 1602, "page": "https://embodied-ai-radar.com/e/arxiv-2108.03298.html", "labs": [ "stanford" ], "code_license": "MIT" }, { "id": "arxiv-2110.06169", "title": "Offline Reinforcement Learning with Implicit Q-Learning", "type": "paper", "arxiv": "2110.06169", "year": 2021, "venue": "ICLR 2022", "org": "UC Berkeley", "url": null, "code": "https://github.com/ikostrikov/implicit_q_learning", "project": null, "topics": [ "learning" ], "tags": [ "offline-rl", "expectile-regression", "offline-to-online" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "IQL — learns values with expectile regression without ever querying actions outside the dataset, then extracts a policy by advantage-weighted regression; a common base for offline-to-online robot RL.", "takeaway_zh": "IQL：用期望分位数回归学习价值函数、从不查询数据集外的动作，再用优势加权回归提取策略；是机器人离线到在线强化学习的常用基础。", "github_stars": 341, "page": "https://embodied-ai-radar.com/e/arxiv-2110.06169.html", "labs": [ "berkeley" ], "code_license": "MIT" }, { "id": "arxiv-2206.11251", "title": "Behavior Transformers: Cloning $k$ modes with one stone", "type": "paper", "arxiv": "2206.11251", "year": 2022, "venue": "NeurIPS 2022", "org": "NYU", "url": null, "code": "https://github.com/notmahi/bet", "project": null, "topics": [ "learning" ], "tags": [ "imitation", "multimodal", "transformer" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "BeT — clusters actions into k bins plus a continuous offset so a transformer can imitate multimodal demonstrations instead of averaging them.", "takeaway_zh": "BeT：把动作聚成 k 个类别再加连续偏移，让 Transformer 能模仿多模态示教，而不是把不同模式平均掉。", "github_stars": 153, "page": "https://embodied-ai-radar.com/e/arxiv-2206.11251.html", "labs": [ "nyu" ], "code_license": "MIT" }, { "id": "arxiv-2409.00588", "title": "Diffusion Policy Policy Optimization", "type": "paper", "arxiv": "2409.00588", "year": 2024, "venue": "ICLR 2025", "org": "Princeton", "url": null, "code": "https://github.com/irom-princeton/dppo", "project": null, "topics": [ "learning", "manipulation" ], "tags": [ "rl", "diffusion-policy", "fine-tuning", "sim-to-real" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "DPPO — fine-tunes a pre-trained diffusion policy with PPO by treating the denoising steps as part of the MDP; reports stable gains over other RL fine-tuning methods and sim-to-real transfer.", "takeaway_zh": "DPPO：把去噪步骤视为 MDP 的一部分，用 PPO 微调预训练扩散策略；报告称比其他强化学习微调方法更稳定，并实现了仿真到真机迁移。", "github_stars": 874, "page": "https://embodied-ai-radar.com/e/arxiv-2409.00588.html", "labs": [ "princeton" ], "code_license": "MIT" }, { "id": "arxiv-2006.04779", "title": "Conservative Q-Learning for Offline Reinforcement Learning", "type": "paper", "arxiv": "2006.04779", "year": 2020, "venue": "NeurIPS 2020", "org": "UC Berkeley", "url": null, "code": "https://github.com/aviralkumar2907/CQL", "project": null, "topics": [ "learning" ], "tags": [ "offline-rl", "value-learning" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "CQL — penalizes Q-values on actions outside the dataset so the learned value is a lower bound; a standard offline-RL baseline for learning from fixed robot logs.", "takeaway_zh": "CQL：压低数据集外动作的 Q 值，使学到的价值成为下界；是从固定机器人日志中学习的标准离线强化学习基线。", "github_stars": 496, "page": "https://embodied-ai-radar.com/e/arxiv-2006.04779.html", "labs": [ "berkeley" ], "code_license": "" }, { "id": "arxiv-2106.01345", "title": "Decision Transformer: Reinforcement Learning via Sequence Modeling", "type": "paper", "arxiv": "2106.01345", "year": 2021, "venue": "NeurIPS 2021", "org": "UC Berkeley · Google · FAIR", "url": null, "code": "https://github.com/kzl/decision-transformer", "project": null, "topics": [ "learning" ], "tags": [ "offline-rl", "transformer", "sequence-modeling" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Recasts RL as sequence modeling — a GPT-style model predicts actions conditioned on the desired return; an early bridge between transformers and control.", "takeaway_zh": "把强化学习改写成序列建模：GPT 式模型以期望回报为条件预测动作；是 Transformer 与控制结合的早期代表。", "github_stars": 2845, "page": "https://embodied-ai-radar.com/e/arxiv-2106.01345.html", "labs": [ "berkeley", "google-deepmind", "meta" ], "code_license": "MIT" }, { "id": "arxiv-1802.09477", "title": "Addressing Function Approximation Error in Actor-Critic Methods", "type": "paper", "arxiv": "1802.09477", "year": 2018, "venue": "ICML 2018", "org": "McGill University", "url": null, "code": "https://github.com/sfujim/TD3", "project": null, "topics": [ "learning" ], "tags": [ "rl", "off-policy", "continuous-control" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "TD3 — fixes DDPG's value overestimation with twin critics, delayed policy updates and target smoothing; still a standard continuous-control baseline.", "takeaway_zh": "TD3：通过双 Critic、延迟策略更新和目标策略平滑解决 DDPG 的价值高估问题，至今仍是连续控制的常用基线。", "github_stars": 2117, "page": "https://embodied-ai-radar.com/e/arxiv-1802.09477.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-1606.03476", "title": "Generative Adversarial Imitation Learning", "type": "paper", "arxiv": "1606.03476", "year": 2016, "venue": "NeurIPS 2016", "org": "Stanford", "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "imitation", "adversarial", "inverse-rl" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "GAIL — learns a policy whose state-action distribution a discriminator cannot tell apart from the expert's, skipping explicit reward recovery.", "takeaway_zh": "GAIL：训练策略使判别器无法区分其状态-动作分布与专家数据，无需显式恢复奖励函数。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1606.03476.html", "labs": [ "stanford" ], "code_license": null }, { "id": "arxiv-2109.00137", "title": "Implicit Behavioral Cloning", "type": "paper", "arxiv": "2109.00137", "year": 2021, "venue": "CoRL 2021", "org": "Google", "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "imitation", "energy-based", "multimodal" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Represents the policy as an energy-based model over actions instead of a regressor, which handles multimodal and discontinuous demonstrations much better.", "takeaway_zh": "用基于能量的模型（而非直接回归）表示策略，更好地处理多模态、不连续的示范数据。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2109.00137.html", "labs": [ "google-deepmind" ], "code_license": null }, { "id": "arxiv-2006.09359", "title": "AWAC: Accelerating Online Reinforcement Learning with Offline Datasets", "type": "paper", "arxiv": "2006.09359", "year": 2020, "venue": null, "org": "UC Berkeley", "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "rl", "offline-to-online", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Advantage-weighted actor-critic that pre-trains on prior demonstrations and keeps improving online, shown on real dexterous-hand tasks.", "takeaway_zh": "优势加权的 Actor-Critic：先用已有示范数据预训练，再在线持续提升，并在真实灵巧手任务上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2006.09359.html", "labs": [ "berkeley" ], "code_license": null }, { "id": "arxiv-1806.10293", "title": "QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation", "type": "paper", "arxiv": "1806.10293", "year": 2018, "venue": "CoRL 2018", "org": "Google", "url": null, "code": null, "project": null, "topics": [ "learning", "manipulation" ], "tags": [ "real", "grasping", "off-policy", "q-learning", "large-scale" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Off-policy Q-learning with a cross-entropy-method action optimizer, trained on several hundred thousand real grasp attempts collected by a fleet of robots; reports 96% grasp success on unseen objects.", "takeaway_zh": "使用交叉熵方法优化动作的离策略 Q 学习，在机器人集群采集的数十万次真实抓取上训练；报告对未见物体的抓取成功率为 96%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1806.10293.html", "labs": [ "google-deepmind" ], "code_license": null }, { "id": "arxiv-2107.09645", "title": "Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning", "type": "paper", "arxiv": "2107.09645", "year": 2021, "venue": "ICLR 2022", "org": "NYU, Meta AI", "url": null, "code": "https://github.com/facebookresearch/drqv2", "project": null, "topics": [ "learning" ], "tags": [ "sim", "pixels", "data-augmentation", "off-policy" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "DrQ-v2 - a simple model-free actor-critic with image augmentation that learns continuous control from pixels (incl. humanoid tasks in the DeepMind Control Suite) on a single GPU; a common visual-RL baseline.", "takeaway_zh": "DrQ-v2：带图像增强的简单无模型 actor-critic，单 GPU 即可从像素学习连续控制（包括 DeepMind Control Suite 的人形任务），是常用的视觉强化学习基线。", "github_stars": 438, "page": "https://embodied-ai-radar.com/e/arxiv-2107.09645.html", "labs": [ "meta", "nyu" ], "code_license": "MIT" }, { "id": "arxiv-2506.15799", "title": "Steering Your Diffusion Policy with Latent Space Reinforcement Learning", "type": "paper", "arxiv": "2506.15799", "year": 2025, "venue": "CoRL 2025", "org": "UC Berkeley, University of Washington", "url": null, "code": "https://github.com/ajwagen/dsrl", "project": "https://diffusion-steering.github.io", "topics": [ "learning", "manipulation" ], "tags": [ "real", "rl-finetuning", "diffusion-policy", "sample-efficient" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "DSRL — adapts a frozen diffusion policy by running RL over its input noise, not its weights; needs only forward passes, works with π0, and reports real-robot success rising from 20% to 90% in under 50 online episodes.", "takeaway_zh": "DSRL：不改扩散策略的权重，而是对其输入的初始噪声做强化学习来\"引导\"输出动作；只需前向推理、可用于 π0，论文报告真机任务在不到 50 次在线交互内成功率从 20% 升至 90%。", "github_stars": 241, "page": "https://embodied-ai-radar.com/e/arxiv-2506.15799.html", "labs": [ "berkeley", "uw" ], "code_license": "" }, { "id": "arxiv-2502.02538", "title": "Flow Q-Learning", "type": "paper", "arxiv": "2502.02538", "year": 2025, "venue": "ICML 2025", "org": "UC Berkeley", "url": null, "code": "https://github.com/seohongpark/fql", "project": "https://seohong.me/projects/fql/", "topics": [ "learning" ], "tags": [ "sim", "offline-rl", "flow-matching", "ogbench" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "FQL — a flow-matching BC policy plus a one-step policy that maximizes Q while distilling from it; no backprop through flow steps, strong on 73 OGBench / D4RL offline and offline-to-online tasks.", "takeaway_zh": "FQL：先用行为克隆训练流匹配策略，再训练一个最大化 Q 值、同时向流策略蒸馏的单步策略；避免对多步流采样反向传播，在 73 个 OGBench / D4RL 离线及离线转在线任务上表现突出。", "github_stars": 348, "page": "https://embodied-ai-radar.com/e/arxiv-2502.02538.html", "labs": [ "berkeley" ], "code_license": "MIT" }, { "id": "arxiv-2509.09674", "title": "SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning", "type": "paper", "arxiv": "2509.09674", "year": 2025, "venue": "ICLR 2026", "org": "Shanghai Jiao Tong University, Peking University, HKU, Shanghai AI Lab", "url": null, "code": "https://github.com/PRIME-RL/SimpleVLA-RL", "project": null, "topics": [ "learning", "vla" ], "tags": [ "sim", "rl-finetuning", "grpo", "libero", "robotwin" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Online RL for VLAs with GRPO and only binary success rewards on OpenVLA-OFT, plus exploration tweaks; reports 99.1% on LIBERO and 70.4% on RoboTwin 1.0 (vs 39.8% for the SFT baseline).", "takeaway_zh": "在 OpenVLA-OFT 上用 GRPO 与仅\"成功/失败\"二值奖励做在线强化学习，并加入若干促进探索的改动；论文报告 LIBERO 成功率 99.1%、RoboTwin 1.0 为 70.4%（SFT 基线 39.8%）。", "github_stars": 1908, "page": "https://embodied-ai-radar.com/e/arxiv-2509.09674.html", "labs": [ "shanghai-ai-lab", "pku", "sjtu", "hku" ], "code_license": "MIT" }, { "id": "arxiv-2303.05479", "title": "Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning", "type": "paper", "arxiv": "2303.05479", "year": 2023, "venue": "NeurIPS 2023", "org": "UC Berkeley, Stanford", "url": null, "code": "https://github.com/nakamotoo/Cal-QL", "project": null, "topics": [ "learning" ], "tags": [ "offline-to-online", "rl", "conservative-q" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Keeps conservative offline Q-values from dropping below a reference policy's value, which avoids the early performance dip when an offline-pretrained policy starts online fine-tuning.", "takeaway_zh": "约束保守的离线 Q 值不低于参考策略的价值，从而避免离线预训练策略开始在线微调时常见的性能先降后升。", "github_stars": 124, "page": "https://embodied-ai-radar.com/e/arxiv-2303.05479.html", "labs": [ "berkeley", "stanford" ], "code_license": "" }, { "id": "arxiv-2309.10150", "title": "Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions", "type": "paper", "arxiv": "2309.10150", "year": 2023, "venue": "CoRL 2023", "org": "Google DeepMind", "url": null, "code": null, "project": null, "topics": [ "learning", "manipulation" ], "tags": [ "real", "offline-rl", "transformer", "large-scale" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Discretizes each action dimension and predicts Q-values per dimension autoregressively with a Transformer, plus a conservative regularizer; trained offline on large real robot datasets mixing demos and autonomous data.", "takeaway_zh": "将每个动作维度离散化，用 Transformer 自回归地逐维预测 Q 值，并加入保守正则；在混合演示与自主采集数据的大规模真实机器人数据集上离线训练。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2309.10150.html", "labs": [ "google-deepmind" ], "code_license": null }, { "id": "arxiv-2510.06710", "title": "RLinf-VLA: A Unified and Efficient Framework for Reinforcement Learning of Vision-Language-Action Models", "type": "paper", "arxiv": "2510.06710", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/RLinf/RLinf", "project": null, "topics": [ "learning", "vla" ], "tags": [ "sim", "rl-finetuning", "ppo", "grpo", "infrastructure", "libero", "maniskill", "robotwin" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Open RL training framework for VLAs (PPO/GRPO across GPU-parallel simulators); reports 98.11% on 130 LIBERO tasks and 97.66% on 25 ManiSkill tasks after RL — a common base for VLA+RL work.", "takeaway_zh": "面向 VLA 的开源强化学习训练框架（PPO/GRPO，支持 GPU 并行仿真器）；论文报告经 RL 后在 130 个 LIBERO 任务上成功率 98.11%、25 个 ManiSkill 任务上 97.66%，是 VLA+RL 研究常用的底座。", "github_stars": 5627, "page": "https://embodied-ai-radar.com/e/arxiv-2510.06710.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.25736", "title": "Learning from Fleet Experience: Collective Experience Learning Across 96 Robots", "type": "paper", "arxiv": "2610.25736", "year": 2026, "venue": null, "org": "Tsinghua, AgiBot, Infinigence AI", "url": null, "code": "https://github.com/RLinf/RLinf", "project": "https://cel-robot-learning.github.io", "topics": [ "learning", "manipulation" ], "tags": [ "real", "fleet-learning", "deployment", "failure-retrieval" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-10-23", "takeaway_en": "Deployed robots pool their runs; failures retrieve matching successful segments from the fleet buffer to fine-tune on, with no new demos. Reports 93–99% on five real tasks; 96 robots lift phone packing 79% → 98%.", "takeaway_zh": "部署中的机器人共享执行经验：用失败片段在机群经验库中检索相似的成功片段来微调策略，无需新增示教或人工纠正。论文报告五个真机任务成功率 93–99%，96 台机器人把手机装盒任务从 79% 提升到 98%。", "github_stars": 5627, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25736.html", "labs": [ "tsinghua", "agibot" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.18596", "title": "ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models", "type": "paper", "arxiv": "2610.18596", "year": 2026, "venue": null, "org": "NVIDIA, KAIST", "url": null, "code": null, "project": "https://accrue-rl.github.io", "topics": [ "learning", "vla", "manipulation" ], "tags": [ "real", "sim", "continual-learning", "off-policy-rl", "rl-finetuning", "assembly" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-24", "takeaway_en": "Continual off-policy RL for VLAs — small actor/critic networks carry over between tasks, distilled on a few kept episodes to avoid forgetting; reports 31–68.5% less real-robot time on new assembly tasks.", "takeaway_zh": "面向 VLA 的持续离策略强化学习：轻量 actor/critic 在任务间延续，用少量保留的旧任务轨迹做蒸馏以防遗忘。论文报告新的接触式装配任务所需真机训练时间减少 31–68.5%，旧任务几乎不遗忘。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.18596.html", "labs": [ "nvidia", "kaist" ], "code_license": null }, { "id": "arxiv-2610.28589", "title": "AgenticRoboRL: Bringing Coding-Agent Post-Training to Agentic Robotics", "type": "paper", "arxiv": "2610.28589", "year": 2026, "venue": null, "org": "University of Central Florida", "url": null, "code": "https://github.com/xinxinliu-ai/AgenticRoboRL", "project": "https://xinxinliu-ai.github.io/AgenticRoboRL/", "topics": [ "learning", "vla" ], "tags": [ "llm-planner", "reinforcement-learning", "agentic", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-24", "takeaway_en": "Post-trains an open 9B robot planner (SFT + multi-turn RL) across harnesses that call VLA skills or write control code; reports LIBERO-PRO success rising from 18.1% to 71.0% without external memory.", "takeaway_zh": "像训练编程智能体一样对 9B 开源机器人规划器做后训练（SFT + 多轮强化学习），适配调用 VLA 技能和生成控制代码两类执行框架；报告在 LIBERO-PRO 上无外部记忆成功率从 18.1% 提升到 71.0%。", "github_stars": 65, "page": "https://embodied-ai-radar.com/e/arxiv-2610.28589.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.28698", "title": "Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds", "type": "paper", "arxiv": "2610.28698", "year": 2026, "venue": null, "org": "Tsinghua Spatial Intelligence Lab", "url": null, "code": "https://github.com/THUSI-Lab/GameScaling", "project": "https://gamescaling.github.io", "topics": [ "learning" ], "tags": [ "scaling-law", "games", "virtual-worlds", "continued-pretraining", "sim" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-10-24", "takeaway_en": "Continued pre-training study for game-playing agents: 120 runs over 0.8B–27B models, 50–1,600 h of data and multi-world mixes; reports diversity aids transfer to related worlds, little to distant ones.", "takeaway_zh": "针对游戏中的具身智能体做持续预训练的对照研究：120 组配置覆盖 0.8B–27B 模型、50–1600 小时数据和多世界混合；报告数据多样性有助于迁移到相近世界，对差异大的世界帮助有限。", "github_stars": 510, "page": "https://embodied-ai-radar.com/e/arxiv-2610.28698.html", "labs": [ "tsinghua" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.29677", "title": "Toward Recursive Self-Improvement in Physical AI", "type": "paper", "arxiv": "2610.29677", "year": 2026, "venue": null, "org": "National University of Singapore", "url": null, "code": null, "project": "https://github.com/Physical2RSI/Awesome-Physical2RSI", "topics": [ "learning" ], "tags": [ "survey", "self-improvement", "awesome-list" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-25", "takeaway_en": "Survey of recursive self-improvement in robot learning — improving skills, then the process that makes them — and when gains carry forward; notes no physical study has yet isolated gains from recursion itself.", "takeaway_zh": "把\"递归自我改进\"放到机器人学习语境下的综述：从提升技能到改进产生技能的过程，并总结增益能持续累积的条件；指出现有真机研究尚未单独验证\"递归\"本身带来的收益。附论文列表。", "github_stars": 82, "page": "https://embodied-ai-radar.com/e/arxiv-2610.29677.html", "labs": [ "nus" ], "code_license": "MIT" }, { "id": "arxiv-2610.26848", "title": "Where is Physical RSI capability from? An Observational Scaling Law of LLM-as-policy", "type": "paper", "arxiv": "2610.26848", "year": 2026, "venue": null, "org": "Galbot", "url": null, "code": "https://github.com/PhysRSI-Eval/PhysRSI-Eval-code", "project": "https://physrsi-eval.github.io/", "topics": [ "learning", "datasets" ], "tags": [ "benchmark", "self-improvement", "vlm-agent", "scaling-law", "technical-report" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-25", "takeaway_en": "PhysRSI-eval scores VLM agents on 18 manipulation tasks under a fixed trial budget on five axes, incl. learning from failure; reports similar benchmark averages hide different improvement speeds.", "takeaway_zh": "PhysRSI-eval 在固定交互预算下用 18 个操作任务、五个维度（含从失败中改进）评测 VLM 智能体；报告公开基准平均分相近的模型改进速度差别很大，且早期物理反馈可用于预测后续进展。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2610.26848.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.25376", "title": "TILDE: Teacher-in-the-Loop Distillation from Embodied Foundation Models", "type": "paper", "arxiv": "2610.25376", "year": 2026, "venue": null, "org": "The University of Hong Kong", "url": null, "code": "https://github.com/hku-sail/TILDE", "project": "https://hku-sail.github.io/TILDE/", "topics": [ "learning", "vla" ], "tags": [ "distillation", "dagger", "recovery", "reward-model", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-25", "takeaway_en": "Distills a pretrained VLA teacher into a policy without action pretraining via on-policy supervision plus teacher-run recoveries, triggered by a reward model (AutoDAgger); reports up to 4.1x over BC on 3 real tasks.", "takeaway_zh": "把预训练具身基础模型（教师）蒸馏到未做动作预训练的策略：在学生访问的状态上做在线监督，并由奖励模型监控进度、自动触发教师接管采集纠错轨迹（AutoDAgger，无需人工看护）；报告在 3 个真机任务上比行为克隆最高提升 4.1 倍。", "github_stars": 10, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25376.html", "labs": [ "hku" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.31845", "title": "EgoForesight: Effectively Scaling Policy Learning with Noisy Egocentric Actions", "type": "paper", "arxiv": "2610.31845", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/LinShan-Bin/EgoForesight", "project": "https://lin-shan.com/EgoForesight", "topics": [ "learning", "vla" ], "tags": [ "real", "sim", "human-video", "egocentric", "scaling", "dexterous-hand" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Co-trains a VLA on robot demos and noisy egocentric-video actions by weighting each source by reliability and distilling from clean future frames; reports steeper scaling than the baseline up to 30k hours of data.", "takeaway_zh": "将机器人示教与动作标签噪声较大的第一视角视频联合训练 VLA：按数据源可靠性分配监督权重，并从干净的未来画面蒸馏表征；报告在最多 3 万小时预训练数据范围内扩展斜率高于基线。", "github_stars": 15, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31845.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.29264", "title": "Training in Deployment: Human-in-the-loop Online Post-Training for Robot Manipulation", "type": "paper", "arxiv": "2610.29264", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://logosroboticsgroup.github.io/TIDE/", "topics": [ "learning", "manipulation" ], "tags": [ "human-in-the-loop", "online-learning", "post-training", "bimanual", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "TIDE updates a pretrained policy online from human interventions and its own rollouts, with no critic or reward model; reports gains on four real bimanual tasks, each session finishing in under an hour.", "takeaway_zh": "TIDE 在部署过程中利用人类干预和策略自身的执行数据在线更新预训练策略，无需价值函数或奖励模型；报告在四个真实双臂任务上优于初始策略，每次在线后训练不到一小时完成。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.29264.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.32093", "title": "DiscoDemo: Discovering Efficient and Diverse Robot Demonstrations for Imitation Learning", "type": "paper", "arxiv": "2610.32093", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/DAVIAN-Robotics/DiscoDemo", "project": "https://davian-robotics.github.io/DiscoDemo", "topics": [ "learning", "manipulation" ], "tags": [ "rl", "data-generation", "imitation-learning", "diversity", "sim", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Uses RL as a demo generator, not a controller: a latent-conditioned policy finds several distinct successful solutions, exported as an imitation dataset; reports 78% real-robot success vs. 39% for the best baseline.", "takeaway_zh": "把强化学习当作示教数据生成器而非部署控制器：以隐变量为条件的策略为同一任务找到多种不同的成功解法，导出为模仿学习数据集；报告真机下游成功率从最强基线的 39% 提升到 78%。", "github_stars": 18, "page": "https://embodied-ai-radar.com/e/arxiv-2610.32093.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.36374", "title": "HIDRA: Hierarchical Dual-Routing Attention for Replay-Free Lifelong Imitation Learning", "type": "paper", "arxiv": "2610.36374", "year": 2026, "venue": "NeurIPS 2026", "org": null, "url": null, "code": "https://github.com/yfqi/HIDRA", "project": null, "topics": [ "learning", "manipulation" ], "tags": [ "lifelong-learning", "imitation-learning", "mixture-of-experts", "libero" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Replay-free lifelong imitation learning: hierarchical dual-routing attention retrieves experts by instruction, then refines by context, plus key regularization against drift. Reports beating replay baselines on LIBERO.", "takeaway_zh": "无回放的终身模仿学习：分层双路由注意力先按指令检索专家、再按上下文细化，并用键级几何正则抑制表征漂移带来的路由干扰；报告在多个 LIBERO 任务序列上优于无回放和有回放基线。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36374.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.25692", "title": "Progress-Guided Preference Generation for Aligning Generative Control Policies", "type": "paper", "arxiv": "2610.25692", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://pgpg2026.github.io/", "topics": [ "learning", "manipulation" ], "tags": [ "preference-learning", "diffusion-policy", "flow-matching", "fine-tuning", "long-horizon" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Fine-tunes diffusion and flow policies with automatic preference labels: rollouts are ranked by progress measured against a few expert demos, no human labels or reward design. Reports beating SFT on long-horizon tasks.", "takeaway_zh": "用自动生成的偏好标签微调扩散与流匹配策略：以少量专家示教为参照衡量自主回放的顺序进度并据此排序，无需人工标注或手工奖励；报告在长时程操作任务上优于监督微调基线。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25692.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.28893", "title": "Flexible Language-Guided Representation Editing for Adaptive Robot Behavior", "type": "paper", "arxiv": "2610.28893", "year": 2026, "venue": "CoRL 2026", "org": "MIT", "url": null, "code": null, "project": "https://mit-clear-lab.github.io/FLEX/", "topics": [ "learning", "manipulation" ], "tags": [ "language-corrections", "reward-features", "representation-learning", "llm", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-10-29", "takeaway_en": "FLEX lets language corrections edit a robot's task features via add, generalize and mask (not add-only). Reports higher normalized reward than other editing methods in 2D nav, Franka tabletop and a real user study.", "takeaway_zh": "FLEX 用自然语言纠正来迭代修改机器人的任务特征集合，支持添加、泛化、屏蔽三种操作（而非只能添加）；在二维导航、Franka 桌面操作和真实用户研究中报告比其他特征编辑方法获得更高的归一化奖励。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.28893.html", "labs": [ "mit" ], "code_license": null }, { "id": "arxiv-2610.38221", "title": "CuraEval: Evaluating Demonstration Curation on Mixed-Skill Robot Data", "type": "paper", "arxiv": "2610.38221", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/jiuyixu25/CuraEval", "project": null, "topics": [ "learning", "datasets" ], "tags": [ "data-curation", "imitation-learning", "diffusion-policy", "robomimic", "evaluation", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "Sanity check for demo-curation methods on mixed-skill robomimic data: one score ranks demos almost exactly like episode length, and after Holm correction no curated subset significantly beats training on all data.", "takeaway_zh": "对示范数据筛选方法做严格对照：在混合技能的 robomimic 数据上与等量随机子集和全量训练比较，发现某评分几乎等同于按轨迹长度排序；经多重比较校正后，没有任何筛选条件显著优于全量数据训练（CoRL 2026 Workshop 论文）。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2610.38221.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.38266", "title": "Recursive Self-Improvement in Physical AI: Mechanisms, Evidence, and Evaluation", "type": "paper", "arxiv": "2610.38266", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://github.com/nkd-lkz/physical-ai-rsi-survey", "topics": [ "learning" ], "tags": [ "survey", "self-improvement", "evaluation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Survey separating ordinary learner updates from improvers that revise how a robot learns, with an L0–L3 rubric from task-local adaptation to verified improver gain, and proposed comparisons from shared learner starts.", "takeaway_zh": "综述：区分普通的学习器更新与会修改学习过程本身的“改进器”，给出从任务内适应到已验证改进增益的 L0–L3 分级，并提出从相同起点比较的评测方法。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2610.38266.html", "labs": [], "code_license": "" }, { "id": "arxiv-2602.02481", "title": "Flow Policy Gradients for Robot Control", "type": "paper", "arxiv": "2602.02481", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/amazon-far/fpo-control", "project": "https://hongsukchoi.github.io/fpo-control", "topics": [ "learning" ], "tags": [ "flow-matching", "policy-gradient", "locomotion" ], "robot_types": [ "humanoid", "legged" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. Makes flow matching policy gradients work for robot control, bypassing action likelihoods, so policies need not be Gaussian. Applied to legged locomotion, humanoid motion tracking and manipulation.", "takeaway_zh": "CoRL 2026 Oral。让流匹配策略梯度可用于机器人控制：绕过动作似然计算，策略不再限于高斯分布。应用于足式运动、人形动作跟踪与操作。", "github_stars": 318, "page": "https://embodied-ai-radar.com/e/arxiv-2602.02481.html", "labs": [], "code_license": "" }, { "id": "arxiv-2604.23073", "title": "RL Token: Bootstrapping Online RL with Vision-Language-Action Models", "type": "paper", "arxiv": "2604.23073", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "online-rl", "vla-finetuning", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. Adapts a VLA to expose a compact RL token, an interface for sample-efficient online RL, so a pretrained policy gains precision and speed from a few hours of real-world practice.", "takeaway_zh": "CoRL 2026 Oral。让 VLA 输出紧凑的 RL token 作为在线强化学习接口，预训练策略只需几小时真实练习即可提升精度与速度。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.23073.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.08877", "title": "FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space", "type": "paper", "arxiv": "2607.08877", "year": 2026, "venue": "CoRL 2026", "org": "Microsoft", "url": null, "code": "https://github.com/microsoft/FlowDAgger", "project": "https://microsoft.github.io/FlowDAgger", "topics": [ "learning" ], "tags": [ "human-in-the-loop", "flow-matching", "adaptation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Adapts frozen generative policies from human interventions in latent space, as a cheaper alternative to mass data collection or online RL on hardware.", "takeaway_zh": "CoRL 2026 Spotlight。在隐空间中根据人类干预适配冻结的生成式策略，替代大规模采集或实机在线强化学习。", "github_stars": 90, "page": "https://embodied-ai-radar.com/e/arxiv-2607.08877.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2607.19919", "title": "Diffusion ReRoll: Revisable Denoising for Robotic Sequential Prediction", "type": "paper", "arxiv": "2607.19919", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://seonsoo-p1.github.io/DiffusionReRoll/", "topics": [ "learning" ], "tags": [ "diffusion", "sequence-prediction" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Diffusion sequence prediction with selective re-noising, so segments of the horizon can be refined again using context from the rest.", "takeaway_zh": "CoRL 2026 Spotlight。在扩散序列预测中选择性重新加噪，让时间跨度内的片段可以借助其余片段的上下文再次修正。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.19919.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.03622", "title": "D&A: Decoupling Description and Assessment Enables VLMs as Zero-Shot Robotic Reward Models", "type": "paper", "arxiv": "2611.03622", "year": 2026, "venue": null, "org": "University of Washington", "url": null, "code": "https://github.com/memmelma/dna", "project": "https://weirdlabuw.github.io/dna", "topics": [ "learning" ], "tags": [ "reward", "vlm", "zero-shot", "prompting" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Prompt-only task-progress scoring: the VLM first describes what happens and defines absolute success, then scores with an explicit rubric. This curbs hallucinated progress on failed runs, and the rewards can drive RL.", "takeaway_zh": "只靠提示词的任务进度评估：先让 VLM 客观描述画面并定义绝对成功标准，再按明确评分表打分，减少对失败执行“幻觉式”的进度判断，其奖励可用于下游强化学习。", "github_stars": 18, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03622.html", "labs": [ "uw" ], "code_license": "MIT" }, { "id": "arxiv-2611.03501", "title": "When Robots Outweigh Objects: How Inertia Mismatch Shapes Robot Policy Learning", "type": "paper", "arxiv": "2611.03501", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://younghyopark.me/inertia-mismatch/", "topics": [ "learning", "manipulation" ], "tags": [ "imitation-learning", "reinforcement-learning", "contact", "hardware-design", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Simulation study varying robot and object mass: RL copes, but behavior cloning does better when the robot is lighter than the object, and more data narrows the gap without closing it. Lower-inertia demos help.", "takeaway_zh": "在仿真中改变机器人与物体质量：强化学习影响不大，行为克隆则在机器人比物体轻时更可靠，增加数据只能缩小而无法消除差距。采集低惯量示教可部分缓解。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03501.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.01565", "title": "Beyond Validation Loss: Coverage and Reliability Dynamics in Robot Policy Pretraining", "type": "paper", "arxiv": "2611.01565", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://zhanyisun.github.io/robot-policy-learning-dynamics/", "topics": [ "learning" ], "tags": [ "evaluation", "generative-policy", "post-training", "checkpoint-selection" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Tracks closed-loop pass@k, the chance that one of k rollouts succeeds. Checkpoints with equal pass@1 can post-train differently, so it proposes offline checkpoint selection that balances coverage and reliability.", "takeaway_zh": "提出跟踪闭环 pass@k（k 次尝试至少成功一次的概率）。pass@1 相同的检查点在后训练中可能表现不同，因此给出兼顾覆盖度与可靠性的离线检查点选择方法。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.01565.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.00405", "title": "Behavior Graphs: Unsupervised Graph Structures for Interpreting Robot Deployment Data", "type": "paper", "arxiv": "2611.00405", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://try.behavior-graphs.com/", "topics": [ "learning", "datasets" ], "tags": [ "deployment-data", "interpretability", "data-flywheel", "user-study", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Compresses policy rollouts into one directed graph of behavior modes linked to outcomes, to guide what data to collect next. In a user study on three real tasks, 16 of 18 participants preferred it to rollout videos.", "takeaway_zh": "把策略回放压缩成一张有向图，将行为模式与结果相连，帮助决定下一步采集什么数据。在三个真实任务的用户研究中，18 名参与者有 16 名更喜欢它而非回放视频。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.00405.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.05295", "title": "GSBR: Geometric Support Behavior Regularization for Online RL", "type": "paper", "arxiv": "2611.05295", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/charlesxu0124/gsbr", "project": "https://charlesxu0124.github.io/gsbr/", "topics": [ "learning" ], "tags": [ "sim", "online-rl", "residual-rl" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Online RL on top of a pretrained policy, penalised by distance from that policy's support: free inside it, smoothly costly outside; reports average success rising from 33% to 95% across 19 benchmark tasks.", "takeaway_zh": "在预训练策略之上做在线强化学习，按动作到其支撑集的距离施加惩罚：集内不罚，集外平滑增长；报告 19 个基准任务的平均成功率从 33% 升至 95%。", "github_stars": 18, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05295.html", "labs": [], "code_license": "" }, { "id": "arxiv-2609.06061", "title": "SCoCaT: Success Conditioned Constrained Reinforcement Learning for Spacecraft Docking", "type": "paper", "arxiv": "2609.06061", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning", "control" ], "tags": [ "safe-rl", "constrained-rl", "space" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Shows termination-based constrained RL can learn to avoid the goal when constraints tighten near it, and fixes this with a dense success critic; validated on spacecraft docking tasks.", "takeaway_zh": "发现基于终止的约束强化学习在目标附近约束收紧时会学会回避目标，并用稠密成功信号的辅助价值网络解决；在航天器对接任务上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.06061.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.12431", "title": "Control-Ready Uncertainty for Trajectory Diffusion", "type": "paper", "arxiv": "2610.12431", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://zackaxue.github.io/SCOPE-project-page/", "topics": [ "learning", "control" ], "tags": [ "diffusion", "uncertainty" ], "robot_types": [ "arm" ], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "SCOPE gives diffusion trajectory models calibrated uncertainty tubes without Monte Carlo sampling, by distilling score curvature; tested in crowd navigation and Franka manipulation.", "takeaway_zh": "SCOPE 通过蒸馏得分函数曲率，让扩散轨迹模型无需蒙特卡洛采样即可给出校准的不确定性管道；在人群导航和 Franka 操作上测试。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.12431.html", "labs": [], "code_license": null }, { "id": "arxiv-2510.23176", "title": "TARC: Time-Adaptive Robotic Control", "type": "paper", "arxiv": "2510.23176", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://arnavsukhija.github.io/projects/tarc", "topics": [ "learning", "locomotion" ], "tags": [ "rl", "control-frequency" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A policy predicts each action and how long to hold it. Reports matching high-frequency control at under half the rate on an RC car and Unitree Go1, plus simulated VLA inference.", "takeaway_zh": "策略同时预测动作及其保持时长。报告在遥控车、Unitree Go1 和仿真 VLA 上以不到一半的控制频率达到高频控制的性能。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2510.23176.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.18964", "title": "FedGuide: Diffusion Prior Alignment and Value Baseline Guidance for Heterogeneous Federated Reinforcement Learning", "type": "paper", "arxiv": "2609.18964", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "federated-rl", "diffusion" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "Federated RL for heterogeneous clients that aggregates diffusion behavior priors with optimal transport rather than averaging policies, plus a low-variance DICE value baseline.", "takeaway_zh": "面向异构客户端的联邦强化学习：不再平均策略，而是用最优传输聚合扩散行为先验，并加入低方差的 DICE 价值基线。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.18964.html", "labs": [], "code_license": null }, { "id": "arxiv-2505.05753", "title": "Towards Embodiment Scaling Laws in Robot Locomotion", "type": "paper", "arxiv": "2505.05753", "year": 2025, "venue": "CoRL 2025", "org": null, "url": null, "code": "https://github.com/BoAi01/embodiment-scaling-laws", "project": "https://embodiment-scaling-laws.github.io/", "topics": [ "learning", "locomotion" ], "tags": [ "cross-embodiment", "scaling" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Tests 'embodiment scaling laws' on about 1,000 generated robots: does training on more bodies improve generalization to unseen ones?", "takeaway_zh": "在约 1000 个生成的机器人形态上检验“形态缩放定律”：用更多形态训练能否提升对未见形态的泛化？", "github_stars": 36, "page": "https://embodied-ai-radar.com/e/arxiv-2505.05753.html", "labs": [], "code_license": "BSD-3-Clause" }, { "id": "arxiv-2611.04974", "title": "LLMs as MDP Designers: A Dependency-Aware Agentic Framework for Automated Robot Policy Generation", "type": "paper", "arxiv": "2611.04974", "year": 2026, "venue": "NeurIPS 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "llm", "rl", "agent", "isaac-sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "ARPG has an LLM design the RL pipeline before deployment: MDP first, then simulator code, with checks that state, action and reward agree; reports more successful policies than monolithic LLM baselines in Isaac Sim.", "takeaway_zh": "ARPG 让大模型在部署前设计强化学习流程：先定 MDP，再写仿真代码，并校验状态、动作与奖励是否一致；报告在 Isaac Sim 中成功率高于单体式大模型基线。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.04974.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.05838", "title": "RACE: Calibrated Cumulative Scoring for Rollout-Free Robot Policy Checkpoint Selection", "type": "paper", "arxiv": "2611.05838", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://changyeon.site/race", "topics": [ "learning" ], "tags": [ "evaluation", "checkpoint-selection", "imitation-learning", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Ranks checkpoints without rollouts: action deviations are calibrated against state-dependent thresholds from held-out demos, then combined in a running product. Reports rankings that align with real deployment.", "takeaway_zh": "无需实机回放即可排序检查点：用保留示教得到的状态相关阈值校准动作偏差，再按累乘聚合。论文报告其排序与真实机器人部署成功率一致。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05838.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.05571", "title": "ELA: End-to-End Latent Action Policy Learning from Video Demonstrations", "type": "paper", "arxiv": "2611.05571", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://end2end-latent-action.github.io/", "topics": [ "learning", "manipulation" ], "tags": [ "imitation-learning", "latent-action", "video-demonstrations", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Jointly trains a latent action model, latent policy and action decoder, so plentiful action-free video plus a few labelled robot demos can train a policy. Reports beating prior latent-action baselines.", "takeaway_zh": "联合训练潜在动作模型、潜在策略和动作解码器，用大量无动作标注的视频加少量带标注示教即可训练策略。报告在仿真和真实任务上优于已有潜在动作方法。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05571.html", "labs": [], "code_license": null }, { "id": "arxiv-2404.19664", "title": "Towards Generalist Robot Learning from Internet Video: A Survey", "type": "paper", "arxiv": "2404.19664", "year": 2024, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "survey", "video", "internet-data" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Survey of learning from videos as a way around robot learning's data bottleneck, drawing on internet-scale video.", "takeaway_zh": "综述从视频学习的方法，以互联网规模视频缓解机器人学习的数据瓶颈。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2404.19664.html", "labs": [], "code_license": null }, { "id": "arxiv-2503.24361", "title": "Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation", "type": "paper", "arxiv": "2503.24361", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": "https://co-training.github.io/", "topics": [ "learning" ], "tags": [ "sim2real", "co-training", "imitation-learning" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A simple recipe that trains on both real and simulated data to supplement expensive real-world collection for vision-based manipulation.", "takeaway_zh": "简单的联合训练做法：同时使用真实和仿真数据，弥补视觉操作中真实数据采集昂贵的问题。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2503.24361.html", "labs": [], "code_license": null }, { "id": "arxiv-2304.11477", "title": "LLM+P: Empowering Large Language Models with Optimal Planning Proficiency", "type": "paper", "arxiv": "2304.11477", "year": 2023, "venue": null, "org": null, "url": null, "code": "https://github.com/Cranial-XIX/llm-pddl", "project": null, "topics": [ "learning" ], "tags": [ "llm", "planning", "pddl" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "LLM+P has an LLM translate problems into a formal form for classical planners, which handle the long-horizon planning LLMs struggle with.", "takeaway_zh": "LLM+P 让大模型把问题转成规范形式交给经典规划器，解决大模型不擅长的长程规划。", "github_stars": 466, "page": "https://embodied-ai-radar.com/e/arxiv-2304.11477.html", "labs": [], "code_license": "" }, { "id": "arxiv-2212.04088", "title": "LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models", "type": "paper", "arxiv": "2212.04088", "year": 2022, "venue": null, "org": null, "url": null, "code": "https://github.com/OSU-NLP-Group/LLM-Planner", "project": "https://dki-lab.github.io/LLM-Planner", "topics": [ "learning" ], "tags": [ "llm", "planning", "few-shot" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Early use of LLMs as few-shot planners for embodied agents following natural-language instructions in visual environments.", "takeaway_zh": "较早把大模型用作具身智能体的少样本规划器，在视觉环境中执行自然语言指令。", "github_stars": 226, "page": "https://embodied-ai-radar.com/e/arxiv-2212.04088.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2602.19313", "title": "TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics", "type": "paper", "arxiv": "2602.19313", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/TOPReward/TOPReward", "project": null, "topics": [ "learning", "datasets" ], "tags": [ "reward-model", "vlm", "zero-shot", "progress-estimation", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Training-free progress reward: reads a video-language model's token probability that the instructed task is done, rather than asking it for a number. Introduces ManiRewardBench (130 tasks, four robots).", "takeaway_zh": "免训练的进度奖励：不让视频语言模型输出数值，而是读取其判断“任务已完成”的 token 概率作为稠密奖励。并提出 ManiRewardBench（130 个任务、4 种机器人）。", "github_stars": 120, "page": "https://embodied-ai-radar.com/e/arxiv-2602.19313.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2602.24121", "title": "Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation", "type": "paper", "arxiv": "2602.24121", "year": 2026, "venue": "CoRL 2026", "org": "University of Washington", "url": null, "code": "https://github.com/UWRobotLearning/mpail2", "project": "https://uwrobotlearning.github.io/mpail2/", "topics": [ "learning", "manipulation" ], "tags": [ "inverse-rl", "learning-from-observation", "world-model", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Inverse RL from observation only (videos, no actions or rewards) made practical on a real robot via online world modeling. Reports pick-and-place learned from scratch to 82% in under 40 minutes.", "takeaway_zh": "仅从观测（视频，无动作和奖励）做逆强化学习，借助在线世界模型在真机上实现。报告不到 40 分钟从零学会抓放，成功率 82%。", "github_stars": 21, "page": "https://embodied-ai-radar.com/e/arxiv-2602.24121.html", "labs": [ "uw" ], "code_license": "" }, { "id": "arxiv-2610.27194", "title": "Learning to Be Different: Component Specialization for Compositional Diffusion Policies", "type": "paper", "arxiv": "2610.27194", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Al3cL1/decorrelation_release", "project": "https://al3cl1.github.io/robotics-score-decorrelation", "topics": [ "learning", "manipulation" ], "tags": [ "diffusion-policy", "compositional", "multitask", "auxiliary-loss" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Composed diffusion policies often learn identical components. A drop-in score-decorrelation loss makes each factor specialize, with no loss in multitask performance.", "takeaway_zh": "组合式扩散策略的各个分量常常学成相同的副本。即插即用的得分去相关损失让各分量各自专门化，且不损失多任务性能。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2610.27194.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.17709", "title": "Where Small Mistakes Matter: Quantifying State Criticality for Imitation Learning", "type": "paper", "arxiv": "2610.17709", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://sia-lab-git.github.io/SCRIM/", "topics": [ "learning", "manipulation", "datasets" ], "tags": [ "imitation-learning", "state-criticality", "data-curation", "adversarial" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Scores how much a small action error at each demo state would hurt the outcome, using an adversarial disturbance policy. The scores reweight behavior cloning and select smaller, higher-value demo subsets.", "takeaway_zh": "用对抗扰动策略衡量每个示教状态上的小动作误差会对结果造成多大损害。该分数可用于给行为克隆加权，并挑选更小但价值更高的示教子集。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.17709.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.17422", "title": "MIRROR: Reflecting Hidden Symmetries from Offline Robot Interaction Data", "type": "paper", "arxiv": "2610.17422", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://pomdp-mirror.github.io", "topics": [ "learning", "manipulation" ], "tags": [ "symmetry", "equivariance", "offline-data", "behavior-cloning", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns how a known symmetry acts on pixel observations and actions from offline data, then uses it for frame-averaged behavior cloning. Reports average success 80.5% to 85.1% on four Robosuite tasks.", "takeaway_zh": "从离线交互数据中学习已知对称群如何同时作用于像素观测和动作，再用于帧平均行为克隆。报告在四个 Robosuite 任务上平均成功率从 80.5% 升到 85.1%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.17422.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.12465", "title": "A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control", "type": "paper", "arxiv": "2610.12465", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://sgs-rl.github.io/", "topics": [ "learning", "locomotion", "sim" ], "tags": [ "massively-parallel-rl", "curriculum", "assembly", "quadruped", "sim2real" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Success Guided Sampling focuses massively parallel RL on task configurations at the edge of the policy's ability. With up to a million envs it solves quadruped terrain and assembly tasks; assembly transfers zero-shot.", "takeaway_zh": "成功引导采样把大规模并行强化学习集中在策略能力边界附近的任务配置上。用最多约一百万个并行环境解决四足多地形与装配任务，装配策略零样本迁移到真机。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.12465.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.32236", "title": "RoboFFT: Finetuning generative robot policy via online reinforcement learning with forward process", "type": "paper", "arxiv": "2609.32236", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/shshsunny/RoboFFT", "project": "https://student-of-holmes.github.io/RoboFFT/", "topics": [ "learning" ], "tags": [ "rl-finetuning", "diffusion-policy", "flow-matching", "ppo", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "PPO-style fine-tuning for diffusion and flow policies that builds a surrogate policy ratio from forward noising and the score/flow loss, avoiding likelihoods. Tested in sim and real.", "takeaway_zh": "为扩散与流匹配策略做 PPO 式微调：用前向加噪与得分/流匹配损失构造替代的策略比率，绕开似然计算；在仿真与真机上验证。", "github_stars": 14, "page": "https://embodied-ai-radar.com/e/arxiv-2609.32236.html", "labs": [], "code_license": "" }, { "id": "arxiv-2609.27068", "title": "HiRE: Hindsight Reward Editing for Policy Finetuning", "type": "paper", "arxiv": "2609.27068", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/HiRE-Project/HiRE", "project": "https://hire-project.github.io", "topics": [ "learning" ], "tags": [ "reward-shaping", "rl-finetuning", "foundation-models", "hindsight" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Training-free reward editing: compares successful and failed rollouts in hindsight to penalize trap states that foundation-model rewards rate highly. Reports at least 3x the base policy's performance.", "takeaway_zh": "免训练的奖励修正：事后对比成功与失败轨迹，惩罚被基础模型奖励高估、最终却导致失败的“陷阱状态”。报告性能至少是基础策略的 3 倍。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2609.27068.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2608.26053", "title": "$R^3$: Training Robots to Reason in Natural Language via Reinforcement Learning", "type": "paper", "arxiv": "2608.26053", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://robotic-reasoner.github.io/", "topics": [ "learning" ], "tags": [ "reasoning", "vlm", "rl-finetuning", "language-table", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Post-training recipe that turns an off-the-shelf VLM into a free-form language reasoner guiding a low-level policy: mid-train on expert reasoning traces, then rubric-based RL from offline action data.", "takeaway_zh": "把现成 VLM 训练成用自由语言推理来指导底层策略的“机器人推理器”：先用专家推理轨迹做中训练，再用离线动作数据做基于评分规则的单步强化学习。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.26053.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.14551", "title": "TRACE: Trajectory-Routed Causal Memory for Delayed-Evidence Visuomotor Imitation", "type": "paper", "arxiv": "2606.14551", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Jeong-zju/corl-trace", "project": "https://jeong-zju.github.io/trace", "topics": [ "learning" ], "tags": [ "memory", "imitation-learning", "path-signatures", "long-horizon", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Bounded latent memory for imitation policies on tasks where a cue disappears before the decision; it is keyed by path signatures of the robot's own trajectory and attaches via adapters. Real-robot branching tasks.", "takeaway_zh": "为“线索在决策前已消失”的任务设计的模仿策略记忆：固定大小的潜在记忆以机器人自身轨迹的路径签名为键读写，通过适配器接入现有策略。在真机分支任务上验证。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2606.14551.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.16917", "title": "Unified Motion-Action Modeling for Heterogeneous Robot Learning", "type": "paper", "arxiv": "2606.16917", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://uma-manipulation.github.io/", "topics": [ "learning" ], "tags": [ "object-motion", "masked-generative", "human-video", "cross-embodiment", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Uses 3D object motion as a shared interface: object motion and robot actions are modelled jointly with masking, so one pretrained model serves as policy, dynamics model and few-shot adapter. Robot, human and sim data.", "takeaway_zh": "以 3D 物体运动轨迹作为统一接口，用掩码生成目标联合建模物体运动与机器人动作，同一套预训练参数可作为策略、动力学模型或少样本适配器。混合机器人、人类视频与仿真数据预训练。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.16917.html", "labs": [], "code_license": null }, { "id": "arxiv-2605.00416", "title": "Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies", "type": "paper", "arxiv": "2605.00416", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://learning-while-deploying.github.io/", "topics": [ "learning" ], "tags": [ "fleet-learning", "offline-to-online", "vla", "bimanual", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Fleet-scale offline-to-online RL that keeps post-training a generalist VLA from autonomous rollouts and human interventions. On 16 dual-arm robots and eight tasks it reports 95% average success.", "takeaway_zh": "车队规模的离线到在线强化学习：利用自主执行与人工干预数据持续后训练通用 VLA。在 16 台双臂机器人、八个任务上报告平均成功率 95%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2605.00416.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.26545", "title": "Memory Anchors for Continual Robot Learning", "type": "paper", "arxiv": "2608.26545", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://robot-adaptation.github.io/MemoryAnchors", "topics": [ "learning" ], "tags": [ "continual-learning", "replay", "forgetting", "libero", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Finds that a few replay samples, where new and old observations look alike but need different actions, prevent most forgetting. Dropping just 10% of them raises forgetting more than 4.5x on LIBERO.", "takeaway_zh": "发现回放缓冲中少数“观测相似但所需动作冲突”的样本（记忆锚点）承担了防遗忘的主要作用：仅剔除其中 10% 就会让 LIBERO 上的遗忘增加 4.5 倍以上。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.26545.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.12372", "title": "UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning", "type": "paper", "arxiv": "2606.12372", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Denghaoyuan123/UniIntervene", "project": "https://denghaoyuan123.github.io/UniIntervene-project/", "topics": [ "learning" ], "tags": [ "human-in-the-loop", "real-world-rl", "value-estimation", "recovery", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Agentic intervention model for human-in-the-loop RL: detects stalled exploration from value trends and steers the policy back to high-value states. Reports 57% fewer human interventions.", "takeaway_zh": "用于人在回路强化学习的智能干预模型：根据价值变化趋势发现无效探索，并自动把策略拉回高价值状态。报告人工干预减少 57%，成功率提升 8.6%。", "github_stars": 18, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12372.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2607.09866", "title": "Robo-ValueRL: Reliable Value Estimation for Offline-to-Online Reinforcement Learning", "type": "paper", "arxiv": "2607.09866", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Open-X-Humanoid/Robo-ValueRL", "project": "https://gewu-lab.github.io/Robo-ValueRL/", "topics": [ "learning" ], "tags": [ "offline-to-online", "value-function", "precision", "insertion", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Studies how value-function reliability drives offline-to-online RL for manipulation, with a history-conditioned value estimator. Reports 86% on millimeter-level chip insertion.", "takeaway_zh": "系统研究价值函数可靠性如何影响操作任务的离线到在线强化学习，提出历史条件价值估计器。报告毫米级芯片插装成功率 86%。", "github_stars": 44, "page": "https://embodied-ai-radar.com/e/arxiv-2607.09866.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.17174", "title": "Generative Flow on Symbolic Abstractions for Embodiment Capability Discovery", "type": "paper", "arxiv": "2610.17174", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://www.youwei-yu.com/fosa", "topics": [ "learning" ], "tags": [ "task-generation", "gflownet", "curriculum", "cross-embodiment", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Discovers what a given robot can actually learn: a GFlowNet builds symbolic task skeletons, rewarded by how well low-level policies learn them in sim, then the tasks are distilled into one language-conditioned policy.", "takeaway_zh": "自动发现某台机器人“学得会”的任务：用 GFlowNet 生成符号化任务骨架，以仿真中底层策略的学习效果作为奖励，再把发现的任务蒸馏进一个语言条件策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.17174.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.19980", "title": "ENPIRE: Agentic Robot Policy Self-Improvement in the Real World", "type": "paper", "arxiv": "2606.19980", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/NVlabs/ENPIRE", "project": "https://research.nvidia.com/labs/gear/enpire/", "topics": [ "learning" ], "tags": [ "coding-agents", "autonomous-research", "real-world", "dexterous", "nvidia" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Harness that lets coding agents improve real robot policies on their own: automatic reset and verification, parallel rollouts and agent-driven code changes. Reports 99% success on tasks like fastening a zip tie.", "takeaway_zh": "让编程智能体自主改进真实机器人策略的框架：自动复位与结果验证、多机并行评测，并由智能体分析日志、修改训练代码。报告在扎扎带等灵巧任务上成功率 99%。", "github_stars": 351, "page": "https://embodied-ai-radar.com/e/arxiv-2606.19980.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2604.25050", "title": "DiscreteRTC: Discrete Diffusion Policies are Natural Asynchronous Executors", "type": "paper", "arxiv": "2604.25050", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://outsider86.github.io/DiscreteRTCSite/", "topics": [ "learning" ], "tags": [ "discrete-diffusion", "real-time-chunking", "asynchronous", "dynamic-tasks", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Shows discrete diffusion policies do real-time chunking natively, since inpainting is just unmasking: no fine-tuning, ~0.7x compute. Reports 65% higher success than flow-matching RTC on a real hockey task.", "takeaway_zh": "指出离散扩散策略天然支持实时分块异步执行（补全即解掩码），无需微调、计算量约为 0.7 倍。报告在真机冰球防守任务上成功率比流匹配 RTC 高 65%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.25050.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.01111", "title": "FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement", "type": "paper", "arxiv": "2607.01111", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Hoar012/FAR", "project": "https://hoar012.github.io/FAR-Project", "topics": [ "learning" ], "tags": [ "failure-recovery", "preference-learning", "diffusion-policy", "continual-learning", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Retries that learn from failure: preference pairs from failed attempts steer the policy away from them, plus small action perturbations, and feeds recoveries back into training. Reports +17.6% sim, +11.7% real.", "takeaway_zh": "会从失败中学习的重试机制：用失败尝试构造偏好数据，引导策略避开失败行为，并加入小幅动作扰动做局部探索，成功恢复的轨迹再回流训练。报告仿真提升 17.6%，真机提升 11.7%。", "github_stars": 14, "page": "https://embodied-ai-radar.com/e/arxiv-2607.01111.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.12604", "title": "EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations", "type": "paper", "arxiv": "2606.12604", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/GaTech-RL2/EgoEngine-CoRL2026", "project": "https://egoengine.github.io", "topics": [ "learning" ], "tags": [ "human-video", "egocentric", "data-generation", "dexterous", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Converts egocentric human videos into robot data: a video with the human replaced by a robot plus a feasible robot action trajectory. Claims the first zero-shot dexterous visuomotor policy with no real-robot demos.", "takeaway_zh": "把第一人称人类视频转为机器人数据：生成把人替换成机器人的视频，以及满足可行性约束的机器人动作轨迹。声称首次在无真机演示下零样本训练出灵巧视觉运动策略。", "github_stars": 12, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12604.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.13403", "title": "Robot-WATCHDOG : failure detection through object-centric graph representation", "type": "paper", "arxiv": "2610.13403", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://cea-list.github.io/Robot-WATCHDOG/", "topics": [ "learning" ], "tags": [ "failure-detection", "scene-graphs", "self-supervised", "monitoring", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Self-supervised failure detection from nominal demos only, using object-centric spatio-temporal scene graphs; both of its scores beat image-based detectors, especially on semantic and interaction failures.", "takeaway_zh": "仅用正常演示做自监督失败检测：把操作场景建模为以物体为中心的时空图，所提两种失败评分都优于基于图像的检测器，尤其擅长语义和交互层面的失败。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.13403.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.16153", "title": "Unified Condition-Action Modeling for Accurate One-Step Action Generation", "type": "paper", "arxiv": "2608.16153", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/NTUMARS/UCA-Policy", "project": "https://uca-policy.github.io/UCA.github.io/", "topics": [ "learning" ], "tags": [ "one-step", "flow-policy", "3d", "fast-inference", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "One-step flow policy that puts observation, timestep and action tokens in one sequence for a single transformer. Reports 86.4% average success (+7.5 points) at 3.0 ms latency, 43.9x faster than DP3.", "takeaway_zh": "一步生成的流策略：把观测、时间步与动作 token 拼成一个序列，由同一 Transformer 联合建模。报告平均成功率 86.4%（提升 7.5 个百分点），推理延迟 3.0 ms，比 DP3 快 43.9 倍。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2608.16153.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.18328", "title": "Recover, Discover, Plan: Learning Skills and Concepts from Robot Failures", "type": "paper", "arxiv": "2606.18328", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Jaraxxus-Me/ReSYNC", "project": "https://jaraxxus-me.github.io/ReSYNC/", "topics": [ "learning" ], "tags": [ "failure-recovery", "abstraction", "task-planning", "predicates", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns recovery skills with RL and, from them, discovers new relational predicates for an abstract planner, turning local recoveries into failure avoidance. Reports >50% gains over baselines in four sim domains.", "takeaway_zh": "用强化学习学习失败恢复技能，并据此发现新的关系谓词、完善抽象规划模型，把局部恢复转化为全局避免失败。报告在四个仿真领域比基线高 50% 以上，并迁移到真机。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2606.18328.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.12365", "title": "Ambient Diffusion Policy: Imitation Learning from Suboptimal Data in Robotics", "type": "paper", "arxiv": "2606.12365", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://ambient-diffusion-policy.github.io/", "topics": [ "learning" ], "tags": [ "diffusion-policy", "suboptimal-data", "co-training", "open-x-embodiment", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Uses suboptimal demos only at high and low diffusion noise levels, where their coarse or local structure still helps. Covers noisy, sim, mismatched and mixed data; reports up to 33% over co-training on Open X-Embodiment.", "takeaway_zh": "只在扩散过程的高噪声与低噪声阶段使用次优演示，此时其粗粒度或局部结构仍然有用。覆盖带噪、仿真、任务不匹配和混合数据；报告在 Open X-Embodiment 上比协同训练基线高最多 33%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12365.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.28320", "title": "WARP-RM: A Warp-Augmented Relative Progress Reward Model for Data Curation", "type": "paper", "arxiv": "2606.28320", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/uynitsuj/WARP-RM", "project": "https://uynitsuj.github.io/warp-rm/", "topics": [ "learning" ], "tags": [ "data-curation", "progress-reward", "self-supervised", "cloth-folding", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Self-supervised progress reward learned from time-warped demos (speed changes, reversals), used to filter and reweight behaviour-cloning data. Reports up to ~18x T-shirt folding throughput over vanilla BC.", "takeaway_zh": "用时间扭曲（变速、倒放）增强的演示自监督学习帧级进度奖励，再用它过滤和加权行为克隆数据。报告 T 恤折叠吞吐量最高为普通 BC 的约 18 倍。", "github_stars": 67, "page": "https://embodied-ai-radar.com/e/arxiv-2606.28320.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2510.27114", "title": "Learning Robust Visuomotor Policies via Dynamics Alignment", "type": "paper", "arxiv": "2510.27114", "year": 2025, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://dynamics-aligned-policy.github.io", "topics": [ "learning" ], "tags": [ "flow-matching", "dynamics-model", "imitation-learning", "robustness", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "A flow-matching policy and an action-conditioned dynamics model co-sample an action and its predicted future until they agree. Reports 56.5% (sim) and 33.3% (real) relative gains over the strongest baselines.", "takeaway_zh": "流匹配策略与动作条件动力学模型联合采样动作及其未来观测，相互修正直到二者自洽。报告相对最强基线在仿真提升 56.5%、真机提升 33.3%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2510.27114.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.29940", "title": "WARP: Whole-Body Retargeting for Learning from Offline Human Demonstrations", "type": "paper", "arxiv": "2606.29940", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://warp-retargeting.github.io/", "topics": [ "learning" ], "tags": [ "retargeting", "human-demos", "mobile-manipulation", "whole-body", "real" ], "robot_types": [ "arm", "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Retargets human whole-body demos to a mobile manipulator offline with a closed-form shoulder-elbow-wrist solver and lazy base control; claims the first zero-shot whole-body mobile manipulation from them.", "takeaway_zh": "离线把人类全身演示重定向到移动操作机器人：闭式肩-肘-腕几何求解器加“惰性”底盘控制，得到一致的动作；声称首次仅凭离线人类演示实现零样本全身移动操作。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.29940.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.21672", "title": "Imitation from Heterogeneous Demonstrations using Grounded Latent-Action World Models", "type": "paper", "arxiv": "2606.21672", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/ViccccciV/GLAM_Code", "project": "https://viccccciv.github.io/glam/", "topics": [ "learning" ], "tags": [ "latent-actions", "world-model", "heterogeneous-data", "behavior-cloning", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Shared latent action space grounded by predicting the same future across data sources, so action-free and differently-labelled data can train one BC policy. Reports +48% average success on five tasks.", "takeaway_zh": "通过让不同数据源对未来观测的预测保持一致来“落地”一个共享潜在动作空间，使无动作标签和动作空间不同的数据都能训练同一个行为克隆策略。报告五个任务平均成功率提升 48%。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2606.21672.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.11628", "title": "LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition", "type": "paper", "arxiv": "2606.11628", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://lucid-robot.github.io/", "topics": [ "learning" ], "tags": [ "human-video", "intent-model", "sim-training", "cross-embodiment", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns a short-horizon 'what should happen next' intent model from internet human videos and pairs it with embodiment-specific policies trained in parallel sim; the same intent model drives a hand and a gripper.", "takeaway_zh": "从互联网人类视频学习预测“场景接下来该发生什么”的短时意图模型，再与在大规模并行仿真中训练的本体专用策略结合；同一意图模型可驱动灵巧手和平行夹爪。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.11628.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.06194", "title": "ActiveMimic: Egocentric Video Pretraining with Active Perception", "type": "paper", "arxiv": "2606.06194", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/leolin9248/activemimic-data", "project": "https://activemimic.github.io/", "topics": [ "learning" ], "tags": [ "egocentric-video", "active-perception", "pretraining", "camera-motion", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Treats head-camera motion in egocentric human video as a viewpoint action and pretrains active perception plus manipulation from it. Reports matching state-of-the-art models pretrained on robot data.", "takeaway_zh": "把第一人称人类视频中的头部相机运动视为“视角动作”，同时预训练主动感知与操作能力。报告效果与使用机器人数据预训练的最先进模型相当。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2606.06194.html", "labs": [], "code_license": "" }, { "id": "arxiv-2606.08154", "title": "SynthICL: Scalable In-context Imitation Learning with Synthetic Data", "type": "paper", "arxiv": "2606.08154", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/sNiper-Qian/synthicl", "project": "https://synth-icl.github.io", "topics": [ "learning" ], "tags": [ "in-context-learning", "synthetic-data", "flow-matching", "one-shot", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "In-context imitation policy trained only on RGB synthetic data, with next-subgoal image prediction. Reports 79% average success on 16 unseen real tasks from a single test-time demo.", "takeaway_zh": "完全用 RGB 合成数据训练的上下文模仿学习策略，并预测下一个子目标图像。报告仅给一条演示时，在 16 个未见真机任务上平均成功率 79%。", "github_stars": 11, "page": "https://embodied-ai-radar.com/e/arxiv-2606.08154.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.32027", "title": "Freeform Preference Learning for Robotic Manipulation", "type": "paper", "arxiv": "2606.32027", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://freeform-pl.github.io/fpl.website/", "topics": [ "learning" ], "tags": [ "preference-learning", "reward-model", "language", "long-horizon", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Annotators define their own preference axes in language (speed, safety, care) and compare trajectories per axis; a language-conditioned reward trains a steerable policy. Reports +38 points over sparse and binary rewards.", "takeaway_zh": "让标注者用自然语言自定义偏好维度（速度、安全、细致等）并逐维比较轨迹，训练语言条件奖励模型和可引导的策略。报告比稀疏奖励与二元偏好方法高 38 个百分点。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.32027.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.22303", "title": "FlowDPG: Deterministic Policy Gradient on Flow Matching Policies for Real-World Manipulation", "type": "paper", "arxiv": "2606.22303", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://flowdpg.github.io", "topics": [ "learning" ], "tags": [ "real-world-rl", "flow-matching", "ddpg", "assembly", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "DDPG-style RL for flow-matching policies without backprop through the ODE: the critic gradient at a one-step action estimate is distilled into the velocity field. Reports 92% on dual-arm AirPods assembly.", "takeaway_zh": "无需沿 ODE 反向传播的流匹配策略 DDPG 式强化学习：在一步估计的动作上计算 critic 梯度，并蒸馏进速度场。报告双臂 AirPods 装配成功率 92%，炒蛋任务评分 86%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.22303.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.06009", "title": "How to Learn from What a Human Would Avoid? Intervention-Aware World Models with Real-World RL for Dexterous Manipulation", "type": "paper", "arxiv": "2609.06009", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://whirl-dexterous.github.io/", "topics": [ "learning" ], "tags": [ "real-world-rl", "human-in-the-loop", "world-model", "dexterous", "leap-hand" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Human-in-the-loop RL whose latent world model also predicts when the operator would take over, steering the policy away from risky states. On a 16-DoF LEAP Hand: 96.7% grasp success, up to 84% less intervention.", "takeaway_zh": "人在回路强化学习：潜在世界模型额外预测操作者何时会接管，并据此让策略远离高风险状态。在 16 自由度 LEAP 手上抓取成功率 96.7%，人工干预最多减少 84%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.06009.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.25337", "title": "AI Coaching for Accelerating Human Skill Development with Reinforcement Learning", "type": "paper", "arxiv": "2606.25337", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "human-skill", "coaching", "shared-control", "user-study", "drone" ], "robot_types": [ "aerial" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "RL-trained AI coach that steps back as the learner improves, modelled as a game between learner and coach. A 33-person FPV drone-racing study shows faster human skill gains than prior AI coaching.", "takeaway_zh": "用强化学习训练的 AI 教练：随学习者进步逐步减少辅助，把教练与学习者建模为非合作动态博弈。33 人第一视角无人机竞速研究显示，人类技能提升快于已有 AI 教练方法。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.25337.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.08610", "title": "HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning", "type": "paper", "arxiv": "2606.08610", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "agents", "rl-automation", "reward-design", "sim-to-real", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Agent harness that automates sim RL from environment setup to trained policy: staged specialist agents, executable gates, parallel trials and reusable knowledge. 16 tasks across 6 benchmarks, some moved to real robots.", "takeaway_zh": "自动化仿真强化学习全流程的智能体框架：从环境搭建、奖励设计到调参训练，由分阶段的专职智能体、可执行检查点、并行试验与经验复用完成。覆盖 6 个基准 16 个任务，部分迁移到真机。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.08610.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.08408", "title": "Localized Visual Feature Aggregation via Focus Pooling for Visuomotor Policies", "type": "paper", "arxiv": "2609.08408", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "visual-representation", "attention-pooling", "data-efficiency", "cnn", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Attention pooling over intermediate CNN features, weighted by the robot's current proprioception, to keep control-relevant local detail. Reports +36.2% and +41.2% over pooling and explicit-crop methods.", "takeaway_zh": "以机器人当前本体状态为查询，对 CNN 中间特征做注意力池化，保留与控制相关的局部信息。报告比常规池化与显式裁剪方法分别高 36.2% 和 41.2%，仅需训练 5.8% 的编码器参数。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.08408.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.24576", "title": "Chameleon: Control-Indexed Prospective Memory for Visuomotor Manipulation", "type": "paper", "arxiv": "2603.24576", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "memory", "visuomotor-policy", "benchmark", "libero", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "~60M-parameter policy with event memory for tasks where the deciding cue comes long before the action, plus the real-robot Camo-Dataset. Reports decision success up from 22.5% to 80.8% and 87.1% on LIBERO-10.", "takeaway_zh": "约 6000 万参数的视觉运动策略，带事件记忆，面向“决定性线索早于动作很久出现”的任务，并发布真机基准 Camo-Dataset。报告决策成功率从 22.5% 提升到 80.8%，LIBERO-10 达 87.1%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.24576.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.24155", "title": "Object-Centric Conditioning for Visuomotor Flow Matching", "type": "paper", "arxiv": "2609.24155", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "flow-matching", "object-centric", "robustness", "distractors", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Action-to-action flow-matching policy conditioned on object-centric 'what' features and image-plane 'where' cues, improving robustness to distractors and object shifts while keeping few-step inference.", "takeaway_zh": "以物体为中心的动作到动作流匹配策略：把观测拆成语义“是什么”特征与图像平面“在哪里”线索作为条件，在保持少步推理的同时提升对干扰物和位置偏移的鲁棒性。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.24155.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.03949", "title": "Preference-Calibrated Human-in-the-Loop Reinforcement Learning for Robotic Manipulation", "type": "paper", "arxiv": "2606.03949", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "human-in-the-loop", "credit-assignment", "preferences", "actor-critic", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Human-in-the-loop RL that treats each intervention as a preference (human action over policy action) and corrects credit on suboptimal segments. Reports +24.5% average success and 1.3x faster convergence.", "takeaway_zh": "人在回路强化学习：把每次人工干预视为偏好（人类动作优于策略动作），对次优片段做信用重新分配。报告五个真机任务平均成功率提升 24.5%，收敛快 1.3 倍。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.03949.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.03340", "title": "Learning Additively Compositional Latent Actions for Embodied AI", "type": "paper", "arxiv": "2604.03340", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "latent-actions", "video", "compositionality", "pretraining", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Latent action model constrained to compose additively over short horizons (identity, inverse, cycle), so latents capture motion rather than scene detail and give better supervision for policies.", "takeaway_zh": "对潜在动作空间施加短时可加组合约束（恒等、逆、循环一致），使潜在动作只编码运动而非场景细节，并为下游策略学习提供更好的监督。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.03340.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.05677", "title": "Learning Coordinated Visuomotor Box-Pushing from Solo Demonstrations", "type": "paper", "arxiv": "2610.05677", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "multi-robot", "imitation-learning", "decentralized", "box-pushing", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Studies whether single-robot teleop data can train decentralised coordinated box-pushing; tests with up to 40 robots show coordination is hard to learn just by watching other robots.", "takeaway_zh": "研究能否只用单机遥操作数据训练去中心化的多机协同推箱；多达 40 台机器人的实验表明，仅靠被动观察其他机器人很难学会协调。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.05677.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.20820", "title": "Workspace Models: Lightweight Robotic Memory via Saliency-Driven Supervision", "type": "paper", "arxiv": "2609.20820", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "memory", "distillation", "vlm", "long-horizon", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Uses a VLM only at training time to pick task-relevant current and past information, distilled into a 'workspace token' the policy reads at deployment. Beats conditioning on curated past frames.", "takeaway_zh": "仅在训练时用 VLM 挑出完成任务所需的当前与历史信息，蒸馏成策略部署时读取的“工作区 token”。效果优于直接输入精选历史帧。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.20820.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.22027", "title": "RARM: Confidence-Gated Progress Reward Modeling for RL in Manipulation", "type": "paper", "arxiv": "2606.22027", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "reward-model", "progress", "one-demo", "cloth-folding", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Video comparator trained once on general videos that turns one successful demo into a dense progress reward, paying only for confident forward progress. Best RL success on 9 sim and 4 real tasks.", "takeaway_zh": "只在通用视频上训练一次的视觉比较器，可把一条成功演示变成稠密的进度奖励，并只奖励置信度高的前进。在 9 个仿真和 4 个真机任务的强化学习中成功率最高。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.22027.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.22285", "title": "Asking Smarter Questions: Targeted Active Preference Learning for Underspecified Rewards", "type": "paper", "arxiv": "2610.22285", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "reward-learning", "active-learning", "preferences", "explanations", "user-study" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "When teaching data under-specifies some reward features, the robot targets preference queries at those features and explains what to look at. Up to 30.8% better than standard active preference learning with few queries.", "takeaway_zh": "当示教数据对某些奖励特征监督不足时，机器人把偏好询问集中在这些特征上，并附带解释提示人该关注什么。少量询问下比标准主动偏好学习高最多 30.8%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.22285.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.13982", "title": "Rethinking the Implications of Human Feedback for Preference Learning in Human-Robot Collaboration", "type": "paper", "arxiv": "2609.13982", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "preference-learning", "human-feedback", "hri", "reward-learning", "user-study" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Shows fixed rules for deriving labels from human feedback often disagree with what people mean, and learns these implications instead. Better preference estimates, incl. a real pizza-making study.", "takeaway_zh": "发现从人类反馈推导未选动作标签的固定规则常与人的真实意图不符，改为学习这种“隐含含义”。偏好估计更准，并在真实做披萨协作实验中验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.13982.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.07527", "title": "Task-Space Imitation Guidance for Efficient Reinforcement Learning", "type": "paper", "arxiv": "2610.07527", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "rl", "imitation-guidance", "dense-rewards", "safety", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Uses an imitation policy's predicted action chunks as short task-space references for dense progress rewards in sparse-reward RL, plus guided pretraining. Better early sample efficiency and fewer safety violations.", "takeaway_zh": "把模仿策略预测的动作块转成短时末端参考轨迹，为稀疏奖励强化学习提供稠密进度奖励，并用于引导预训练。提升早期样本效率、减少安全违规。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.07527.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.07275", "title": "Efficient Diffusion Policy Fine Tuning with Latent Noise Representation Bridging", "type": "paper", "arxiv": "2611.07275", "year": 2026, "venue": "NeurIPS 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "diffusion-policy", "rl-finetuning", "noise-space", "theory", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "LARBRID fine-tunes a pre-trained diffusion/flow policy via its initial noise, with a noise/action shared representation that fixes 'noise aliasing'. Reports up to 2-3x gains on locomotion and manipulation tasks.", "takeaway_zh": "LARBRID 通过调整初始噪声来强化学习微调预训练的扩散/流策略，并学习噪声空间与动作空间共享的表示来消除“噪声混叠”问题。在运动与操作任务上报告最高 2–3 倍的提升。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.07275.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.11525", "title": "Learning Object Manipulation from Scratch via Contrastive Interaction", "type": "paper", "arxiv": "2606.11525", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "contrastive-rl", "goal-conditioned", "sim2real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Contrastive RL struggles when contact changes the dynamics mode. Interaction-weighted Resampling samples around before/during/after contact, reporting a 19.8% average gain in sim, including robot air hockey.", "takeaway_zh": "接触会改变动力学模式，使对比式强化学习难以处理操作任务。交互加权重采样在接触前、中、后阶段重点采样，仿真中平均提升 19.8%，任务包括机器人空气曲棍球。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.11525.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.13675", "title": "Improving Robotic Generalist Policies via Flow Reversal Steering", "type": "paper", "arxiv": "2606.13675", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "flow-matching", "steering", "generalist-policy", "rl", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Runs rough actions (from a human or a VLM) backwards through a flow policy to their noise, then forward again to land on a nearby good action mode. Distilling this reports up to 95-point success gains in under a minute.", "takeaway_zh": "把粗糙动作（来自人或 VLM 的指引）反向送入流策略得到其噪声，再正向生成，落到附近的优质动作模态。将其蒸馏后，不到一分钟训练即可报告最高 95 个百分点的成功率提升。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.13675.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.08075", "title": "Leave It Better Than Found: Value-Guided Cooperation in Sequential Tasks", "type": "paper", "arxiv": "2611.08075", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "multi-robot", "value-functions", "ad-hoc-cooperation", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Robots that never trained together share value functions, so a robot finishing a task leaves the workspace in a state the next robot prefers; a KNN uncertainty filter blocks overconfident values. Up to +48.5 pts.", "takeaway_zh": "从未共同训练的机器人通过共享价值函数来协作，前一个机器人会把工作空间留在后一个机器人偏好的状态；KNN 不确定性过滤避免分布外的过度自信。成功率最高提升 48.5 个百分点。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08075.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.09148", "title": "Proxy Policy Steering", "type": "paper", "arxiv": "2609.09148", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "steering", "diffusion-policy", "adaptation", "generalist-policy" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Adapts a frozen generalist policy at inference: two small proxies (one copies base behaviour, one is fine-tuned on the task) and their velocity difference steers each denoising step. Base weights are not needed.", "takeaway_zh": "推理时适配冻结的通用策略：训练两个小代理策略（一个模仿基座行为，一个在目标任务上微调），以二者的速度场差值引导每一步去噪。无需访问基座参数。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.09148.html", "labs": [], "code_license": null }, { "id": "arxiv-2505.18083", "title": "What Do You Need for Compositional Generalization in Diffusion Planning?", "type": "paper", "arxiv": "2505.18083", "year": 2025, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "diffusion-planner", "stitching", "compositional-generalization" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Finds that shift equivariance, local receptive fields and inference choices let diffusion planners stitch sub-trajectories without TD learning; locality matters most. Proposes the Eq-Net architecture.", "takeaway_zh": "发现平移等变性、局部感受野和推理设置使扩散规划器无需 TD 学习就能拼接子轨迹，其中局部性最关键；据此提出 Eq-Net 架构。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2505.18083.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.22630", "title": "Simultaneous Forward and Inverse Human-in-the-Loop Optimization", "type": "paper", "arxiv": "2609.22630", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "human-in-the-loop", "preference-learning", "assistive", "reward-learning" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "For assistive devices with costly user trials: a forward model predicts user outcomes from control policies and drives active queries to learn each user's reward, then picks the final policy without more trials.", "takeaway_zh": "面向用户试验代价高的辅助设备：先训练前向模型从控制策略预测用户结果，用它主动选择查询来学习个体奖励，再在不增加试验的情况下选出最终策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.22630.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.24633", "title": "Beyond Monotonic Progress: Retry-Supervised Value Learning for Robot Imitation", "type": "paper", "arxiv": "2606.24633", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "value-learning", "imitation-learning", "mixed-quality-data", "retries" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "ReTVL learns a mistake-sensitive value function from imperfect demos, using sparse retry keypoints as pairwise preferences on top of global progress, then reweights demo chunks for behavior cloning.", "takeaway_zh": "ReTVL 从不完美示教中学习对失误敏感的价值函数：在全局进度校准之上，以稀疏标注的“重试”关键点构造局部成对偏好，再用该价值为行为克隆的示教片段加权。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.24633.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.19425", "title": "SCAPE: Scenario-Conditioned Simulation-Augmented Policy Evaluation", "type": "paper", "arxiv": "2608.19425", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "policy-evaluation", "sim2real", "conformal-prediction", "quadruped" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "SCAPE predicts per-scenario real-world policy performance from a few paired sim/real runs plus many sim runs, correcting sim bias and calibrating with conformal prediction. Tested on driving and quadrupeds.", "takeaway_zh": "SCAPE 利用少量成对的仿真/真机数据和大量仿真推演，按场景预测策略在真实世界的表现：先校正仿真标签偏差，再用共形预测校准不确定性。在自动驾驶与四足速度跟踪上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.19425.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.12200", "title": "Implicit Neural Representations of Individual Behavior", "type": "paper", "arxiv": "2606.12200", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "policy-representation", "implicit-neural-representation", "self-supervised" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Behavioral INR represents a policy as a state-to-action function modulated by an episode latent (FiLM), so policy identity in unlabeled multi-policy data can be inferred without labels.", "takeaway_zh": "Behavioral INR 把策略表示为由回合级潜变量（FiLM）调制的“状态→动作”函数，从而在无标签的多策略行为数据中无监督地推断策略身份。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.12200.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.02547", "title": "Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control?", "type": "paper", "arxiv": "2608.02547", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "action-chunking", "behavior-cloning", "analysis" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Tests why action chunking helps BC: not temporal consistency or horizon reduction, but non-Markovian expressivity and less compounding error (also matched by delayed policies), plus an 'implicit ensembling' effect.", "takeaway_zh": "检验动作分块为何有助于行为克隆：并非时间一致性或缩短时域，而是非马尔可夫表达力与更少的误差累积（延迟策略也能做到），另外还有一种“隐式集成”效应。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.02547.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.13719", "title": "Coverage Aware Active Evaluation for Failure Discovery with Paired Systems", "type": "paper", "arxiv": "2608.13719", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "failure-discovery", "active-testing", "proxy", "sim2real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Finds real-system failures on a small budget by correcting cheap proxy (e.g. simulator) failure signals with residual modeling and choosing diverse, well-supported scenarios. Up to 2x the failures of random sampling.", "takeaway_zh": "在有限测试预算下寻找真实系统的失败：用残差建模校正廉价代理（如仿真器）的失败信号，并挑选多样且有数据支撑的场景。发现的失败数最多为随机采样的 2 倍。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.13719.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.18699", "title": "RoboFQE: Rollout-Free Evaluation of Visual Robot Policies from Offline Trajectories", "type": "paper", "arxiv": "2610.18699", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "policy-evaluation", "off-policy-evaluation", "fqe", "offline" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "RoboFQE scores visual manipulation policies from offline trajectories, no rollouts: fitted Q-evaluation on a pretrained vision encoder plus learned success labels. Keeps rankings where video world models drift.", "takeaway_zh": "RoboFQE 无需推演、直接用离线轨迹评估视觉操作策略：在预训练视觉编码器上做拟合 Q 评估并学习终止成功标注。在视频世界模型会累积误差的长时任务上仍能保持策略排序。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.18699.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.14603", "title": "AnyRank: Reliable Anytime Policy Ranking via Sequential Testing", "type": "paper", "arxiv": "2610.14603", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "policy-evaluation", "sequential-testing", "statistics", "hardware-trials" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "AnyRank ranks robot policies with sequential tests: trials go to hard-to-separate pairs, a (partial) ranking holds at any stopping time, and imperfect priors (sim, world model) save trials safely.", "takeaway_zh": "AnyRank 用序贯检验为多个机器人策略排序：把试验分给难以区分的策略，任何时刻停止都能给出有误差保证的（部分）排序，可借助不完美的先验（仿真、世界模型）节省试验而不破坏保证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.14603.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.17493", "title": "When Robots Sleep: Offline Skill Consolidation for Shared-Policy Robot Learning", "type": "paper", "arxiv": "2606.17493", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "continual-learning", "skill-consolidation", "multi-task", "meta-world" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Sleeping Robots: learn a new skill while 'awake', then consolidate one shared policy offline during 'sleep' from frozen critics or actor snapshots, combining gradients by Nash bargaining. +64% success on Meta-World MT5.", "takeaway_zh": "Sleeping Robots：“清醒”时学习新技能，“睡眠”时利用冻结的评估器或策略快照离线巩固单一共享策略，并以纳什议价合并梯度。在 Meta-World MT5 上平均成功率提升 64%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.17493.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.17408", "title": "Where Should Action Generation Begin? A Learnable Source Prior for Generative Robot Policies", "type": "paper", "arxiv": "2606.17408", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "generative-policy", "source-distribution", "flow-matching", "robotwin" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "LeaP replaces the standard Gaussian source of a flow/diffusion-bridge policy with a proprioception-conditioned Gaussian over action chunks from a small MLP. 81.6% average on 15 RoboTwin tasks.", "takeaway_zh": "LeaP 用一个小型 MLP 预测、以本体感觉为条件的动作块高斯分布，替换流匹配/扩散桥策略的标准高斯起点。在 15 个 RoboTwin 任务上平均成功率 81.6%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.17408.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.08743", "title": "Guided Discovery of New Behaviors using Diffusion Policies", "type": "paper", "arxiv": "2606.08743", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "diffusion-policy", "exploration", "trajectory-optimization", "rare-modes" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Feynman-Kac correctors with a new guiding potential push diffusion-policy samples toward rare but promising modes; trajectory optimization repairs them and they are added back to training.", "takeaway_zh": "用 Feynman-Kac 校正器配合新的引导势能，把扩散策略的采样推向少见但有前景的模态；再以基于采样的轨迹优化修复这些轨迹，并加回训练集重训策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.08743.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.20056", "title": "MAGMA-GEN: Validated Recovery Supervision from Ambiguous Failures via Counterfactual Re-Execution", "type": "paper", "arxiv": "2609.20056", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "failure-recovery", "data-generation", "long-horizon", "hierarchical" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Turns ambiguous failed rollouts into recovery training data: a privileged coach proposes a corrected high-level decision, kept only if re-executing from the same state improves progress.", "takeaway_zh": "把含义不明的失败推演转化为恢复训练数据：由特权教练推测早期的高层决策错误并提出修正，只有从同一状态重新执行确实提升进度时才保留。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.20056.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.29201", "title": "Behavior Uncloning: Distilling Mode Redirection into Policy Weights without Inference-Time Steering", "type": "paper", "arxiv": "2606.29201", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "behavior-cloning", "mode-suppression", "safety", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "MoRE 'unclones' unwanted behavior modes (e.g. handing a knife blade-first) by distilling a temporary mode classifier into the policy weights, with a retain loss. +44 pts success across eight tasks, no inference cost.", "takeaway_zh": "MoRE 通过把临时模式分类器的重定向信号蒸馏进策略权重，并配合保持损失，“去克隆”不想要的行为模式（如刀刃朝前递刀）。八个任务平均成功率提升 44 个百分点，推理零开销。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.29201.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.06262", "title": "Optimal Transport Q-Learning for Flow Policy Steering and Acceleration", "type": "paper", "arxiv": "2607.06262", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "flow-policy", "rl-finetuning", "optimal-transport", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "OTQL post-trains and speeds up a suboptimal flow policy from the robot's own experience with advantage-weighted conditional optimal-transport flow matching, in 50-60 episodes and without distillation.", "takeaway_zh": "OTQL 用优势加权的条件最优传输流匹配，从机器人自身经验对次优流策略做强化学习后训练并加速推理，只需 50–60 个交互回合，无需蒸馏。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.06262.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.15956", "title": "ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors", "type": "paper", "arxiv": "2603.15956", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "sim2real", "diffusion-policy", "noise-space-rl", "data-generation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "ExpertGen trains a diffusion prior on imperfect demos (even LLM-synthesized), then RL steers its initial noise with the policy frozen, keeping behaviour human-like under sparse rewards; experts feed sim-to-real BC.", "takeaway_zh": "ExpertGen 先在不完美示教（甚至由大模型合成）上训练扩散先验，再冻结策略、用强化学习只优化初始噪声，在稀疏奖励下保持类人行为；得到的专家策略用于仿真到真实的行为克隆。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.15956.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.36171", "title": "SkillWeaver: Agentic Exploration over Neural Interaction Skills for Scalable Robot Data Generation", "type": "paper", "arxiv": "2609.36171", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "data-generation", "vlm-agent", "skills", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "A VLM agent explores simulated tasks by calling reusable RL-trained interaction skills, verifying outcomes and keeping memory in a verifier-guided tree search, to generate robot data without scripts.", "takeaway_zh": "VLM 智能体在仿真中调用可复用的强化学习交互技能来探索任务，借助验证、反思与记忆进行验证器引导的树搜索，无需脚本即可生成机器人数据。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.36171.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.05368", "title": "GUILD: Guided Iterative Data Collection for Imitation Learning", "type": "paper", "arxiv": "2611.05368", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "data-collection", "imitation-learning", "active-learning", "vlm" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "GUILD decides what to collect next: a VLM proposes environments, object setups and goals, collected data is evaluated for its value to the policy, and the loop repeats, instead of filtering after the fact.", "takeaway_zh": "GUILD 主动决定下一步采集什么数据：由 VLM 提出环境、物体布置与目标，采集后评估数据对策略的价值，再进入下一轮，而非事后筛选数据集。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05368.html", "labs": [], "code_license": null }, { "id": "arxiv-2605.26478", "title": "Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient", "type": "paper", "arxiv": "2605.26478", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "visual-rl", "policy-gradient", "efficiency", "sim2real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "SDPG estimates policy gradients from random perturbations of rollouts, so visual RL needs far fewer batch-rendered envs and trains in hours on one RTX 4080; adds visual benchmarks and sim-to-real demos.", "takeaway_zh": "SDPG 通过对推演轨迹随机扰动来估计策略梯度，视觉强化学习所需的批量渲染环境大幅减少，单张 RTX 4080 几小时即可训练；并提供视觉机器人基准与仿真到真实演示。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2605.26478.html", "labs": [], "code_license": null }, { "id": "arxiv-2602.08776", "title": "Mind the Gap: Rethinking I/O Design for Contact-Rich Visuomotor Policy Learning", "type": "paper", "arxiv": "2602.08776", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "imitation-learning", "action-space", "teleoperation", "contact-rich" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Argues policies should condition on both the executed robot state and the leader command while predicting future commands, since their mismatch signals contact; beats either alone with no force sensing.", "takeaway_zh": "认为策略应同时以机器人实际执行状态和主端指令为输入并预测未来指令，因为两者的偏差蕴含接触信息；在没有力觉输入的低成本平台上优于单独使用任一信号。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.08776.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.08576", "title": "Q-OIL: Optimistic Exploration in Learning from Human Interventions", "type": "paper", "arxiv": "2611.08576", "year": 2026, "venue": "CoRL 2026", "org": "University of Washington", "url": null, "code": null, "project": "https://weirdlabuw.github.io/qoil", "topics": [ "learning" ], "tags": [ "human-in-the-loop", "interventions", "rl", "real", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-09", "takeaway_en": "Real users intervene when progress stalls, often with imperfect fixes; a progress-based human model makes sim benchmarking match them. Q-OIL adds optimistic bonuses on intervention steps; reports 10-50% higher success.", "takeaway_zh": "真实用户往往在进展停滞时介入，且纠正动作并不完美；据此建立的基于进展的人类模型让仿真评测更贴近真人。Q-OIL 对介入转移加乐观奖励并用解耦评论家稳定训练，报告成功率提升 10-50%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08576.html", "labs": [ "uw" ], "code_license": null }, { "id": "arxiv-2611.11464", "title": "Off-Policy RL with Online Inference Tuning: My Solution to the CoRL 2026 RoboEval Challenge", "type": "paper", "arxiv": "2611.11464", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/IliaLarchenko/RoboEvalSolution", "project": "https://ilialarchenko.com/projects/roboeval2026/", "topics": [ "learning", "vla" ], "tags": [ "challenge", "offline-rl", "awr", "pi0.5", "bimanual", "thompson-sampling" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-10", "takeaway_en": "Challenge report: a π0.5 policy that is its own critic, trained off-policy on the exact score; Thompson sampling tunes ~20 inference settings; adds generated and recovery demos. Reports 0.987 RDS.", "takeaway_zh": "比赛技术报告：基于 π0.5 的策略同时充当价值函数，直接以官方评分做离策略 RL，每次 rollout 用 Thompson 采样调约 20 个推理参数，并加入生成示范与失败恢复数据。官方种子上报告 0.987 RDS。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2611.11464.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2611.10949", "title": "Explicitly capturing demonstrator multi-modality for distillation into diffusion policies", "type": "paper", "arxiv": "2611.10949", "year": 2026, "venue": null, "org": "MIT", "url": null, "code": null, "project": "https://shaoyuan.cc/research/vine/", "topics": [ "learning" ], "tags": [ "diffusion-policy", "behavior-cloning", "multimodality", "data-collection" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-10", "takeaway_en": "Diffusion policies trained on sliding-window demos often memorise one action per state; querying the demonstrator k times at one state (vine sampling) keeps the multimodality and aids best-of-N and expert iteration.", "takeaway_zh": "用滑窗示范训练的扩散策略往往每个状态只记住一个动作。在同一状态下向示范者查询 k 次（vine 采样）可保留其多模态分布，并提升 best-of-N 与专家迭代效果。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.10949.html", "labs": [ "mit" ], "code_license": null }, { "id": "arxiv-2611.12102", "title": "SPARK: Simple Post-training for Adapting pRetrained Knowledge to Robot Control", "type": "paper", "arxiv": "2611.12102", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "learning" ], "tags": [ "visual-representation", "post-training", "foundation-models" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-10", "takeaway_en": "Post-trains a vision foundation model for robot control: a temporal-abstraction loss keeps action-relevant change, patch alignment to the original keeps semantics. Gains on several benchmarks and a real arm.", "takeaway_zh": "不从头预训练，而是对现成视觉基础模型做面向机器人控制的后训练：时间抽象目标保留与动作相关的变化，与原模型的 patch 级对齐保留其语义。在多个基准和真实机械臂上提升成功率与泛化。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.12102.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.10061", "title": "UniLatent: Cross-Embodiment Transfer via Latent Observation Alignment", "type": "paper", "arxiv": "2611.10061", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://selina2023.github.io/unilatent/", "topics": [ "learning" ], "tags": [ "cross-embodiment", "representation-alignment", "sim-paired-data" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-10", "takeaway_en": "Reuses demos across arms by aligning a source encoder to a frozen target encoder on sim-rendered paired views, no pixel translation. Up to 93% zero-shot in sim; real xArm→Franka 27/30 with 10 demos per task.", "takeaway_zh": "用仿真渲染的成对视角，把源机器人的视觉编码器对齐到冻结的目标编码器，无需像素级图像翻译即可跨机械臂复用示范。仿真零样本成功率最高 93%；真实 xArm→Franka 每任务仅用 10 条目标示范微调即 27/30 成功。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.10061.html", "labs": [], "code_license": null }, { "id": "mujoco", "title": "MuJoCo", "type": "simulator", "arxiv": null, "year": 2012, "venue": null, "org": "Google DeepMind", "url": "https://mujoco.org/", "code": "https://github.com/google-deepmind/mujoco", "project": null, "topics": [ "sim" ], "tags": [ "physics", "open-source" ], "robot_types": [], "highlight": true, "rating": 3, "added": null, "takeaway_en": "Fast, accurate contact physics; the default simulator for robot learning research, open-sourced by DeepMind.", "takeaway_zh": "快速准确的接触物理仿真，机器人学习研究的默认仿真器，由 DeepMind 开源。", "github_stars": 17304, "page": "https://embodied-ai-radar.com/e/mujoco.html", "labs": [ "google-deepmind" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2502.08844", "title": "MuJoCo Playground", "type": "paper", "arxiv": "2502.08844", "year": 2025, "venue": null, "org": "Google DeepMind, UC Berkeley", "url": null, "code": "https://github.com/google-deepmind/mujoco_playground", "project": null, "topics": [ "sim" ], "tags": [ "gpu-parallel", "sim2real", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "GPU-accelerated MJX environments for locomotion and manipulation that train in minutes and transfer to real robots.", "takeaway_zh": "基于 MJX 的 GPU 加速运动与操作环境，几分钟即可训练并迁移到真实机器人。", "github_stars": 2294, "page": "https://embodied-ai-radar.com/e/arxiv-2502.08844.html", "labs": [ "berkeley", "google-deepmind" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2108.10470", "title": "Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning", "type": "paper", "arxiv": "2108.10470", "year": 2021, "venue": null, "org": "NVIDIA", "url": null, "code": null, "project": null, "topics": [ "sim" ], "tags": [ "gpu-parallel", "physics" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Keeps physics and policy on the GPU end-to-end, enabling thousands of parallel environments on one card.", "takeaway_zh": "物理仿真与策略全程在 GPU 上运行，单卡即可并行数千个环境。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2108.10470.html", "labs": [ "nvidia" ], "code_license": null }, { "id": "arxiv-2301.04195", "title": "Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments", "type": "paper", "arxiv": "2301.04195", "year": 2023, "venue": null, "org": "ETH Zurich, NVIDIA, Toronto", "url": null, "code": "https://github.com/isaac-sim/IsaacLab", "project": null, "topics": [ "sim" ], "tags": [ "gpu-parallel", "framework", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "The framework that became Isaac Lab — modular robots, sensors and tasks on top of Isaac Sim.", "takeaway_zh": "Isaac Lab 的前身，在 Isaac Sim 之上提供模块化的机器人、传感器与任务。", "github_stars": 8612, "page": "https://embodied-ai-radar.com/e/arxiv-2301.04195.html", "labs": [ "nvidia", "eth", "toronto" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2410.00425", "title": "ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI", "type": "paper", "arxiv": "2410.00425", "year": 2024, "venue": null, "org": "UC San Diego", "url": null, "code": "https://github.com/haosulab/ManiSkill", "project": null, "topics": [ "sim" ], "tags": [ "gpu-parallel", "rendering", "benchmark", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "GPU-parallel simulation and rendering with many manipulation tasks; fast visual RL and data generation.", "takeaway_zh": "GPU 并行仿真与渲染，内置大量操作任务，适合快速视觉强化学习与数据生成。", "github_stars": 3504, "page": "https://embodied-ai-radar.com/e/arxiv-2410.00425.html", "labs": [ "ucsd" ], "code_license": "Apache-2.0" }, { "id": "genesis", "title": "Genesis", "type": "simulator", "arxiv": null, "year": 2024, "venue": null, "org": "Genesis Embodied AI", "url": null, "code": "https://github.com/Genesis-Embodied-AI/Genesis", "project": null, "topics": [ "sim" ], "tags": [ "physics", "gpu-parallel", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "An open-source, Python-native physics platform covering rigid bodies, soft bodies, fluids and fast GPU simulation.", "takeaway_zh": "开源、Python 原生的物理平台，覆盖刚体、软体、流体，支持高速 GPU 仿真。", "github_stars": 30208, "page": "https://embodied-ai-radar.com/e/genesis.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2009.12293", "title": "robosuite: A Modular Simulation Framework and Benchmark for Robot Learning", "type": "paper", "arxiv": "2009.12293", "year": 2020, "venue": null, "org": "Stanford, UT Austin", "url": null, "code": "https://github.com/ARISE-Initiative/robosuite", "project": null, "topics": [ "sim", "datasets" ], "tags": [ "mujoco", "benchmark", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Modular MuJoCo-based manipulation environments behind many imitation-learning benchmarks.", "takeaway_zh": "基于 MuJoCo 的模块化操作环境，是众多模仿学习基准的底座。", "github_stars": 2708, "page": "https://embodied-ai-radar.com/e/arxiv-2009.12293.html", "labs": [ "stanford", "ut-austin" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2406.02523", "title": "RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots", "type": "paper", "arxiv": "2406.02523", "year": 2024, "venue": null, "org": "UT Austin, NVIDIA", "url": null, "code": "https://github.com/robocasa/robocasa", "project": null, "topics": [ "sim", "datasets" ], "tags": [ "kitchen", "benchmark", "synthetic-data" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "120 kitchen scenes, 2,500+ objects and 100 tasks with generated demonstrations for training generalist policies.", "takeaway_zh": "120 个厨房场景、2500 多个物体与 100 个任务，并附生成演示，用于训练通用策略。", "github_stars": 1872, "page": "https://embodied-ai-radar.com/e/arxiv-2406.02523.html", "labs": [ "nvidia", "ut-austin" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2003.08515", "title": "SAPIEN: A SimulAted Part-based Interactive ENvironment", "type": "paper", "arxiv": "2003.08515", "year": 2020, "venue": null, "org": "UC San Diego", "url": null, "code": null, "project": null, "topics": [ "sim" ], "tags": [ "articulated-objects", "physics" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A simulator with large-scale articulated objects (PartNet-Mobility) for interaction tasks like opening drawers.", "takeaway_zh": "包含大规模铰接物体（PartNet-Mobility）的仿真器，适合开抽屉等交互任务。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2003.08515.html", "labs": [ "ucsd" ], "code_license": null }, { "id": "arxiv-1703.06907", "title": "Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World", "type": "paper", "arxiv": "1703.06907", "year": 2017, "venue": null, "org": "OpenAI", "url": null, "code": null, "project": null, "topics": [ "sim" ], "tags": [ "sim2real", "domain-randomization" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Randomizing textures, lighting and cameras in sim makes vision models work on real images with no real data.", "takeaway_zh": "在仿真中随机化纹理、光照和相机，使视觉模型无需真实数据即可在真实图像上工作。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1703.06907.html", "labs": [ "openai" ], "code_license": null }, { "id": "arxiv-1710.06537", "title": "Sim-to-Real Transfer of Robotic Control with Dynamics Randomization", "type": "paper", "arxiv": "1710.06537", "year": 2017, "venue": null, "org": "OpenAI, UC Berkeley", "url": null, "code": null, "project": null, "topics": [ "sim" ], "tags": [ "sim2real", "domain-randomization", "rl" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Randomizing physical parameters during training yields control policies robust enough to run on a real arm.", "takeaway_zh": "训练中随机化物理参数，得到足以在真实机械臂上运行的鲁棒控制策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1710.06537.html", "labs": [ "berkeley", "openai" ], "code_license": null }, { "id": "arxiv-2610.19479", "title": "Active Generative Simulation: Meta-Learning What to Simulate for Sim-and-Real Policy Co-Training", "type": "paper", "arxiv": "2610.19479", "year": 2026, "venue": "CoRL 2026", "org": "Georgia Tech", "url": null, "code": null, "project": "https://meta-gensim.github.io", "topics": [ "sim" ], "tags": [ "sim-and-real", "co-training", "meta-learning" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-19", "takeaway_en": "MetaGenSim learns which simulated data to generate, using meta-gradients from real-world performance, instead of sampling the simulator uniformly.", "takeaway_zh": "MetaGenSim 利用来自真实世界表现的元梯度，学习应该生成哪些仿真数据，而不是对仿真器做均匀采样。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.19479.html", "labs": [ "georgia-tech" ], "code_license": null }, { "id": "arxiv-2506.18088", "title": "RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation", "type": "paper", "arxiv": "2506.18088", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/RoboTwin-Platform/RoboTwin", "project": null, "topics": [ "sim", "manipulation", "datasets" ], "tags": [ "sim", "dual-arm", "data-generation", "domain-randomization", "benchmark" ], "robot_types": [ "arm" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Simulation pipeline that writes and checks task code with multimodal LLMs to mass-produce bimanual demos under heavy randomization (clutter, lighting, backgrounds, instructions); also a 50-task dual-arm benchmark.", "takeaway_zh": "用多模态大模型自动生成并校验任务代码，在强域随机化（杂物、光照、背景、指令）下批量产出双臂演示数据的仿真管线，同时提供 50 个双臂任务的基准。", "github_stars": 3051, "page": "https://embodied-ai-radar.com/e/arxiv-2506.18088.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2310.17596", "title": "MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations", "type": "paper", "arxiv": "2310.17596", "year": 2023, "venue": "CoRL 2023", "org": "NVIDIA, UT Austin", "url": null, "code": "https://github.com/NVlabs/mimicgen", "project": null, "topics": [ "sim", "manipulation", "datasets" ], "tags": [ "sim", "data-generation", "imitation" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Turns a handful of human demos into thousands by splitting them into object-centric segments and replaying those segments in new scenes; widely reused to scale imitation data.", "takeaway_zh": "把少量人类演示切分为以物体为中心的片段，并在新场景中变换重放，从而扩增为数千条演示；被广泛用于扩充模仿学习数据。", "github_stars": 669, "page": "https://embodied-ai-radar.com/e/arxiv-2310.17596.html", "labs": [ "nvidia", "ut-austin" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2403.09227", "title": "BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation", "type": "paper", "arxiv": "2403.09227", "year": 2022, "venue": "CoRL 2022", "org": "Stanford", "url": null, "code": "https://github.com/StanfordVL/BEHAVIOR-1K", "project": null, "topics": [ "sim", "datasets" ], "tags": [ "sim", "household", "long-horizon", "benchmark" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "1,000 household activities chosen from a survey of what people want robots to do, defined in a logic language and simulated in OmniGibson with realistic physics and rendering.", "takeaway_zh": "依据人们希望机器人做什么的调查挑选出 1000 项家务活动，用逻辑语言定义，并在具备真实物理与渲染的 OmniGibson 中仿真。", "github_stars": 1797, "page": "https://embodied-ai-radar.com/e/arxiv-2403.09227.html", "labs": [ "stanford" ], "code_license": "MIT" }, { "id": "arxiv-2511.04831", "title": "Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning", "type": "paper", "arxiv": "2511.04831", "year": 2025, "venue": null, "org": "NVIDIA", "url": null, "code": "https://github.com/isaac-sim/IsaacLab", "project": null, "topics": [ "sim" ], "tags": [ "gpu-parallel", "framework", "sensors", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "The reference paper for Isaac Lab, Orbit's successor — GPU-parallel physics, rendering and sensor simulation with ready-made robots and tasks for RL and imitation.", "takeaway_zh": "Isaac Lab（Orbit 的后继）的正式论文：GPU 并行的物理、渲染与传感器仿真，内置机器人与任务，面向强化学习和模仿学习。", "github_stars": 8612, "page": "https://embodied-ai-radar.com/e/arxiv-2511.04831.html", "labs": [ "nvidia" ], "code_license": "NOASSERTION" }, { "id": "arxiv-1712.05474", "title": "AI2-THOR: An Interactive 3D Environment for Visual AI", "type": "paper", "arxiv": "1712.05474", "year": 2017, "venue": null, "org": "Allen Institute for AI", "url": null, "code": "https://github.com/allenai/ai2thor", "project": null, "topics": [ "sim" ], "tags": [ "household", "interactive", "embodied-agents", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Photorealistic interactive indoor scenes where agents open, pick up and change objects; the base of many embodied-AI benchmarks such as ALFRED.", "takeaway_zh": "具有可交互物体的逼真室内场景，智能体可开关、拾取和改变物体，是 ALFRED 等众多具身智能基准的基础。", "github_stars": 1831, "page": "https://embodied-ai-radar.com/e/arxiv-1712.05474.html", "labs": [ "ai2" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2310.13724", "title": "Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots", "type": "paper", "arxiv": "2310.13724", "year": 2023, "venue": "ICLR 2024", "org": "Meta AI", "url": null, "code": "https://github.com/facebookresearch/habitat-lab", "project": null, "topics": [ "sim", "navigation" ], "tags": [ "sim", "human-robot-collaboration", "humanoid-avatars", "social-navigation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Adds simulated humanoid avatars and a human-in-the-loop interface to Habitat, so robots can be trained and evaluated on tasks shared with people, such as social navigation and social rearrangement.", "takeaway_zh": "为 Habitat 加入仿真人形化身和真人在环交互接口，可在与人协作的任务（如社交导航、社交重排）上训练和评测机器人。", "github_stars": 3187, "page": "https://embodied-ai-radar.com/e/arxiv-2310.13724.html", "labs": [ "meta" ], "code_license": "MIT" }, { "id": "arxiv-2610.28950", "title": "HyperDrone: Simulation-to-Reality Transfer at Millions of Frames per Second", "type": "paper", "arxiv": "2610.28950", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "sim" ], "tags": [ "aerial", "quadrotor", "simulator", "gpu", "rendering", "sim-to-real" ], "robot_types": [ "aerial" ], "highlight": false, "rating": 0, "added": "2026-10-22", "takeaway_en": "GPU batch-rendering quadrotor simulator; reports 20×+ the speed of existing batch quadrotor sims, with a new ray tracer rendering RGB, depth, segmentation, normals and optical flow (CoRL 2026).", "takeaway_zh": "以 GPU 批量渲染为核心的四旋翼仿真器；报告比现有批量四旋翼仿真器快 20 倍以上，新的光线追踪引擎（支持 OptiX、Vulkan、Metal、WebGPU）可渲染 RGB、深度、分割、法向和光流（CoRL 2026）。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.28950.html", "labs": [], "code_license": null }, { "id": "arxiv-2106.13281", "title": "Brax: A Differentiable Physics Engine for Large Scale Rigid Body Simulation", "type": "paper", "arxiv": "2106.13281", "year": 2021, "venue": "NeurIPS 2021 Datasets and Benchmarks", "org": "Google", "url": null, "code": "https://github.com/google/brax", "project": null, "topics": [ "sim", "learning" ], "tags": [ "physics", "jax", "gpu", "tpu", "differentiable" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "JAX physics engine that runs thousands of environments in parallel on GPU/TPU together with the RL training loop, cutting locomotion training from hours to minutes.", "takeaway_zh": "基于 JAX 的物理引擎，在 GPU/TPU 上与强化学习训练循环一起并行运行数千个环境，把运动控制训练从数小时缩短到数分钟。", "github_stars": 3258, "page": "https://embodied-ai-radar.com/e/arxiv-2106.13281.html", "labs": [ "google-deepmind" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2106.14405", "title": "Habitat 2.0: Training Home Assistants to Rearrange their Habitat", "type": "paper", "arxiv": "2106.14405", "year": 2021, "venue": "NeurIPS 2021", "org": "Meta AI", "url": null, "code": null, "project": null, "topics": [ "sim", "navigation", "manipulation" ], "tags": [ "rearrangement", "mobile-manipulation", "home", "benchmark" ], "robot_types": [ "arm", "mobile" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Adds fast rigid-body physics and an interactive apartment dataset (ReplicaCAD) to Habitat, plus a home-rearrangement benchmark for mobile manipulators.", "takeaway_zh": "为 Habitat 加入快速刚体物理与可交互公寓数据集 ReplicaCAD，并提出面向移动操作机器人的家居物品重排基准。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2106.14405.html", "labs": [ "meta" ], "code_license": null }, { "id": "arxiv-2404.05695", "title": "Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer", "type": "paper", "arxiv": "2404.05695", "year": 2024, "venue": null, "org": "RobotEra", "url": null, "code": "https://github.com/roboterax/humanoid-gym", "project": null, "topics": [ "sim", "locomotion" ], "tags": [ "humanoid", "rl", "isaac-gym", "sim-to-sim", "sim-to-real" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Isaac Gym–based RL framework for humanoid locomotion with a sim-to-sim check in MuJoCo before deploying zero-shot to real humanoids; a common starting point for humanoid RL.", "takeaway_zh": "基于 Isaac Gym 的人形机器人运动强化学习框架，先在 MuJoCo 中做 sim2sim 验证，再零样本部署到真实人形机器人，是人形 RL 的常用起点。", "github_stars": 2121, "page": "https://embodied-ai-radar.com/e/arxiv-2404.05695.html", "labs": [ "robotera" ], "code_license": "" }, { "id": "gazebo", "title": "Gazebo", "type": "simulator", "arxiv": null, "year": 2004, "venue": null, "org": "Open Robotics", "url": "https://gazebosim.org/", "code": "https://github.com/gazebosim/gz-sim", "project": null, "topics": [ "sim" ], "tags": [ "ros", "ros2", "physics", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "The long-standing open-source robot simulator of the ROS ecosystem — sensors, plugins and ROS 2 integration; the usual choice for testing full robot software stacks rather than training policies.", "takeaway_zh": "ROS 生态中历史悠久的开源机器人仿真器，提供传感器、插件与 ROS 2 集成；通常用于测试完整的机器人软件栈，而非训练策略。", "github_stars": 1571, "page": "https://embodied-ai-radar.com/e/gazebo.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2006.12983", "title": "dm_control: Software and Tasks for Continuous Control", "type": "paper", "arxiv": "2006.12983", "year": 2020, "venue": null, "org": "DeepMind", "url": null, "code": "https://github.com/google-deepmind/dm_control", "project": null, "topics": [ "sim", "learning" ], "tags": [ "mujoco", "benchmark", "rl", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "DeepMind's Python stack on top of MuJoCo — bindings, PyMJCF and Composer for building models and tasks, the standard Control Suite benchmarks, plus locomotion and arm-and-bricks manipulation tasks.", "takeaway_zh": "DeepMind 基于 MuJoCo 的 Python 工具栈：底层绑定、用于程序化建模与编写任务的 PyMJCF 和 Composer、作为标准基准的 Control Suite，以及运动控制和机械臂拼积木操作任务。", "github_stars": 4725, "page": "https://embodied-ai-radar.com/e/arxiv-2006.12983.html", "labs": [ "google-deepmind" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2011.07215", "title": "SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation", "type": "paper", "arxiv": "2011.07215", "year": 2020, "venue": "CoRL 2020", "org": "Carnegie Mellon University", "url": null, "code": "https://github.com/Xingyu-Lin/softgym", "project": "https://sites.google.com/view/softgym", "topics": [ "sim", "manipulation" ], "tags": [ "deformable", "cloth", "rope", "liquid", "benchmark", "rl" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Open-source Gym-style benchmark for manipulating deformable objects such as cloth, rope and liquids; shows that high-dimensional, partially observable states make these tasks hard for standard RL.", "takeaway_zh": "开源、Gym 接口的可变形物体操作基准（布料、绳子、液体等）；实验表明高维且部分可观测的状态让这类任务对常规强化学习方法很困难。", "github_stars": 357, "page": "https://embodied-ai-radar.com/e/arxiv-2011.07215.html", "labs": [ "cmu" ], "code_license": "BSD-3-Clause" }, { "id": "newton", "title": "Newton", "type": "simulator", "arxiv": null, "year": 2025, "venue": null, "org": "Disney Research, Google DeepMind, NVIDIA (Linux Foundation project)", "url": null, "code": "https://github.com/newton-physics/newton", "project": null, "topics": [ "sim" ], "tags": [ "physics", "gpu-parallel", "open-source", "warp", "openusd" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Open, GPU-accelerated physics engine built on NVIDIA Warp and OpenUSD with multiple solvers for contact-rich robot learning; co-developed by Disney Research, Google DeepMind and NVIDIA, hosted by the Linux Foundation.", "takeaway_zh": "基于 NVIDIA Warp 和 OpenUSD 的开源 GPU 物理引擎，支持多种求解器，面向接触丰富的机器人学习；由 Disney Research、Google DeepMind 和 NVIDIA 联合开发，由 Linux 基金会托管。", "github_stars": 5938, "page": "https://embodied-ai-radar.com/e/newton.html", "labs": [ "nvidia", "google-deepmind" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2410.24185", "title": "DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning", "type": "paper", "arxiv": "2410.24185", "year": 2024, "venue": "ICRA 2025", "org": "NVIDIA, UT Austin", "url": null, "code": "https://github.com/NVlabs/dexmimicgen", "project": "https://dexmimicgen.github.io/", "topics": [ "sim", "manipulation", "datasets" ], "tags": [ "sim", "humanoid", "dexterous-hand", "bimanual", "data-generation", "imitation" ], "robot_types": [ "arm", "hand", "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Extends MimicGen to humanoids with two dexterous hands, turning 60 human demos into 21K simulated ones across bimanual tasks, plus a real-to-sim-to-real pipeline tested on real can sorting.", "takeaway_zh": "把 MimicGen 扩展到双灵巧手人形机器人，从 60 条人类演示生成 2.1 万条双臂仿真演示，并给出在真实分拣罐子任务上验证的 real-to-sim-to-real 流程。", "github_stars": 298, "page": "https://embodied-ai-radar.com/e/arxiv-2410.24185.html", "labs": [ "nvidia", "ut-austin" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.32978", "title": "SceneBloom: Agentic Indoor Environment Generation with Realistic and Diverse Layouts for Embodied Learning", "type": "paper", "arxiv": "2610.32978", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Tinaliu0123/scenebloom", "project": null, "topics": [ "sim", "navigation" ], "tags": [ "scene-generation", "diffusion", "simulation-ready", "object-navigation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Generates sim-ready rooms by sampling furniture layouts with diffusion priors, then compiling them into collision-free 3D scenes; nav policies trained there beat ones trained on real scans on held-out real scans.", "takeaway_zh": "先用扩散先验在功能分区内采样家具布局，再“编译”为无碰撞、可直接仿真的 3D 场景；论文报告在其场景中训练的物体导航策略，在留出的真实扫描场景上优于用等量真实扫描训练的策略。", "github_stars": 74, "page": "https://embodied-ai-radar.com/e/arxiv-2610.32978.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2506.10966", "title": "GenManip: LLM-driven Simulation for Generalizable Instruction-Following Manipulation", "type": "paper", "arxiv": "2506.10966", "year": 2025, "venue": "CVPR 2025", "org": "Shanghai AI Lab", "url": null, "code": "https://github.com/InternRobotics/GenManip", "project": null, "topics": [ "sim", "manipulation", "datasets" ], "tags": [ "sim", "isaac-sim", "benchmark", "llm", "generalization" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Isaac Sim platform where an LLM builds tasks and layouts from a task-oriented scene graph over ~10K objects; its 200-scenario benchmark finds modular foundation-model systems generalize better than end-to-end policies.", "takeaway_zh": "基于 Isaac Sim 的平台：LLM 依据任务导向场景图，从约 1 万个物体中生成任务与布局；其 200 个场景的基准发现，结合基础模型的模块化系统比端到端策略泛化更好。", "github_stars": 179, "page": "https://embodied-ai-radar.com/e/arxiv-2506.10966.html", "labs": [ "shanghai-ai-lab" ], "code_license": "" }, { "id": "arxiv-2610.28465", "title": "MARE: An Open-Source Lunar Rover Simulator with Shadow Ground Truth for Perception and Autonomy", "type": "paper", "arxiv": "2610.28465", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/raulcastar/MARE-Lunar-Simulator", "project": null, "topics": [ "sim", "perception", "navigation" ], "tags": [ "sim", "space", "rover", "unreal-engine", "ros2", "synthetic-data" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Unreal Engine 5 + ROS 2 lunar rover simulator: generated cratered terrain, two rovers with stereo, lidar, event and SPAD cameras, pixel-accurate shadow ground truth for low-sun polar lighting, and a GNC stack.", "takeaway_zh": "基于 Unreal Engine 5 与 ROS 2 的开源月球车仿真：可生成带陨石坑的地形，两台月球车配备双目、激光雷达、事件相机与 SPAD 相机，提供极区低太阳角下像素级的阴影真值，并附 GNC 导航控制栈。", "github_stars": 15, "page": "https://embodied-ai-radar.com/e/arxiv-2610.28465.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.37799", "title": "DexSuite: An All-in-One Framework for Dexterous Manipulation Across Arms, Hands, and Tasks", "type": "paper", "arxiv": "2610.37799", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://dexsuiteorg.github.io", "topics": [ "sim", "manipulation", "datasets" ], "tags": [ "dexterous-hands", "genesis", "benchmark", "teleoperation", "tactile-sim", "cross-embodiment", "sim" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "Open dexterous-manipulation framework on Genesis: 30 tasks, 10 arms × 12 end effectors (100+ single-arm robots), teleoperation, simulated touch, a 16-pair benchmark and 4,700 demo episodes.", "takeaway_zh": "基于 Genesis 仿真器的开源灵巧操作一体化框架：30 个任务，10 种机械臂与 12 种末端（从平行夹爪到灵巧手）组合出 100 多种单臂机器人和大量双臂组合；自带统一环境 API、遥操作、仿真触觉，定义 16 组任务-本体基准，并发布 4,700 条成功示范。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.37799.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.40565", "title": "Gym-Anything for Robotics: Scaling physical simulations with digital agents", "type": "paper", "arxiv": "2610.40565", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/gym-anything/robotics", "project": "https://gym-anything.github.io/robotics", "topics": [ "sim", "datasets", "learning" ], "tags": [ "coding-agents", "sim-generation", "rl-environments", "benchmark", "frontier-models", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "Coding agents build robotics simulations, checked by an adversarial create-and-audit loop; result RoboWorld: 25 sims chosen by U.S. GDP share, 200 RL envs. Best frontier models average just 0.06 reward on test tasks.", "takeaway_zh": "把“写仿真”当作编码智能体任务：智能体生成机器人仿真与任务，再由对抗式的创建-审核循环把关；据此构建 RoboWorld（按美国 GDP 构成挑选的 25 个仿真、200 个强化学习环境），最强前沿模型在测试任务上平均奖励仅 0.06。", "github_stars": 48, "page": "https://embodied-ai-radar.com/e/arxiv-2610.40565.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.41857", "title": "When the Simulator Fails, Not the Policy: Auditing Contact Artifacts in Simulated Robot Benchmarks", "type": "paper", "arxiv": "2610.41857", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/ALEXander22568/simuguard", "project": null, "topics": [ "sim" ], "tags": [ "benchmark-audit", "physx", "robotwin", "libero", "contact" ], "robot_types": [], "highlight": true, "rating": 2, "added": "2026-11-02", "takeaway_en": "Many benchmark 'policy failures' are physics-engine ejections. SimuGuard traced RoboTwin artifacts to one collision constant; changing it cut artifact episodes from 12/100 to none and lifted five tasks 89% → 98%.", "takeaway_zh": "审计仿真基准：不少“策略失败”其实是物理引擎把物体弹飞。SimuGuard 为 LIBERO、RoboTwin、RoboCasa、RoboDojo 提供审计插件，把 RoboTwin 上的问题追溯到碰撞检测的一个常量；仅改动它，异常回合由 100 回合 12 次降到 0，五个容器任务成功率由 89% 升到 98%。", "github_stars": 54, "page": "https://embodied-ai-radar.com/e/arxiv-2610.41857.html", "labs": [], "code_license": "" }, { "id": "arxiv-2603.16861", "title": "MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation", "type": "paper", "arxiv": "2603.16861", "year": 2026, "venue": "CoRL 2026", "org": "Ai2", "url": null, "code": "https://github.com/allenai/MolmoBot", "project": "https://allenai.github.io/MolmoBot", "topics": [ "sim" ], "tags": [ "sim-to-real", "zero-shot", "procedural-data", "mobile-manipulation" ], "robot_types": [ "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. Argues simulation can be enough: large, diverse synthetic data gives zero-shot real transfer for static and mobile manipulation. MolmoBot-Engine is an open procedural data pipeline.", "takeaway_zh": "CoRL 2026 Oral。挑战“仅靠仿真不够”的观点：足够大规模、多样的合成数据即可零样本迁移到真实静态与移动操作。MolmoBot-Engine 是开源的程序化数据管线。", "github_stars": 119, "page": "https://embodied-ai-radar.com/e/arxiv-2603.16861.html", "labs": [ "ai2" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2603.16536", "title": "Kamino: GPU-based Simulation of Rigid Multi-Body Physics with Kinematic Loops", "type": "paper", "arxiv": "2603.16536", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "sim" ], "tags": [ "gpu-simulation", "kinematic-loops", "newton" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. GPU physics solver in Python on NVIDIA Warp, integrated into Newton, for parallel simulation of robots with true kinematic loops rather than tree approximations.", "takeaway_zh": "CoRL 2026 Spotlight。基于 NVIDIA Warp 的 Python GPU 物理求解器，集成于 Newton，可并行仿真含真实运动学闭环的机器人，而非树形近似。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.16536.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.34452", "title": "AuRA: From Atoms to Bits through Autonomous Robot Interaction", "type": "paper", "arxiv": "2610.34452", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Yoonkyo/AuRA", "project": "https://aura-robot.github.io/", "topics": [ "sim" ], "tags": [ "digital-twin", "deformable", "data-collection" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Automates the robot interaction and data preparation behind physics-informed digital twins; reports 88.5% less active human labor per asset across 13 deformable objects.", "takeaway_zh": "自动完成物理数字孪生所需的机器人交互与数据准备；在 13 种柔性物体上报告每个资产的人工投入减少 88.5%。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2610.34452.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2611.02691", "title": "Closing the Dynamics Gap through Self-Directed Robotic Interaction", "type": "paper", "arxiv": "2611.02691", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://cue-dynamics.github.io/", "topics": [ "sim", "learning" ], "tags": [ "real2sim", "system-identification", "agentic", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "CUE (CoRL 2026 Late Breaking Results): an agent identifies friction and restitution by choosing interactions where its simulation is least accurate; on billiards it reports 72% lower held-out position error.", "takeaway_zh": "CUE（CoRL 2026 Late Breaking Results）：智能体主动选择仿真最不准的交互来辨识摩擦和恢复系数；在台球任务上报告留出位置误差比最强基线低 72%，并部署到真实球桌。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.02691.html", "labs": [], "code_license": null }, { "id": "arxiv-2403.08716", "title": "DIFFTACTILE: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation", "type": "simulator", "arxiv": "2403.08716", "year": 2024, "venue": "ICLR 2024", "org": null, "url": null, "code": "https://github.com/Genesis-Embodied-AI/DiffTactile", "project": null, "topics": [ "sim", "tactile" ], "tags": [ "differentiable", "tactile", "fem" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Differentiable tactile simulator with an FEM soft-body elastomer model, built for contact with many material types rather than mostly rigid objects.", "takeaway_zh": "可微触觉仿真器，用 FEM 软体模型模拟弹性体，面向多种材料的接触，而非主要针对刚体。", "github_stars": 317, "page": "https://embodied-ai-radar.com/e/arxiv-2403.08716.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2504.12908", "title": "Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation", "type": "simulator", "arxiv": "2504.12908", "year": 2025, "venue": "NeurIPS 2025", "org": null, "url": null, "code": null, "project": null, "topics": [ "sim", "tactile" ], "tags": [ "gpu", "vision-based-tactile", "ipc" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "GPU tactile simulation combining IPC and ABD; the authors report running 18 times faster than real time across thousands of parallel environments.", "takeaway_zh": "结合 IPC 与 ABD 的 GPU 触觉仿真；作者报告在数千并行环境下达到实时速度的 18 倍。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2504.12908.html", "labs": [], "code_license": null }, { "id": "arxiv-2411.04776", "title": "TacEx: GelSight Tactile Simulation in Isaac Sim -- Combining Soft-Body and Visuotactile Simulators", "type": "simulator", "arxiv": "2411.04776", "year": 2024, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "sim", "tactile" ], "tags": [ "isaac", "vision-based-tactile", "rl" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Brings GIPC soft-body contact and the Taxim and FOTS tactile simulators into Isaac Sim for GelSight Mini, with Isaac Lab RL environments.", "takeaway_zh": "把 GIPC 软体接触与 Taxim、FOTS 触觉仿真器接入 Isaac Sim，模拟 GelSight Mini，并提供 Isaac Lab 强化学习环境。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2411.04776.html", "labs": [], "code_license": null }, { "id": "arxiv-2407.07788", "title": "BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark", "type": "benchmark", "arxiv": "2407.07788", "year": 2024, "venue": null, "org": null, "url": null, "code": null, "project": "https://chernyadev.github.io/bigym/", "topics": [ "sim", "datasets", "manipulation" ], "tags": [ "bimanual", "mobile-manipulation", "benchmark", "human-demonstrations" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Benchmark of 40 home tasks for mobile bimanual robots, from reaching to kitchen cleaning, each with human-collected demonstrations.", "takeaway_zh": "面向移动双臂机器人的基准，含 40 个家庭任务（从抓取到厨房清洁），均配人类采集的演示。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2407.07788.html", "labs": [], "code_license": null }, { "id": "arxiv-2407.10943", "title": "GRUtopia: Dream General Robots in a City at Scale", "type": "simulator", "arxiv": "2407.10943", "year": 2024, "venue": null, "org": "OpenRobotLab", "url": null, "code": "https://github.com/OpenRobotLab/GRUtopia", "project": null, "topics": [ "sim", "datasets" ], "tags": [ "city-scale", "scenes", "humanoid" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "Simulated interactive 3D society for many robot types; its GRScenes set has 100k annotated scenes across 89 categories that combine into city-scale worlds.", "takeaway_zh": "面向多种机器人的交互式三维仿真社会；GRScenes 含 10 万标注场景、89 个类别，可组合成城市级环境。", "github_stars": 1308, "page": "https://embodied-ai-radar.com/e/arxiv-2407.10943.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2412.13211", "title": "ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks", "type": "benchmark", "arxiv": "2412.13211", "year": 2024, "venue": "ICLR 2025", "org": null, "url": null, "code": "https://github.com/arth-shukla/mshab", "project": null, "topics": [ "sim", "datasets", "manipulation" ], "tags": [ "gpu", "benchmark", "home", "rearrangement" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "GPU-accelerated Home Assistant Benchmark with realistic low-level control; the authors report over 3x the speed of prior magical-grasp versions.", "takeaway_zh": "GPU 加速的 Home Assistant Benchmark，采用真实低层控制；作者报告速度超过此前“魔法抓取”实现的 3 倍。", "github_stars": 210, "page": "https://embodied-ai-radar.com/e/arxiv-2412.13211.html", "labs": [], "code_license": "" }, { "id": "arxiv-2607.13451", "title": "Learning Physics-Guided Residual Dynamics for Deformable Object Simulation", "type": "paper", "arxiv": "2607.13451", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/shivanshpatel35/pgrd", "project": "https://pgrd-robot.github.io/", "topics": [ "sim", "manipulation" ], "tags": [ "deformable", "residual-dynamics" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "PGRD simulates deformables with an optimizable spring-mass backbone plus learned residuals, reporting better accuracy than either alone; used for MPC and action-conditioned video prediction.", "takeaway_zh": "PGRD 以可优化的弹簧质点模型为骨架，加上学习得到的残差来仿真可变形物体，报告精度优于单用任一方法；可用于 MPC 和动作条件视频预测。", "github_stars": 14, "page": "https://embodied-ai-radar.com/e/arxiv-2607.13451.html", "labs": [], "code_license": "" }, { "id": "arxiv-2510.15352", "title": "GaussGym: An open-source real-to-sim framework for learning locomotion from pixels", "type": "paper", "arxiv": "2510.15352", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "sim", "locomotion" ], "tags": [ "gaussian-splatting", "real-to-sim", "open-source" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Drops 3D Gaussian Splatting into vectorized physics simulators as a renderer, reporting over 100,000 steps per second on consumer GPUs for learning from pixels.", "takeaway_zh": "把 3D 高斯溅射作为渲染器接入矢量化物理仿真，报告在消费级 GPU 上超过每秒 10 万步，用于从像素学习。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2510.15352.html", "labs": [], "code_license": null }, { "id": "arxiv-2601.02078", "title": "Genie Sim 3.0 : A High-Fidelity Comprehensive Simulation Platform for Humanoid Robot", "type": "paper", "arxiv": "2601.02078", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/AgibotTech/genie_sim", "project": null, "topics": [ "sim" ], "tags": [ "simulator", "humanoid", "benchmark" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": "2026-11-04", "takeaway_en": "Genie Sim 3.0: a manipulation simulation platform whose LLM tool builds scenes from natural language, with an LLM- and VLM-based automated benchmark.", "takeaway_zh": "Genie Sim 3.0：面向操作的仿真平台，用 LLM 工具依自然语言生成场景，并提供基于 LLM 与 VLM 的自动化评测基准。", "github_stars": 1540, "page": "https://embodied-ai-radar.com/e/arxiv-2601.02078.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2302.04659", "title": "ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills", "type": "paper", "arxiv": "2302.04659", "year": 2023, "venue": "ICLR 2023", "org": null, "url": null, "code": "https://github.com/mani-skill/ManiSkill", "project": "https://maniskill2.github.io/", "topics": [ "sim" ], "tags": [ "benchmark", "simulator", "generalization" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Unified manipulation benchmark built to add object-level geometric and topological variation that earlier suites lacked.", "takeaway_zh": "统一的操作基准，补上早期环境缺少的物体级几何与拓扑变化。", "github_stars": 3504, "page": "https://embodied-ai-radar.com/e/arxiv-2302.04659.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2412.20977", "title": "UnrealZoo: Enriching Photo-realistic Virtual Worlds for Embodied AI", "type": "paper", "arxiv": "2412.20977", "year": 2024, "venue": "ICCV 2025", "org": null, "url": null, "code": "https://github.com/UnrealZoo/unrealzoo-gym", "project": "https://unrealzoo.site/", "topics": [ "sim" ], "tags": [ "simulator", "unreal-engine", "photorealistic" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Over 100 photorealistic Unreal Engine worlds with playable humans, animals, robots and vehicles for embodied AI research.", "takeaway_zh": "100 多个基于虚幻引擎的高真实感世界，含可操控的人、动物、机器人和车辆，服务具身智能研究。", "github_stars": 378, "page": "https://embodied-ai-radar.com/e/arxiv-2412.20977.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.22362", "title": "CoDA: Co-Evolution of Data Engines and Action Policies", "type": "paper", "arxiv": "2610.22362", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://plan-lab.github.io/projects/coda/", "topics": [ "sim", "manipulation" ], "tags": [ "generative-simulation", "policy-fine-tuning", "failure-driven" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Turns diagnosed policy failures into new simulated scenes and shaped training signals, so generator and policy improve together; on LIBERO-Pro, reports success rising from 25% to 84% under position perturbations.", "takeaway_zh": "把诊断出的策略失败转成新的仿真场景和塑形训练信号，让生成器与策略共同进化；在 LIBERO-Pro 上报告位置扰动下成功率从 25% 升至 84%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.22362.html", "labs": [], "code_license": null }, { "id": "arxiv-2603.18532", "title": "Scaling Sim-to-Real VLA Reinforcement Learning with Generative 3D Worlds", "type": "paper", "arxiv": "2603.18532", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://horizonrobotics.github.io/gail/scale-vla-rl", "topics": [ "sim" ], "tags": [ "generative-3d", "rl-finetuning", "vla", "sim-to-real", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Uses 3D world generative models to build 100 diverse interactive scenes for RL fine-tuning of a VLA. Reports sim success from 9.7% to 79.8% and real-world success from 21.7% to 75%.", "takeaway_zh": "用 3D 世界生成模型构建 100 个多样的可交互场景，对 VLA 做强化学习微调。报告仿真成功率从 9.7% 提升到 79.8%，真机从 21.7% 提升到 75%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.18532.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.13154", "title": "Worlds in One Demo: A Synthetic Data Engine for Learning Open-World Mobile Manipulation", "type": "paper", "arxiv": "2607.13154", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://wanda.lecar-lab.org/", "topics": [ "sim" ], "tags": [ "data-generation", "gaussian-splatting", "mobile-manipulation", "one-demo", "real" ], "robot_types": [ "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Synthetic data engine that turns one real mobile-manipulation demo into many: Gaussian-splat scenes, rearranged contact segments chained by whole-body planning, and generated worlds from photos. Also cross-embodiment.", "takeaway_zh": "合成数据引擎：把一条真实移动操作演示扩展成大量数据——高斯泼溅重建场景、重排接触片段并用全身运动规划串联、再在由照片生成的 3D 世界中合成。也支持跨本体。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.13154.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.04616", "title": "SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing", "type": "paper", "arxiv": "2607.04616", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://silo-cable-routing.github.io/", "topics": [ "sim" ], "tags": [ "deformable", "cable-routing", "sim-to-real", "gpu-simulation", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Sim-to-real RL for multi-stage cable routing: GPU-parallel cable simulation, localized policies and a simulation-in-the-loop executor. Reports higher success and 2x shorter cycle times than prior learning methods.", "takeaway_zh": "多阶段线缆布线的仿真到真机强化学习：GPU 并行线缆仿真、局部化策略，加上执行时“仿真在环”的部署框架。报告成功率更高、节拍时间缩短一半。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.04616.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.09487", "title": "Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks", "type": "paper", "arxiv": "2604.09487", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/intelligent-soft-robots/genan", "project": "https://webdav.tuebingen.mpg.de/genan/", "topics": [ "sim" ], "tags": [ "sim-to-real", "actuator-network", "pneumatic-muscles", "table-tennis", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Learns a neural actuator model from joint trajectories alone, combined with rigid-body sim, to transfer RL policies to a pneumatic-muscle arm. Claims the first sim-to-real transfer for a 4-DoF muscle-actuated arm.", "takeaway_zh": "仅用关节位置轨迹学习神经执行器模型，结合刚体仿真，把强化学习策略迁移到气动人工肌肉机械臂上。声称首次实现 4 自由度肌肉驱动机械臂的仿真到真机迁移。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2604.09487.html", "labs": [], "code_license": "" }, { "id": "arxiv-2607.06699", "title": "RoboSnap: One-Shot Real-to-Sim Scene Generation for Generalizable Robot Learning and Evaluation", "type": "paper", "arxiv": "2607.06699", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://robosnap.github.io", "topics": [ "sim" ], "tags": [ "real-to-sim", "gaussian-splatting", "scene-generation", "policy-evaluation", "droid" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Turns one RGB image into a sim-ready scene: collision-aware foreground assets plus a 3DGS background. Supports synthetic data and sim-real-correlated evaluation; releases DROID-Sim from 564 DROID scenes.", "takeaway_zh": "把单张 RGB 图像变成可用于仿真的场景：前景为碰撞可靠的资产，背景用 3D 高斯泼溅保持外观。可用于合成训练数据和与真机相关的策略评测；发布由 564 个 DROID 场景构建的 DROID-Sim。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.06699.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.11386", "title": "ComSim: Building Scalable Real-World Robot Data Generation via Compositional Simulation", "type": "paper", "arxiv": "2604.11386", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://faceong.github.io/ComSim/", "topics": [ "sim" ], "tags": [ "neural-simulation", "data-generation", "sim-to-real", "video", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Compositional simulation: classical sim produces accurate action-video pairs and a neural simulator restyles them as real-looking video, in a real-sim-real loop seeded by a little real data.", "takeaway_zh": "组合式仿真：经典仿真生成准确的动作-视频对，再由神经模拟器转换成接近真实的画面，形成以少量真机数据为起点的“真-仿-真”数据扩增闭环。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.11386.html", "labs": [], "code_license": null }, { "id": "arxiv-2605.30313", "title": "UniLab: A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms", "type": "paper", "arxiv": "2605.30313", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/Motphys/UniLab", "project": "https://unilabsim.github.io", "topics": [ "sim" ], "tags": [ "rl-infrastructure", "cpu-simulation", "training-efficiency", "mujoco", "cross-platform" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "CPU-parallel simulation with GPU learning, joined by one runtime for data movement and sync. Reports 3–10x end-to-end training speedups on the same hardware and runs on macOS, ROCm and Intel XPU.", "takeaway_zh": "CPU 并行仿真 + GPU 策略学习的异构训练架构，用统一运行时处理数据搬运与同步。报告同等硬件下端到端训练效率提升 3–10 倍，并支持 macOS、ROCm 与 Intel XPU。", "github_stars": 1005, "page": "https://embodied-ai-radar.com/e/arxiv-2605.30313.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.28276", "title": "SimFoundry: Modular and Automated Scene Generation for Policy Learning and Evaluation", "type": "paper", "arxiv": "2606.28276", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/NVlabs/SimFoundry", "project": "https://research.nvidia.com/labs/gear/simfoundry/", "topics": [ "sim" ], "tags": [ "real-to-sim", "digital-twins", "scene-generation", "policy-evaluation", "nvidia" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Builds sim-ready digital twins from a video and edits them into 'digital cousins'. Sim scores predict real results (mean Pearson 0.911); task cousins add 40% real success for sim-trained policies.", "takeaway_zh": "从一段视频自动构建可仿真的数字孪生，并编辑出保持可供性的“数字表亲”场景。仿真评测与真机结果高度相关（平均 Pearson 0.911）；任务表亲让仿真训练策略的真机成功率提升 40%。", "github_stars": 477, "page": "https://embodied-ai-radar.com/e/arxiv-2606.28276.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2606.22397", "title": "Do Rigid-Body Simulators Dream of Soft Robots? Learning Contact-Rich Manipulation for Tendon-Driven Continuum Robots", "type": "paper", "arxiv": "2606.22397", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/ContinuumRoboticsLab/opencr-mujoco", "project": "https://continuumroboticslab.github.io/opencr-mujoco/", "topics": [ "sim" ], "tags": [ "soft-robots", "continuum-robots", "mujoco", "sim-to-real", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Puts tendon-driven continuum robots natively into MuJoCo with a continuum-mechanics-based discretization, validated against Cosserat rods and hardware; sim-trained imitation policies transfer zero-shot to a real robot.", "takeaway_zh": "以连续介质力学推导的离散化方法，把肌腱驱动连续体机器人原生放进 MuJoCo，并对照 Cosserat 杆模型与真机验证；仿真中训练的模仿策略可零样本迁移到真机富接触任务。", "github_stars": 38, "page": "https://embodied-ai-radar.com/e/arxiv-2606.22397.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2505.10755", "title": "Procedural Generation of Articulated Simulation-Ready Assets", "type": "paper", "arxiv": "2505.10755", "year": 2025, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "sim" ], "tags": [ "assets", "articulated-objects", "procedural", "blender", "sim2real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Infinigen-Articulated: procedural generators for 18 articulated object categories plus Blender tools and an export pipeline into common robot simulators, with physical properties.", "takeaway_zh": "Infinigen-Articulated：涵盖 18 类常见铰接物体的程序化生成器，附带 Blender 工具，并可连同物理属性导出到常用机器人仿真器。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2505.10755.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.00641", "title": "AM-Bench: A Modular Simulation Suite and Benchmark for Aerial Manipulation Policy Learning", "type": "paper", "arxiv": "2609.00641", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "sim" ], "tags": [ "aerial-manipulation", "benchmark", "drone", "sim" ], "robot_types": [ "aerial" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Benchmark for multirotor aerial manipulation: under-, fully and over-actuated platforms, 12 tasks, configurable wind and saturation, standard low-level controllers and learning baselines for system-level comparison.", "takeaway_zh": "多旋翼空中操作基准：包含欠驱动、全驱动与过驱动平台，12 个任务，可配置气动扰动与执行器饱和，并提供标准底层控制器和策略学习基线，便于系统级比较。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.00641.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.08278", "title": "SIMPLE: Simulation-Based Policy Learning and Evaluation for Humanoid Loco-manipulation", "type": "paper", "arxiv": "2606.08278", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "sim" ], "tags": [ "humanoid", "benchmark", "mujoco", "isaac-sim", "teleoperation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Humanoid loco-manipulation testbed pairing MuJoCo physics with Isaac Sim rendering: 16 whole-body tasks, 50 scenes, 1,000+ objects, motion-planning and VR data pipelines; benchmarks imitation, VLA and WAM policies.", "takeaway_zh": "人形移动操作仿真平台，结合 MuJoCo 物理与 Isaac Sim 渲染：16 个全身任务、50 个室内场景、1000 多个物体资产，提供运动规划与 VR 数据管线，并评测模仿、VLA 与世界动作模型策略。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.08278.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.08368", "title": "VisionSIM: Beyond Idealized Sensors - A Multi-Modal Simulator for Robot Perception", "type": "paper", "arxiv": "2611.08368", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "sim" ], "tags": [ "sensor-simulation", "lidar", "event-camera", "thermal", "dataset" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Simulator that models raw measurement physics for event, SPAD, iToF/dToF, LiDAR and thermal sensors, reproducing failure modes like pileup, phase wrapping and refractory drops; releases an indoor dataset.", "takeaway_zh": "仿真平台对事件相机、SPAD、iToF/dToF、激光雷达和热成像的原始测量物理过程建模，复现堆积、相位缠绕、不应期丢事件等失效模式；并发布室内数据集。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08368.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.05927", "title": "GIF: Agentic Generation of Interactive and Functional Object Compositions for Robot Learning", "type": "paper", "arxiv": "2609.05927", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "sim" ], "tags": [ "scene-generation", "assets", "vlm-agent", "functional-objects" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Agentic generation of functional object compositions (e.g. parts that must fit): disentangled mesh reconstruction, relative pose refinement under geometric and physical guidance, and a VLM verifier.", "takeaway_zh": "智能体式生成具备功能关系的物体组合：先生成实例分离的网格，再在几何与物理引导下细化相对位姿，最后由 VLM 验证器挑选最符合描述的候选。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.05927.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.08872", "title": "Lodos: A Lightweight Simulator Designed for Contact-Rich Interactions with Detailed Meshes", "type": "paper", "arxiv": "2611.08872", "year": 2026, "venue": null, "org": "University of Tübingen", "url": null, "code": "https://github.com/martius-lab/lodos", "project": null, "topics": [ "sim" ], "tags": [ "simulator", "gpu", "mesh-collision", "contact", "warp" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-09", "takeaway_en": "GPU rigid-body simulator on NVIDIA Warp for detailed mesh–mesh contact; reports physics on par with established simulators at about 10x their throughput there. Lodos-v2 (2611.08868) adds exact frictional contact.", "takeaway_zh": "基于 NVIDIA Warp 的 GPU 刚体仿真器，专攻精细网格间接触；物理精度与主流仿真器相当，该场景下吞吐量约高一个数量级。Lodos-v2（2611.08868）进一步求解精确摩擦接触问题。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08872.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2310.08864", "title": "Open X-Embodiment: Robotic Learning Datasets and RT-X Models", "type": "dataset", "arxiv": "2310.08864", "year": 2023, "venue": null, "org": "Open X-Embodiment Collaboration", "url": null, "code": "https://github.com/google-deepmind/open_x_embodiment", "project": null, "topics": [ "datasets", "vla" ], "tags": [ "real", "cross-embodiment", "multi-robot" ], "robot_types": [], "highlight": true, "rating": 3, "added": null, "takeaway_en": "1M+ trajectories from 22 robot types pooled by 30+ labs; RT-X models trained on it beat single-robot baselines.", "takeaway_zh": "30 多个实验室汇集 22 种机器人的 100 多万条轨迹；在其上训练的 RT-X 优于单机器人基线。", "github_stars": 2095, "page": "https://embodied-ai-radar.com/e/arxiv-2310.08864.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2403.12945", "title": "DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset", "type": "dataset", "arxiv": "2403.12945", "year": 2024, "venue": null, "org": "DROID Collaboration", "url": null, "code": "https://github.com/droid-dataset/droid", "project": null, "topics": [ "datasets" ], "tags": [ "real", "arm", "in-the-wild" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "76k teleoperated Franka trajectories collected in hundreds of real scenes across many institutions.", "takeaway_zh": "由多家机构在数百个真实场景中采集的 7.6 万条 Franka 遥操作轨迹。", "github_stars": 472, "page": "https://embodied-ai-radar.com/e/arxiv-2403.12945.html", "labs": [], "code_license": "" }, { "id": "arxiv-2308.12952", "title": "BridgeData V2: A Dataset for Robot Learning at Scale", "type": "dataset", "arxiv": "2308.12952", "year": 2023, "venue": null, "org": "UC Berkeley", "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "real", "arm", "low-cost" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "60k trajectories on a low-cost WidowX arm across many kitchens and tasks; a common pretraining source.", "takeaway_zh": "在低成本 WidowX 机械臂上采集、涵盖众多厨房与任务的 6 万条轨迹，常用作预训练数据。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2308.12952.html", "labs": [ "berkeley" ], "code_license": null }, { "id": "arxiv-2307.00595", "title": "RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot", "type": "dataset", "arxiv": "2307.00595", "year": 2023, "venue": null, "org": "Shanghai Jiao Tong University", "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "real", "arm", "multimodal", "force" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "110k+ contact-rich sequences with vision, force, audio and paired human demonstration videos.", "takeaway_zh": "11 万多条接触丰富的序列，包含视觉、力、音频及配对的人类演示视频。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2307.00595.html", "labs": [ "sjtu" ], "code_license": null }, { "id": "arxiv-2503.06669", "title": "AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems", "type": "dataset", "arxiv": "2503.06669", "year": 2025, "venue": null, "org": "AgiBot, Shanghai AI Lab", "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "real", "humanoid", "dual-arm", "large-scale" ], "robot_types": [ "arm", "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A million-scale real-robot dataset collected in purpose-built scenes, with the GO-1 generalist policy trained on it.", "takeaway_zh": "在专门搭建的场景中采集的百万级真实机器人数据集，并基于其训练通用策略 GO-1。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2503.06669.html", "labs": [ "shanghai-ai-lab", "agibot" ], "code_license": null }, { "id": "arxiv-2110.07058", "title": "Ego4D: Around the World in 3,000 Hours of Egocentric Video", "type": "dataset", "arxiv": "2110.07058", "year": 2021, "venue": null, "org": "Meta AI and university partners", "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "egocentric-video", "human-data" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "3,670 hours of first-person daily-life video; widely used to pretrain visual representations for robots.", "takeaway_zh": "3670 小时第一视角日常生活视频，广泛用于为机器人预训练视觉表征。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2110.07058.html", "labs": [ "meta" ], "code_license": null }, { "id": "arxiv-2306.03310", "title": "LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning", "type": "benchmark", "arxiv": "2306.03310", "year": 2023, "venue": null, "org": "UT Austin", "url": null, "code": "https://github.com/Lifelong-Robot-Learning/LIBERO", "project": null, "topics": [ "datasets" ], "tags": [ "sim", "manipulation", "lifelong-learning" ], "robot_types": [], "highlight": true, "rating": 2, "added": null, "takeaway_en": "130 language-conditioned tasks in four suites; now the most common simulation benchmark for VLA papers.", "takeaway_zh": "四个套件共 130 个语言条件任务，现已成为 VLA 论文最常用的仿真基准。", "github_stars": 2525, "page": "https://embodied-ai-radar.com/e/arxiv-2306.03310.html", "labs": [ "ut-austin" ], "code_license": "MIT" }, { "id": "arxiv-2112.03227", "title": "CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks", "type": "benchmark", "arxiv": "2112.03227", "year": 2021, "venue": null, "org": "University of Freiburg", "url": null, "code": "https://github.com/mees/calvin", "project": null, "topics": [ "datasets" ], "tags": [ "sim", "manipulation", "long-horizon" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Chains of five language instructions in a row test long-horizon, language-conditioned control.", "takeaway_zh": "连续执行五条语言指令，测试长时程、语言条件下的控制能力。", "github_stars": 1002, "page": "https://embodied-ai-radar.com/e/arxiv-2112.03227.html", "labs": [ "freiburg" ], "code_license": "MIT" }, { "id": "arxiv-1909.12271", "title": "RLBench: The Robot Learning Benchmark & Learning Environment", "type": "benchmark", "arxiv": "1909.12271", "year": 2019, "venue": null, "org": "Imperial College London", "url": null, "code": "https://github.com/stepjam/RLBench", "project": null, "topics": [ "datasets" ], "tags": [ "sim", "manipulation" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "100 hand-designed manipulation tasks with demos; a staple for 3D policy methods like PerAct.", "takeaway_zh": "100 个手工设计的操作任务并附演示，是 PerAct 等 3D 策略方法的常用基准。", "github_stars": 1838, "page": "https://embodied-ai-radar.com/e/arxiv-1909.12271.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-1910.10897", "title": "Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning", "type": "benchmark", "arxiv": "1910.10897", "year": 2019, "venue": null, "org": "Stanford, UC Berkeley", "url": null, "code": "https://github.com/Farama-Foundation/Metaworld", "project": null, "topics": [ "datasets" ], "tags": [ "sim", "manipulation", "rl", "multi-task" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "50 Sawyer manipulation tasks for multi-task and meta-RL evaluation.", "takeaway_zh": "50 个 Sawyer 操作任务，用于多任务与元强化学习评测。", "github_stars": 1906, "page": "https://embodied-ai-radar.com/e/arxiv-1910.10897.html", "labs": [ "berkeley", "stanford" ], "code_license": "MIT" }, { "id": "arxiv-2405.05941", "title": "Evaluating Real-World Robot Manipulation Policies in Simulation", "type": "benchmark", "arxiv": "2405.05941", "year": 2024, "venue": null, "org": "UC San Diego, Stanford, UC Berkeley, Google DeepMind", "url": null, "code": "https://github.com/simpler-env/SimplerEnv", "project": null, "topics": [ "datasets", "vla" ], "tags": [ "sim", "real-to-sim", "evaluation" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "SimplerEnv — real-to-sim environments whose scores correlate with real-robot results for RT-1/Octo-style policies.", "takeaway_zh": "SimplerEnv：真实到仿真的评测环境，其得分与 RT-1/Octo 类策略的真机表现相关。", "github_stars": 1179, "page": "https://embodied-ai-radar.com/e/arxiv-2405.05941.html", "labs": [ "berkeley", "stanford", "google-deepmind", "ucsd" ], "code_license": "MIT" }, { "id": "arxiv-1904.01201", "title": "Habitat: A Platform for Embodied AI Research", "type": "paper", "arxiv": "1904.01201", "year": 2019, "venue": null, "org": "Meta AI", "url": null, "code": "https://github.com/facebookresearch/habitat-sim", "project": null, "topics": [ "datasets", "sim", "navigation" ], "tags": [ "sim", "navigation", "photorealistic" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "A very fast photorealistic 3D simulator and task suite that standardized embodied navigation research.", "takeaway_zh": "极速的逼真 3D 仿真器与任务集，统一了具身导航研究的标准。", "github_stars": 3849, "page": "https://embodied-ai-radar.com/e/arxiv-1904.01201.html", "labs": [ "meta" ], "code_license": "MIT" }, { "id": "lerobot", "title": "LeRobot", "type": "code", "arxiv": null, "year": 2024, "venue": null, "org": "Hugging Face", "url": null, "code": "https://github.com/huggingface/lerobot", "project": null, "topics": [ "datasets", "hardware", "manipulation" ], "tags": [ "library", "open-source", "low-cost", "datasets" ], "robot_types": [], "highlight": true, "rating": 2, "added": null, "takeaway_en": "PyTorch library with datasets, pretrained policies (ACT, Diffusion, SmolVLA) and drivers for low-cost arms.", "takeaway_zh": "PyTorch 机器人学习库，提供数据集、预训练策略（ACT、Diffusion、SmolVLA）与低成本机械臂驱动。", "github_stars": 28795, "page": "https://embodied-ai-radar.com/e/lerobot.html", "labs": [ "hugging-face" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.16238", "title": "Embodied Skill Bench: Benchmarking How Well Agents with Embodied Skills Perform on Physical Tasks", "type": "benchmark", "arxiv": "2610.16238", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/HanZhi306/EmbodiedSkill-Bench", "project": null, "topics": [ "datasets", "vla", "navigation" ], "tags": [ "llm-agents", "skills", "closed-loop" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-19", "takeaway_en": "Packages VLA policies, perception models and know-how as 250 reusable \"embodied skills\" and measures how much they help LLM agents on physical tasks.", "takeaway_zh": "将 VLA 策略、感知模型与经验知识封装成 250 个可复用的“具身技能”，评测它们对大模型智能体完成物理任务的帮助。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2610.16238.html", "labs": [], "code_license": "" }, { "id": "arxiv-2412.13877", "title": "RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation", "type": "dataset", "arxiv": "2412.13877", "year": 2024, "venue": null, "org": "Beijing Humanoid Robot Innovation Center, PKU", "url": null, "code": null, "project": "https://x-humanoid-robomind.github.io/", "topics": [ "datasets" ], "tags": [ "real", "multi-embodiment", "humanoid", "teleoperation" ], "robot_types": [ "arm", "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Large real-robot teleoperation dataset across several embodiments (single-arm, dual-arm, humanoid) collected under one standardized protocol, and it keeps failed demos with annotated causes.", "takeaway_zh": "在统一规范下采集的大规模真机遥操作数据集，覆盖单臂、双臂与人形等多种本体，并保留了标注失败原因的失败演示。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2412.13877.html", "labs": [ "pku" ], "code_license": null }, { "id": "arxiv-2610.27861", "title": "RoboCasa-Shift: Stage-Aware Robustness Evaluation of Vision-Language-Action Policies in Long-Horizon Household Manipulation", "type": "benchmark", "arxiv": "2610.27861", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/HaochengWang23/RoboCasa-Shift", "project": null, "topics": [ "datasets", "vla", "sim" ], "tags": [ "benchmark", "robustness", "long-horizon", "household", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-21", "takeaway_en": "VLA robustness benchmark on RoboCasa365: paired clean/perturbed rollouts differing in one shift, plus stage-level diagnostics of where long-horizon failures start; evaluated on GR00T N1.5.", "takeaway_zh": "基于 RoboCasa365 的 VLA 鲁棒性诊断基准：成对比较只差一个分布偏移因素的干净/扰动执行，并定位长时序任务中失败从哪个阶段开始，而不只看总成功率；在 GR00T N1.5 上评测。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2610.27861.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2109.08238", "title": "Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI", "type": "dataset", "arxiv": "2109.08238", "year": 2021, "venue": "NeurIPS 2021", "org": "Meta AI, Matterport", "url": "https://aihabitat.org/datasets/hm3d/", "code": "https://github.com/facebookresearch/habitat-matterport3d-dataset", "project": null, "topics": [ "datasets", "navigation", "sim" ], "tags": [ "3d-scans", "indoor", "habitat" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "1,000 3D scans of real indoor spaces for Habitat; the main scene set behind ObjectNav and many embodied navigation benchmarks.", "takeaway_zh": "1000 个真实室内空间的 3D 扫描，供 Habitat 使用；ObjectNav 等众多具身导航基准的主要场景集。", "github_stars": 216, "page": "https://embodied-ai-radar.com/e/arxiv-2109.08238.html", "labs": [ "meta" ], "code_license": "MIT" }, { "id": "arxiv-1910.11215", "title": "RoboNet: Large-Scale Multi-Robot Learning", "type": "dataset", "arxiv": "1910.11215", "year": 2019, "venue": "CoRL 2019", "org": "UC Berkeley, Stanford, UPenn", "url": null, "code": "https://github.com/SudeepDasari/RoboNet", "project": null, "topics": [ "datasets", "learning" ], "tags": [ "dataset", "real", "arm", "multi-embodiment", "video-prediction" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "An early shared dataset of robot interaction videos from seven robot platforms, used to pretrain visual dynamics models that transfer to new robots - a forerunner of Open X-Embodiment.", "takeaway_zh": "早期的共享机器人交互视频数据集，涵盖七种机器人平台，用于预训练可迁移到新机器人的视觉动力学模型，是 Open X-Embodiment 的先驱。", "github_stars": 178, "page": "https://embodied-ai-radar.com/e/arxiv-1910.11215.html", "labs": [ "berkeley", "stanford" ], "code_license": "MIT" }, { "id": "arxiv-2311.18259", "title": "Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives", "type": "dataset", "arxiv": "2311.18259", "year": 2023, "venue": "CVPR 2024", "org": "Meta AI and university partners", "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "dataset", "egocentric", "human-video", "multimodal" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Time-synced first-person and third-person video of people performing skilled activities (cooking, sports, repair...), with rich annotations; a large source for learning manipulation skills from humans.", "takeaway_zh": "人们进行技能活动（烹饪、运动、维修等）时同步采集的第一人称与第三人称视频，附丰富标注，是从人类视频学习操作技能的重要数据来源。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2311.18259.html", "labs": [ "meta" ], "code_license": null }, { "id": "arxiv-2212.08051", "title": "Objaverse: A Universe of Annotated 3D Objects", "type": "dataset", "arxiv": "2212.08051", "year": 2022, "venue": "CVPR 2023", "org": "Allen Institute for AI", "url": "https://huggingface.co/datasets/allenai/objaverse", "code": null, "project": null, "topics": [ "datasets", "sim" ], "tags": [ "dataset", "3d-assets", "objects" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "800K+ annotated 3D object models; widely used to populate simulators with diverse objects for grasping, rearrangement and procedural scene generation (Objaverse-XL extends it to 10M+).", "takeaway_zh": "80 万+ 带标注的 3D 物体模型，常用于为仿真器填充多样物体，用于抓取、重排和程序化场景生成（Objaverse-XL 扩展到 1000 万+）。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2212.08051.html", "labs": [ "ai2" ], "code_license": null }, { "id": "arxiv-1702.04405", "title": "ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes", "type": "dataset", "arxiv": "1702.04405", "year": 2017, "venue": "CVPR 2017", "org": "Stanford, Princeton, TUM", "url": null, "code": "https://github.com/ScanNet/ScanNet", "project": null, "topics": [ "datasets", "perception" ], "tags": [ "dataset", "rgbd", "3d-reconstruction", "semantic-segmentation", "indoor" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "RGB-D scans of 1,500+ indoor scenes with 3D reconstructions, camera poses and semantic labels; the standard benchmark for 3D scene understanding that embodied perception builds on.", "takeaway_zh": "1500+ 个室内场景的 RGB-D 扫描，含三维重建、相机位姿与语义标注；具身感知所依赖的三维场景理解标准基准。", "github_stars": 2362, "page": "https://embodied-ai-radar.com/e/arxiv-1702.04405.html", "labs": [ "stanford", "princeton" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2403.10506", "title": "HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation", "type": "benchmark", "arxiv": "2403.10506", "year": 2024, "venue": "RSS 2024", "org": "UC Berkeley · Yonsei University", "url": null, "code": "https://github.com/carlosferrazza/humanoid-bench", "project": "https://humanoid-bench.github.io", "topics": [ "datasets", "locomotion", "sim" ], "tags": [ "sim", "humanoid", "whole-body", "rl-benchmark", "mujoco" ], "robot_types": [ "hand", "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "27 simulated tasks (12 locomotion, 15 whole-body manipulation) on a Unitree H1 with two dexterous hands; reports that DreamerV3, TD-MPC2, SAC and PPO fail most of them, especially long-horizon whole-body ones.", "takeaway_zh": "27 个仿真任务（12 个运动、15 个全身操作），机器人为带两只灵巧手的 Unitree H1；报告 DreamerV3、TD-MPC2、SAC、PPO 在大多数任务上未达标，长时程全身协调任务尤甚。", "github_stars": 805, "page": "https://embodied-ai-radar.com/e/arxiv-2403.10506.html", "labs": [ "berkeley" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2402.08191", "title": "THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation", "type": "benchmark", "arxiv": "2402.08191", "year": 2024, "venue": "RSS 2024", "org": "UW · AI2 · USC · NVIDIA · UCSP", "url": null, "code": "https://github.com/robot-colosseum/robot-colosseum", "project": "https://robot-colosseum.github.io", "topics": [ "datasets", "manipulation" ], "tags": [ "sim", "generalization", "perturbations", "rlbench" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "20 RLBench tasks × 14 perturbation factors (color, texture, lighting, distractors, camera pose, friction, mass…); reports 30–50% success drops for leading policies, 75%+ when perturbations combine.", "takeaway_zh": "20 个 RLBench 任务 × 14 种扰动因素（颜色、纹理、光照、干扰物、相机位姿、摩擦、质量……）；报告主流策略成功率下降 30–50%，多种扰动叠加时下降 75% 以上。", "github_stars": 160, "page": "https://embodied-ai-radar.com/e/arxiv-2402.08191.html", "labs": [ "nvidia", "uw", "ai2", "usc" ], "code_license": "" }, { "id": "arxiv-2104.04631", "title": "DexYCB: A Benchmark for Capturing Hand Grasping of Objects", "type": "dataset", "arxiv": "2104.04631", "year": 2021, "venue": "CVPR 2021", "org": "NVIDIA · University of Washington", "url": null, "code": "https://github.com/NVlabs/dex-ycb-toolkit", "project": null, "topics": [ "datasets", "manipulation", "perception" ], "tags": [ "real", "human-hands", "grasping", "hand-pose", "6d-pose", "handover" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "~582K RGB-D frames (1,000 sequences, 10 people, 20 YCB objects, 8 cameras) of human grasps with hand and object pose labels; benchmarks detection, 6D object pose, 3D hand pose and human-to-robot handover.", "takeaway_zh": "约 58.2 万帧 RGB-D（1000 段序列、10 人、20 个 YCB 物体、8 台相机）的人手抓取数据，带手部与物体位姿标注；评测检测、物体 6D 位姿、三维手部姿态及人到机器人的物体交接。", "github_stars": 215, "page": "https://embodied-ai-radar.com/e/arxiv-2104.04631.html", "labs": [ "nvidia", "uw" ], "code_license": "GPL-3.0" }, { "id": "arxiv-1502.03143", "title": "Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols", "type": "dataset", "arxiv": "1502.03143", "year": 2015, "venue": null, "org": "Yale · CMU · UC Berkeley", "url": null, "code": null, "project": null, "topics": [ "datasets", "manipulation" ], "tags": [ "objects", "3d-models", "benchmark-protocols", "grasping" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "The YCB object set — 73 everyday objects with scans, textured meshes and simulation models, plus benchmarking protocols; widely used in grasping and pose-estimation work.", "takeaway_zh": "YCB 物体集：73 个日常物体，附扫描数据、带纹理网格与仿真模型，以及基准测试规程；被抓取与位姿估计研究广泛使用。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-1502.03143.html", "labs": [ "berkeley", "cmu" ], "code_license": null }, { "id": "arxiv-2006.13256", "title": "Rescaling Egocentric Vision", "type": "dataset", "arxiv": "2006.13256", "year": 2020, "venue": "IJCV 2022", "org": "University of Bristol · University of Catania · University of Toronto", "url": null, "code": "https://github.com/epic-kitchens/epic-kitchens-100-annotations", "project": null, "topics": [ "datasets", "perception" ], "tags": [ "egocentric-video", "human-data", "kitchens", "action-recognition" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "EPIC-KITCHENS-100 — 100 hours (20M frames, ~90K action segments) of unscripted first-person kitchen video from 45 environments, with dense action labels and challenges such as action anticipation.", "takeaway_zh": "EPIC-KITCHENS-100：来自 45 个厨房环境、100 小时（2000 万帧、约 9 万个动作片段）的非脚本第一视角视频，动作标注密集，并设有动作预测等挑战任务。", "github_stars": 173, "page": "https://embodied-ai-radar.com/e/arxiv-2006.13256.html", "labs": [ "toronto" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2203.01577", "title": "HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction", "type": "dataset", "arxiv": "2203.01577", "year": 2022, "venue": "CVPR 2022", "org": "Tsinghua University · Shanghai Qi Zhi Institute", "url": null, "code": "https://github.com/leolyliu/HOI4D-Instructions", "project": null, "topics": [ "datasets", "manipulation", "perception" ], "tags": [ "egocentric-video", "human-data", "rgbd", "articulated-objects", "4d" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "2.4M egocentric RGB-D frames (4,000+ sequences, 800 object instances in 16 categories, 610 rooms) of people handling rigid and articulated objects, with segmentation, hand and object pose labels.", "takeaway_zh": "240 万帧第一视角 RGB-D（4000 多段序列、16 类 800 个物体实例、610 个房间），记录人操作刚体与铰接物体，带分割、手部与物体位姿标注。", "github_stars": 98, "page": "https://embodied-ai-radar.com/e/arxiv-2203.01577.html", "labs": [ "tsinghua", "qizhi" ], "code_license": "MIT" }, { "id": "arxiv-2203.15709", "title": "OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction", "type": "dataset", "arxiv": "2203.15709", "year": 2022, "venue": "CVPR 2022", "org": "Shanghai Jiao Tong University", "url": null, "code": "https://github.com/oakink/OakInk", "project": null, "topics": [ "datasets", "manipulation" ], "tags": [ "human-hands", "grasping", "affordance", "mano" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Affordance labels for ~1,800 household objects plus 50K intent-oriented hand-object interactions recorded on 100 of them; a common source for dexterous grasp synthesis. Follow-up OakInk2 covers bimanual tasks.", "takeaway_zh": "为约 1800 个日常物体标注可供性，并在其中 100 个物体上记录 5 万次带意图的手-物交互；常用于灵巧抓取生成。后续 OakInk2 扩展到双手任务。", "github_stars": 153, "page": "https://embodied-ai-radar.com/e/arxiv-2203.15709.html", "labs": [ "sjtu" ], "code_license": "MIT" }, { "id": "arxiv-2204.13662", "title": "ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation", "type": "dataset", "arxiv": "2204.13662", "year": 2022, "venue": "CVPR 2023", "org": "ETH Zürich · MPI for Intelligent Systems", "url": null, "code": "https://github.com/zc-alexfan/arctic", "project": null, "topics": [ "datasets", "manipulation", "perception" ], "tags": [ "human-hands", "bimanual", "articulated-objects", "mocap", "mano" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "2.1M video frames of two hands manipulating articulated objects (scissors, laptops…) with accurate 3D hand and object meshes from motion capture; a reference for bimanual hand-object reconstruction.", "takeaway_zh": "210 万帧双手操作铰接物体（剪刀、笔记本电脑等）的视频，借助动作捕捉提供精确的手与物体三维网格；双手-物体重建的常用基准。", "github_stars": 532, "page": "https://embodied-ai-radar.com/e/arxiv-2204.13662.html", "labs": [ "eth" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2412.18194", "title": "VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning Tasks", "type": "benchmark", "arxiv": "2412.18194", "year": 2024, "venue": null, "org": "Fudan University", "url": null, "code": "https://github.com/OpenMOSS/VLABench", "project": "https://vlabench.github.io/", "topics": [ "datasets", "vla", "manipulation" ], "tags": [ "sim", "language", "long-horizon", "reasoning", "vla-eval" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "100 task categories over 2,000+ objects that need common sense, world knowledge and multi-step reasoning, not just instruction following; evaluates VLAs, embodied agents and VLMs in one suite.", "takeaway_zh": "100 类任务、2000 多个物体，任务需要常识、世界知识与多步推理，而不只是照指令执行；在同一套件中评测 VLA、具身智能体与 VLM。", "github_stars": 482, "page": "https://embodied-ai-radar.com/e/arxiv-2412.18194.html", "labs": [ "fudan" ], "code_license": "MIT" }, { "id": "arxiv-2504.18904", "title": "RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning", "type": "benchmark", "arxiv": "2504.18904", "year": 2025, "venue": "RSS 2025", "org": "UC Berkeley · PKU · BIGAI · Stanford · CMU and others", "url": null, "code": "https://github.com/RoboVerseOrg/RoboVerse", "project": "https://roboverseorg.github.io/", "topics": [ "datasets", "sim" ], "tags": [ "sim", "multi-simulator", "synthetic-data", "imitation-learning", "rl" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "MetaSim puts many simulators (Isaac Sim/Gym, MuJoCo, Genesis, SAPIEN…) behind one interface; adds a large synthetic dataset migrated from public benchmarks and unified IL/RL benchmarks.", "takeaway_zh": "MetaSim 用统一接口封装多种仿真器（Isaac Sim/Gym、MuJoCo、Genesis、SAPIEN、PyBullet 等）；提供由公开基准迁移而来的大规模合成数据集，以及按泛化层级划分的统一模仿学习/强化学习基准。", "github_stars": 1884, "page": "https://embodied-ai-radar.com/e/arxiv-2504.18904.html", "labs": [ "berkeley", "stanford", "cmu", "pku" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.18872", "title": "EgoExplorer: A Vision-Motion-Touch Observatory for Human Loco-Manipulation In Situ", "type": "dataset", "arxiv": "2610.18872", "year": 2026, "venue": null, "org": "InternRobotics", "url": null, "code": "https://github.com/InternRobotics/EgoExplorer", "project": "https://internrobotics.github.io/egoexplorer.github.io/", "topics": [ "datasets", "locomotion", "tactile", "manipulation" ], "tags": [ "real", "human-data", "egocentric", "wearable", "whole-body", "tactile", "humanoid" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-24", "takeaway_en": "100 h of wearable-captured human loco-manipulation (mostly professional work) aligning egocentric video, whole-body and finger motion, touch and step-level text; reports 71.9% lower body error than baselines.", "takeaway_zh": "用可穿戴设备采集的 100 小时人类移动操作数据，侧重职业场景，对齐第一视角视频、全身与手指动作、触觉和步骤级文字描述；其补全全身动作的方法报告身体关键点误差比最强基线低 71.9%。", "github_stars": 27, "page": "https://embodied-ai-radar.com/e/arxiv-2610.18872.html", "labs": [ "shanghai-ai-lab" ], "code_license": "" }, { "id": "arxiv-2610.26765", "title": "HandsOn: A Large-Scale Simulation Benchmark for Generalizable Dexterous Manipulation", "type": "benchmark", "arxiv": "2610.26765", "year": 2026, "venue": null, "org": "Tsinghua University, BUPT, NatureWill, Peking University", "url": null, "code": "https://github.com/TsinghuaC3I/HandsOn", "project": "https://llltttwww.github.io/HandsOn-website/", "topics": [ "datasets", "manipulation", "sim" ], "tags": [ "sim", "dexterous", "cross-embodiment", "benchmark", "human-demos" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-10-24", "takeaway_en": "Isaac Sim benchmark with 5 arms, 6 dexterous hands, ~200 assets and 18k human demos over 66 tasks; reports the best trained policy (a WAM) at only 29.1% on Core-50, near 0% on precision-fit and deformable tasks.", "takeaway_zh": "基于 Isaac Sim 的灵巧操作基准：5 种机械臂、6 种灵巧手、约 200 个资产、1.8 万条人类演示、66 个任务；报告最好的训练策略（一种世界动作模型）在 Core-50 上仅 29.1%，精密装配和柔性物体任务接近 0%。", "github_stars": 73, "page": "https://embodied-ai-radar.com/e/arxiv-2610.26765.html", "labs": [ "tsinghua", "pku" ], "code_license": "MIT" }, { "id": "arxiv-2610.26663", "title": "RoboValue: A Fine-Grained Sim-and-Real Benchmark for Unified Evaluation of Robotic Value Models", "type": "benchmark", "arxiv": "2610.26663", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/RoboValue-Benchmark/RoboValue", "project": "https://robovalue-benchmark.github.io/", "topics": [ "datasets", "learning", "manipulation" ], "tags": [ "sim", "real", "benchmark", "value-model", "evaluation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-24", "takeaway_en": "Tests robot value (progress) models on 15 sim and 20 real tasks across task state, progress, failure/recovery and consistency; reports that good forward-progress scores hide weak history and recovery reasoning.", "takeaway_zh": "在 15 个仿真和 20 个真实任务上评测机器人价值（进度）模型，覆盖任务状态、进度监测、失败与恢复、一致性四个维度；报告称前向进度得分高的模型在依赖历史和恢复判断上仍然薄弱。", "github_stars": 17, "page": "https://embodied-ai-radar.com/e/arxiv-2610.26663.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.16782", "title": "LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints", "type": "benchmark", "arxiv": "2610.16782", "year": 2026, "venue": null, "org": "Tongji University (IAIE Physical AI Group)", "url": null, "code": "https://github.com/IAIE-PhysicalAI/LIBERO-Industry", "project": "https://iaie-physicalai.github.io/LIBERO-Industry/", "topics": [ "datasets", "manipulation" ], "tags": [ "sim", "benchmark", "industrial", "conveyor", "libero" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-24", "takeaway_en": "216 LIBERO-based tasks with industrial constraints — conveyor loading/unloading/transfer, speed, time windows, tight tolerances; reports that VLAs and world action models degrade as constraints are combined.", "takeaway_zh": "基于 LIBERO 的 216 个工业约束任务：传送带上料、下料与转运，可调节物体形状、带速、交互时间窗与公差；报告 VLA 和世界动作模型在多重约束叠加时性能逐步下降。", "github_stars": 7, "page": "https://embodied-ai-radar.com/e/arxiv-2610.16782.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2505.11709", "title": "EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video", "type": "dataset", "arxiv": "2505.11709", "year": 2025, "venue": "ICLR 2026", "org": "Apple", "url": null, "code": "https://github.com/apple-aiml-research/ml-egodex", "project": null, "topics": [ "datasets", "manipulation" ], "tags": [ "real", "human-video", "egocentric", "dexterous-hand", "hand-pose" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "829 hours of egocentric Apple Vision Pro video with tracked 3D poses for every hand joint, across 194 tabletop tasks from tying shoelaces to folding laundry; includes hand-trajectory prediction benchmarks.", "takeaway_zh": "用 Apple Vision Pro 采集的 829 小时第一视角视频，配有每个手部关节的三维姿态，覆盖从系鞋带到叠衣服的 194 种桌面任务，并提供手部轨迹预测基准。", "github_stars": 410, "page": "https://embodied-ai-radar.com/e/arxiv-2505.11709.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.31779", "title": "FLORA: An Open Model Suite for Scaling Robotics Data Engines", "type": "paper", "arxiv": "2610.31779", "year": 2026, "venue": null, "org": "NVIDIA", "url": null, "code": "https://github.com/NVIDIA/Flora", "project": "https://research.nvidia.com/labs/gear/flora", "topics": [ "datasets", "learning" ], "tags": [ "data-engine", "reward-model", "captioning", "human-video", "open-weights" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "The open data engine behind GR00T training: models that write timestamped action captions, dense process rewards and episode outcome rewards for human and robot video; reports SOTA on WGO-Bench, RoboReward and Robometer.", "takeaway_zh": "GR00T 训练所用的开放数据引擎：一组模型为人类与机器人视频生成带时间戳的动作描述、稠密过程奖励和整段结果奖励；报告在 WGO-Bench、RoboReward 与 Robometer 上达到最优。", "github_stars": 105, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31779.html", "labs": [ "nvidia" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2610.25459", "title": "BiDex-100: A Comprehensive Benchmark for Bimanual Dexterous Manipulation Across 100+ Simulation Tasks", "type": "paper", "arxiv": "2610.25459", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://BiDex-100.github.io/BiDex-100/", "topics": [ "datasets", "manipulation" ], "tags": [ "benchmark", "sim", "bimanual", "dexterous-hand", "teleoperation", "vla" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "106 sim tasks for two dexterous hands along five axes (generalization, instructions, few demos, long horizon, memory), each with an out-of-domain twin; 8,080 teleoperated demos; seven VLAs benchmarked.", "takeaway_zh": "双灵巧手仿真基准：106 个任务按泛化、指令遵循、少示教、长时程、记忆五个维度组织，每个任务配有域外变体；提供 8,080 条遥操作示教，并在统一协议下评测了七个 VLA 模型。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.25459.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.31064", "title": "AstroBench: Benchmarking Free-Floating Capture and Space Robotic Manipulation", "type": "paper", "arxiv": "2610.31064", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/AstroUpLab/AstroBench", "project": "https://astroup.com/astrobench/", "topics": [ "datasets", "manipulation", "sim" ], "tags": [ "benchmark", "sim", "space", "free-floating", "arm" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Benchmark for catching drifting, tumbling objects inside a spacecraft with a fixed-base arm, plus storage and servicing tasks. Reports a frozen generalist policy captured only 2/200 static targets.", "takeaway_zh": "太空机器人操作基准：用固定基座机械臂抓取舱内漂浮、旋转的物体，另含样本存储与在轨维护任务。报告称所测的冻结通用策略在 200 个静止目标中只抓住 2 个。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2610.31064.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.27039", "title": "Embodied Playground: A Long-Horizon Agentic Robotics Benchmark for Physical, Strategic, and Spatial Reasoning", "type": "benchmark", "arxiv": "2610.27039", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Winniechen2002/Embodied-Playground", "project": "https://winniechen2002.github.io/Agent-bench-website/", "topics": [ "datasets", "vla" ], "tags": [ "benchmark", "long-horizon", "llm-agents", "planning", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Benchmark of 410 long-horizon task environments (manipulation, puzzles, household) for foundation-model robot agents, with a curated EP-100 split and a shared protocol checking goals, rules and physical validity.", "takeaway_zh": "面向基础模型机器人智能体的长程任务基准：410 个任务环境（基础操作、序列谜题、多阶段家务），精选 EP-100 子集，统一协议同时检查目标完成、任务规则与物理有效性。", "github_stars": 12, "page": "https://embodied-ai-radar.com/e/arxiv-2610.27039.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.34051", "title": "Behavior Spatial Benchmark: Evaluating Spatial Understanding for Robotics", "type": "paper", "arxiv": "2610.34051", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets", "vla", "sim" ], "tags": [ "benchmark", "spatial-reasoning", "vlm", "instruction-following", "behavior-1k" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "BEHAVIOR-Spatial, built on BEHAVIOR-1K: spatial QA, point grounding and robot action tests. A baseline policy drops from 41.6% on held-out layouts to 16.1% when the requested target changes; oracle target cues help most.", "takeaway_zh": "基于 BEHAVIOR-1K 的 BEHAVIOR-Spatial 基准：包含空间问答、指点定位与机器人动作评测。基线策略在未见布局上成功率 41.6%，但在同一布局中更换目标物体指令时降至 16.1%；提供真值目标提示带来的提升最大。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.34051.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.35385", "title": "EmbodiedLab: Benchmarking Embodied Agents That Act to Know", "type": "paper", "arxiv": "2610.35385", "year": 2026, "venue": null, "org": "HexaFuture", "url": null, "code": "https://github.com/HexaFuture/EmbodiedLab", "project": "https://hexafuture.ai/benchmarks/embodiedlab/", "topics": [ "datasets", "sim", "vla" ], "tags": [ "benchmark", "agents", "scientific-discovery", "simulated-lab", "frontier-models" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Benchmark where a robot must run physics experiments to find an unknown answer: 30 experiments, 5 disciplines, parameters resampled per instance. Reports even the best frontier agent succeeds under half the time.", "takeaway_zh": "“为求知而行动”的基准：机器人在物理仿真实验室中自行操作仪器、读数，测出每次随机重采样的未知参数。共 30 个实验、5 个学科；报告最强的前沿模型智能体在原生控制下成功率也不到一半，失败多源于与仪器交互。", "github_stars": 19, "page": "https://embodied-ai-radar.com/e/arxiv-2610.35385.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.34649", "title": "RobotAgentBench: Towards Open-world Robot Autonomy", "type": "paper", "arxiv": "2610.34649", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/AetherLabsAI/RobotAgentBench", "project": "https://aetherlabsai.github.io/RobotAgentBench/", "topics": [ "datasets", "vla" ], "tags": [ "benchmark", "robot-agent", "household", "real-to-sim", "long-horizon", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-27", "takeaway_en": "Benchmark for open-world robot agents: 50 household layouts built real-to-sim, tasks from 90 atomic ones to 4 open-ended scenarios, scoring discovery, reasoning, manipulation, recovery and robustness.", "takeaway_zh": "面向开放世界机器人智能体的基准：通过真实到仿真流水线构建 50 个家居布局，任务从 90 个原子任务到 4 个开放场景，评测任务发现、上下文推理、操作、失败恢复与鲁棒性，可统一比较 VLA、WAM 与智能体架构。", "github_stars": 221, "page": "https://embodied-ai-radar.com/e/arxiv-2610.34649.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.36420", "title": "SO-101 Bench: Accessible Spatial Manipulation", "type": "paper", "arxiv": "2610.36420", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/5hadytru/so101_bench", "project": "https://so101-bench.github.io/", "topics": [ "datasets", "sim", "manipulation" ], "tags": [ "benchmark", "so-101", "low-cost", "isaac-lab", "digital-twin", "vla" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Isaac Lab digital-twin benchmark for language-conditioned spatial tasks on the low-cost SO-101 arm (138 objects, 916 episodes); sim ranks policies exactly as the real arm. Best VLAs ~61%, an LLM-agent harness 83.2%.", "takeaway_zh": "基于 Isaac Lab 数字孪生的低成本 SO-101 机械臂语言条件空间操作基准（138 个物体、916 个评测回合）；仿真中的策略排名与真机完全一致。最好的 VLA 约 61%，由 LLM 写代码调用抓取策略的方案达 83.2%。", "github_stars": 44, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36420.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.27782", "title": "RoboJudge: Multimodal Language Models as Judges for Embodied Video Generation", "type": "paper", "arxiv": "2610.27782", "year": 2026, "venue": null, "org": "The Chinese University of Hong Kong", "url": null, "code": "https://github.com/Sphere-AI-Lab/RoboJudge", "project": "https://spherelab.ai/RoboJudge/", "topics": [ "datasets", "world-models" ], "tags": [ "benchmark", "video-generation", "mllm-judge", "physical-adherence", "human-annotation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Benchmark of 800 human-rated robot/hand videos from 10 generators to test MLLM judges on physical adherence and instruction alignment, plus a 12K-video training set and RoboJudge-9B (Pearson 0.719 with humans).", "takeaway_zh": "评测多模态大模型能否评判具身视频生成：800 条人工标注视频（10 个生成模型，夹爪/人手/灵巧手），考察物理合理性与指令一致性；另提供 1.2 万条训练视频及 RoboJudge-9B（与人类评分 Pearson 0.719）。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2610.27782.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.39167", "title": "EgoContact: Evaluating Physical Intelligence with Egocentric Video", "type": "paper", "arxiv": "2610.39167", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://egocontact.github.io", "topics": [ "datasets", "manipulation", "perception" ], "tags": [ "egocentric-video", "contact-prediction", "affordance", "benchmark", "human-data", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "Benchmark of 22.5K verified egocentric interactions with pixel-precise contact points: current models often miss where to touch. As a memory bank for an affordance policy it lifts real grasp success from 16% to 58%.", "takeaway_zh": "第一视角接触点预测基准：2.25 万个人工核验的交互事件，每个含接触前画面、语义意图与像素级接触位置。现有方法常常定位不准甚至找不到目标物体；把 EgoContact 用作已有可供性迁移策略的记忆库，真机抓取成功率从 16% 提升到 58%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.39167.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.41377", "title": "MolmoSpaces-CommonSense: Evaluating Commonsense in Robotic Manipulation Policies", "type": "paper", "arxiv": "2610.41377", "year": 2026, "venue": null, "org": "Allen Institute for AI (Ai2)", "url": null, "code": "https://github.com/allenai/molmospaces", "project": null, "topics": [ "datasets" ], "tags": [ "benchmark", "commonsense", "clutter", "affordances", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "Benchmark built on a taxonomy of commonsense reasoning (occlusion, containment, affordances). Reports zero-shot policies dropping over 50% as distractors grow and acting on the wrong object 30% of the time in clutter.", "takeaway_zh": "基于常识推理分类法（遮挡、容纳、支撑、可供性、隐藏物体）构建的操作基准。报告零样本通用策略在干扰物增多时成功率下降超过 50%，在杂乱场景中约 30% 的时间作用于错误物体，抓取正确部位的比例仅约一半。", "github_stars": 517, "page": "https://embodied-ai-radar.com/e/arxiv-2610.41377.html", "labs": [ "ai2" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2606.18097", "title": "WireCraft: A Simulation Benchmark for Industrial DLO Manipulation", "type": "paper", "arxiv": "2606.18097", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets", "manipulation" ], "tags": [ "benchmark", "deformable-objects", "cables", "sim" ], "robot_types": [], "highlight": false, "rating": 1, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Simulation benchmark for wires and cables in industrial assembly, aimed at the gap left by benchmarks tied to specific hardware or lacking modular, customizable task assets.", "takeaway_zh": "CoRL 2026 Spotlight。面向工业装配中线缆等可变形线状物体（DLO）的仿真基准，针对现有基准绑定特定硬件、缺少模块化可定制任务资产等问题。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.18097.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.40532", "title": "BURGER: Benchmarking and Understanding Robotic Generalization via Embodied Drawing", "type": "paper", "arxiv": "2610.40532", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Qxjyqzw/BURGER", "project": "https://siyingzhao.github.io/BURGER/", "topics": [ "datasets" ], "tags": [ "benchmark", "drawing", "vla", "sim", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "A language-conditioned drawing benchmark with open-ended valid solutions: 28,200 simulated demonstrations over 282 tasks, used to compare five VLA models on data scale, language grounding and multimodal behavior.", "takeaway_zh": "语言条件绘画基准：同一指令有无数种合法画法。含 282 个任务的 28,200 条仿真示范，用于比较五种 VLA 模型在数据规模、语言落地和多模态行为上的差异。", "github_stars": 3, "page": "https://embodied-ai-radar.com/e/arxiv-2610.40532.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2603.11634", "title": "Diversity You Can Actually Measure: A Fast, Model-Free Diversity Metric for Robotics Datasets", "type": "paper", "arxiv": "2603.11634", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "dataset-quality", "diversity-metric", "imitation-learning" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. Measures diversity of imitation datasets directly from demonstrations, with entropy defined through a signature kernel, so no model has to be trained first.", "takeaway_zh": "CoRL 2026 Oral。直接从示范数据度量模仿学习数据集的多样性：基于签名核定义熵，无需先训练模型。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.11634.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.04233", "title": "What Are We Actually Benchmarking in Robot Manipulation?", "type": "paper", "arxiv": "2606.04233", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/ripl/ManipulationBenchmarkAudit", "project": "https://ripl.github.io/manipulation_benchmark_audit/", "topics": [ "datasets" ], "tags": [ "benchmarks", "evaluation", "audit" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Oral. Audits LIBERO, CALVIN, SimplerEnv, RoboCasa and RoboTwin 2.0 against four failure modes, from shortcut solvability to creeping overfitting. LIBERO and CALVIN fail several diagnostics.", "takeaway_zh": "CoRL 2026 Oral。用捷径可解、统计不显著、渐进过拟合、数据来源依赖四种失效模式审计 LIBERO、CALVIN、SimplerEnv、RoboCasa 和 RoboTwin 2.0；其中 LIBERO 与 CALVIN 未通过多项诊断。", "github_stars": 22, "page": "https://embodied-ai-radar.com/e/arxiv-2606.04233.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2608.02580", "title": "Ego2Robot: Scalable Robot Data Synthesis from Egocentric Human Data", "type": "paper", "arxiv": "2608.02580", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://www-ye.github.io/ego2robot_blog/", "topics": [ "datasets" ], "tags": [ "egocentric", "data-synthesis", "pretraining" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. A pipeline that turns egocentric human manipulation videos into robot training data, testing whether this helps VLA pretraining at scale.", "takeaway_zh": "CoRL 2026 Spotlight。把第一视角人类操作视频转换为机器人训练数据的流程，并检验其能否大规模助力 VLA 预训练。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.02580.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.16826", "title": "ATOM-Bench: A Real-World Benchmark for Atomic Skills and Compositional Generalization in Manipulation Policies", "type": "paper", "arxiv": "2606.16826", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://flageval-baai.github.io/AtomBenchPage", "topics": [ "datasets" ], "tags": [ "benchmark", "real-world", "compositional-generalization" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Real-world benchmark that factorizes tabletop manipulation into motor and instruction atoms to test atomic skills and how policies recombine them.", "takeaway_zh": "CoRL 2026 Spotlight。真实世界基准：把桌面操作分解为动作原子与指令原子，测试策略的原子技能及其重新组合能力。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.16826.html", "labs": [], "code_license": null }, { "id": "arxiv-2510.26236", "title": "PHUMA: Physically Reliable Humanoid Locomotion Dataset", "type": "paper", "arxiv": "2510.26236", "year": 2025, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/davian-robotics/PHUMA", "project": "https://davian-robotics.github.io/PHUMA", "topics": [ "datasets" ], "tags": [ "humanoid", "motion-imitation" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-02", "takeaway_en": "CoRL 2026 Spotlight. Humanoid locomotion dataset from internet video that targets the floating, penetration and foot skating common in earlier video-derived data.", "takeaway_zh": "CoRL 2026 Spotlight。源自网络视频的人形运动数据集，针对此前视频数据常见的悬空、穿透与脚滑问题。", "github_stars": 252, "page": "https://embodied-ai-radar.com/e/arxiv-2510.26236.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.33718", "title": "RoboJev: An Open Vision-Language Judge for Robot Success Detection", "type": "paper", "arxiv": "2610.33718", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://robo-jev.com", "topics": [ "datasets" ], "tags": [ "evaluation", "open-weights", "success-detection" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "An open-weight model that judges from video frames whether a manipulation attempt succeeded, useful for labeling episodes; reports 0.89 AUROC on unseen labs and robots.", "takeaway_zh": "开放权重模型，根据视频帧判断操作是否成功，可用于标注数据；在未见过的实验室与机器人上报告 AUROC 0.89。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.33718.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.02629", "title": "DexArena: A Unified Benchmark for Long-Horizon Dexterous Manipulation", "type": "paper", "arxiv": "2611.02629", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://sipair-elastic.github.io/DexArena-website/", "topics": [ "datasets", "manipulation" ], "tags": [ "benchmark", "bimanual", "dexterous", "long-horizon", "sim" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "10 long-horizon and 30 stage-level bimanual dexterous tasks with physically grounded success conditions per stage, so partial progress is measurable; includes verified teleoperated demos.", "takeaway_zh": "10 个长时序和 30 个阶段级双手灵巧任务，每个阶段都有物理判定的成功条件，可衡量中间进度；提供经过验证的遥操作示范和统一接口。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.02629.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.00243", "title": "OmniPiano: Diverse Dexterous Piano-Playing Challenges for Standard, Robust, Safe, and Multi-Agent RL", "type": "paper", "arxiv": "2611.00243", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/SAIL-Research-Lab/omnipiano", "project": "https://sail-research-lab.github.io/omnipiano/", "topics": [ "datasets", "manipulation", "learning" ], "tags": [ "benchmark", "dexterous", "multi-agent", "safe-rl", "sim" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Extends robot piano to one to five Shadow Hands, with robust, safe and multi-agent RL settings: at least 912 task settings and 36 baselines. Reports that top RL algorithms and LLM agents still struggle.", "takeaway_zh": "把机器人弹钢琴扩展到 1 至 5 只 Shadow 手，并支持鲁棒、安全和多智能体强化学习：至少 912 种任务设置、36 个基线算法。作者报告前沿算法与大模型智能体在高难任务上仍很吃力。", "github_stars": 8, "page": "https://embodied-ai-radar.com/e/arxiv-2611.00243.html", "labs": [], "code_license": "" }, { "id": "arxiv-2611.03168", "title": "Rollout then Recycle: 3,718 Labelled Real-Robot Episodes from Benchmarking 7 Policies on 12 Tasks", "type": "paper", "arxiv": "2611.03168", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "real", "failures", "so-101", "low-cost" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Releases every episode of the ArmnetBench v0.1 evaluation on SO-101 arms: 600 teleoperated demos and 3,118 labelled policy rollouts, 73% of them failures, so many policies' mistakes can be compared on the same tasks.", "takeaway_zh": "公开 ArmnetBench v0.1 在 SO-101 机械臂上的全部回合：600 条遥操作示范和 3118 条带标注的策略执行，其中 73% 是失败，可以在同一任务上比较多种策略的错误。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03168.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.01190", "title": "ClothDojo: Learned Data Generation and Benchmarking for Bimanual Garment Flattening and Folding", "type": "paper", "arxiv": "2611.01190", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://clothdojo.github.io/", "topics": [ "datasets", "manipulation", "sim" ], "tags": [ "benchmark", "garment", "deformable", "data-generation", "sim" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Sim platform where a fine-tuned VLA generates nearly 8,000 successful garment trajectories, re-rendered for RGB policies. Those beat policies trained on the original human data, but unseen garments stay hard.", "takeaway_zh": "仿真平台：用微调后的 VLA 生成近 8000 条成功的衣物操作轨迹，并重新渲染供 RGB 策略训练，效果优于直接用人类数据训练，但泛化到未见衣物仍是难题。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.01190.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.23001", "title": "Vision-Language-Action in Robotics: A Survey of Datasets, Benchmarks, and Data Engines", "type": "paper", "arxiv": "2604.23001", "year": 2026, "venue": null, "org": "University of Maryland", "url": null, "code": null, "project": "https://github.com/ziyaow1010/vla-datasets-benchmarks", "topics": [ "datasets", "vla" ], "tags": [ "survey" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Argues VLA progress now depends more on data engines and evaluation than on architectures; finds a fidelity-versus-cost trade-off in datasets and gaps in compositional and long-horizon benchmarks.", "takeaway_zh": "认为 VLA 的进展更取决于数据引擎和评测，而不是模型架构；指出数据集存在保真度与成本的权衡，评测基准在组合任务和长时程任务上仍有缺口。", "github_stars": 39, "page": "https://embodied-ai-radar.com/e/arxiv-2604.23001.html", "labs": [], "code_license": "" }, { "id": "arxiv-2510.17950", "title": "RoboChallenge: Large-scale Real-robot Evaluation of Embodied Policies", "type": "paper", "arxiv": "2510.17950", "year": 2025, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets", "vla" ], "tags": [ "benchmark", "real", "online-evaluation" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Report on an online system for testing many robot policies on many real tasks, plus a study of recent VLAs on its first benchmark, Table30.", "takeaway_zh": "介绍一套在线系统，用于在真机上大规模评测多种机器人策略，并报告近期 VLA 在首个基准 Table30 上的表现。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2510.17950.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.40364", "title": "Is It the Curriculum or Just the Easy Start? Benchmarking Unsupervised Environment Design on Quadrupeds", "type": "paper", "arxiv": "2610.40364", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/Solvro/ml-wojtek-ued", "project": null, "topics": [ "datasets", "locomotion" ], "tags": [ "benchmark", "curriculum", "quadruped", "sim", "mjx" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "Runs fourteen curricula under one protocol on five quadrupeds in MuJoCo MJX: no method tops every robot and level set, and methods with easy training levels lead on in-distribution random terrain.", "takeaway_zh": "在 MuJoCo MJX 中用统一协议在五款四足上比较 14 种课程方法：没有方法在所有机器人和地形集上都领先，而训练关卡偏简单的方法在分布内随机地形上表现靠前。", "github_stars": 5, "page": "https://embodied-ai-radar.com/e/arxiv-2610.40364.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.05320", "title": "PyRUA-DataFarm: LLM Agent as a Cost-Efficient Teleoperator for Real-Ready Robot Data", "type": "paper", "arxiv": "2611.05320", "year": 2026, "venue": null, "org": "Peking University", "url": null, "code": "https://github.com/DAGroup-PKU/PyRUA-Lean", "project": "https://dagroup-pku.github.io/PyRUA-Lean/DataFarm", "topics": [ "datasets", "sim" ], "tags": [ "agent", "synthetic-data", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A coding agent rebuilds a task in simulation from one calibration episode, then a video model renders photographic data; reports 93 vs. 84 of 100 real successes against teleop data, at about $107 per hour of data.", "takeaway_zh": "编程智能体凭一条标定轨迹在仿真中重建任务，再由视频模型渲染成写实数据；报告真机 100 次成功 93 次，遥操作数据为 84 次，每小时数据约 107 美元。", "github_stars": 234, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05320.html", "labs": [ "pku" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2611.03850", "title": "RoboScope Kit: Unified Interfaces for Fast and Scalable Embodied-AI Evaluation", "type": "paper", "arxiv": "2611.03850", "year": 2026, "venue": null, "org": "Shanghai Jiao Tong University", "url": null, "code": "https://github.com/MINT-SJTU/RoboScope-Kit", "project": "https://roboscope.pages.dev/", "topics": [ "datasets", "vla" ], "tags": [ "benchmark", "evaluation", "tooling" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Wraps each model once and each benchmark once, then schedules simulator workers and policy replicas as separate pools; reports evaluation 4.45x to 6.16x faster than the official StarVLA evaluator on one GPU.", "takeaway_zh": "模型和基准各封装一次，把仿真 worker 与策略副本分成两个池调度；报告单卡评测比官方 StarVLA 评测器快 4.45 到 6.16 倍。", "github_stars": 61, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03850.html", "labs": [ "sjtu" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2611.05080", "title": "CRONOS: Benchmarking Non-Episodic Reinforcement Learning for Robotic Manipulation", "type": "paper", "arxiv": "2611.05080", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://embodiedai-ntu.github.io/cronos/", "topics": [ "datasets", "vla" ], "tags": [ "benchmark", "rl", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A simulation benchmark for RL fine-tuning of VLAs without frequent resets; finds naive fine-tuning fails from object and end-effector shifts, and that targeted reset allocation and bias fixes mitigate it.", "takeaway_zh": "面向无频繁重置场景下 VLA 强化学习微调的仿真基准；发现直接微调会因物体和末端位姿偏移而失败，定向分配重置并处理预训练偏差可以缓解。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05080.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.07607", "title": "EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World", "type": "paper", "arxiv": "2604.07607", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "human-data", "egocentric", "dataset" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A shared platform and dataset for human-data robot learning: 1,362 hours and 80k episodes across 1,965 tasks and 2,087 demonstrators, with contributions from labs and industry.", "takeaway_zh": "面向人类数据机器人学习的共享平台和数据集：1,362 小时、8 万条片段，覆盖 1,965 项任务和 2,087 名演示者，接受学界和业界贡献。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.07607.html", "labs": [], "code_license": null }, { "id": "arxiv-2604.19092", "title": "RoboWM-Bench: A Benchmark for Evaluating World Models in Robotic Manipulation", "type": "paper", "arxiv": "2604.19092", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets", "world-models" ], "tags": [ "benchmark", "world-models" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Evaluates video world models by whether generated manipulation can be converted into actions and executed in physics simulation, not only whether it looks realistic.", "takeaway_zh": "评估视频世界模型时不只看画面是否逼真，而是把生成的操作转成动作，在物理仿真中验证能否执行。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2604.19092.html", "labs": [], "code_license": null }, { "id": "arxiv-2511.02832", "title": "TWIST2: Scalable, Portable, and Holistic Humanoid Data Collection System", "type": "paper", "arxiv": "2511.02832", "year": 2025, "venue": null, "org": null, "url": null, "code": "https://github.com/amazon-far/TWIST2", "project": "https://twist-data.github.io", "topics": [ "datasets", "locomotion" ], "tags": [ "humanoid", "teleoperation", "data-collection" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Portable, mocap-free humanoid teleoperation and data collection built around a VR headset, aimed at making whole-body demonstrations scale.", "takeaway_zh": "基于 VR 头显的便携式免动捕人形遥操作与数据采集系统，目标是让全身示教数据能够规模化采集。", "github_stars": 895, "page": "https://embodied-ai-radar.com/e/arxiv-2511.02832.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.05567", "title": "MESA: An Evaluation Framework for Compositional, Semantic, and Spatial Generalization in Robotics", "type": "paper", "arxiv": "2611.05567", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/pairlab/mesa-env", "project": "https://pairlab.github.io/MESA", "topics": [ "datasets", "manipulation" ], "tags": [ "benchmark", "generalization", "bimanual", "sim", "vla" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Suites isolate generalisation across spatial layout, object instance, object category and subtask composition, plus bimanual BiMESA. The best of seven baselines reaches only 52% and 49%.", "takeaway_zh": "五组评测分别隔离空间布局、物体实例、物体类别和子任务组合上的泛化能力，并提供双臂版 BiMESA。七种基线中最好的也只有 52% 和 49%，仍有很大提升空间。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2611.05567.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.06911", "title": "BEAKER: An Expert-Curated Benchmark for Embodied Brains in Self-Driving Chemical Laboratories", "type": "paper", "arxiv": "2611.06911", "year": 2026, "venue": "NeurIPS 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "benchmark", "multimodal-llm", "lab-automation" ], "robot_types": [ "vehicle" ], "highlight": false, "rating": 1, "added": "2026-11-05", "takeaway_en": "1,000 expert-curated questions over 11 image and video task types test multimodal LLMs as the brain of a self-driving chemistry lab: affordances, trajectories, apparatus setup, safety and step reasoning.", "takeaway_zh": "1000 道专家整理的题目、11 种图像和视频任务，检验多模态大模型能否担任自动化化学实验室的“大脑”：可供性、轨迹、仪器搭建、安全与步骤推理。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.06911.html", "labs": [], "code_license": null }, { "id": "arxiv-2304.04321", "title": "ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes", "type": "paper", "arxiv": "2304.04321", "year": 2023, "venue": null, "org": null, "url": null, "code": "https://github.com/arnold-benchmark/arnold", "project": "https://arnold-benchmark.github.io", "topics": [ "datasets" ], "tags": [ "benchmark", "language", "continuous-states" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Benchmark for language-grounded tasks with continuous object goal states in realistic 3D scenes, where most suites assume binary goals.", "takeaway_zh": "面向语言任务的基准，目标是连续物体状态（多数基准只有二值目标），场景为真实感三维环境。", "github_stars": 187, "page": "https://embodied-ai-radar.com/e/arxiv-2304.04321.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2402.02500", "title": "Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning", "type": "paper", "arxiv": "2402.02500", "year": 2024, "venue": "NeurIPS 2024", "org": null, "url": null, "code": "https://github.com/HaoyiZhu/PointCloudMatters", "project": null, "topics": [ "datasets" ], "tags": [ "benchmark", "observation-space", "point-cloud" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "OBSBench compares RGB, RGB-D and point cloud observations for robot learning under one setup.", "takeaway_zh": "OBSBench 在统一设置下比较 RGB、RGB-D 和点云观测对机器人学习的影响。", "github_stars": 92, "page": "https://embodied-ai-radar.com/e/arxiv-2402.02500.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.06453", "title": "Beyond Infraction Scores: Failure-Boundary Profiles for Closed-Loop Evaluation of Driving and Robot Navigation Agents", "type": "paper", "arxiv": "2611.06453", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/speedym/failure-boundary-profiles", "project": null, "topics": [ "datasets", "navigation" ], "tags": [ "benchmark", "driving", "evaluation", "isaac-sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-05", "takeaway_en": "Sweeps one measured factor per scenario and certifies whether an agent should proceed or stop; finds the top learned CARLA agent at 0.60 decision accuracy against 0.74 for the rule-based expert.", "takeaway_zh": "每类场景只改变一个可测因素，并认证智能体此时该通过还是该停下；CARLA 中得分最高的学习型智能体决策准确率为 0.60，规则专家为 0.74。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2611.06453.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2609.25636", "title": "RoboFollow: Unveiling the Instruction Following Mirage in Embodied Agents", "type": "paper", "arxiv": "2609.25636", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/AutoLab-SAI-SJTU/RoboFollow", "project": null, "topics": [ "datasets", "vla" ], "tags": [ "benchmark", "instruction-following", "diagnostic", "wam" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Benchmark showing VLA success often hides weak instruction following: scenes allow several valid tasks so language matters. Nine VLA and WAM policies drop sharply beyond the base level.", "takeaway_zh": "揭示 VLA 的高成功率常掩盖薄弱的指令遵循：场景支持多种合法任务，必须依赖语言。九个 VLA 与 WAM 策略在基础级以外的测试中明显下降。", "github_stars": 8, "page": "https://embodied-ai-radar.com/e/arxiv-2609.25636.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.05414", "title": "RMMBench: A Comprehensive Benchmark for Robotic Mobile Manipulation", "type": "paper", "arxiv": "2610.05414", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/mxxq-stack/RMMBench", "project": "https://mxxq-stack.github.io/rmmbench-project/", "topics": [ "datasets", "navigation" ], "tags": [ "benchmark", "mobile-manipulation", "vlm", "long-horizon" ], "robot_types": [ "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Mobile-manipulation benchmark with 70 task scenarios combining navigation and manipulation in continuous spaces. Finds leading VLMs still struggle with spatial localization.", "takeaway_zh": "移动操作评测基准：70 个在连续空间中结合导航与操作的任务场景。发现主流 VLM 在空间定位上仍有明显困难。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2610.05414.html", "labs": [], "code_license": "" }, { "id": "arxiv-2609.12433", "title": "FoldNet++: a Large-Scale Synthetic Dataset for Robotic T-Shirt Folding and Unfolding", "type": "paper", "arxiv": "2609.12433", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/GalaxyGeneralRobotics/FoldNetXX", "project": "https://pku-epic.github.io/FoldNetXX/", "topics": [ "datasets", "manipulation", "sim" ], "tags": [ "cloth", "synthetic-data", "folding", "sim2real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Synthetic T-shirt folding and unfolding data: 6 robots, 1K shirts, 120K episodes. Policies trained only on it are reported above 90% success on unseen real shirts.", "takeaway_zh": "合成 T 恤折叠与展开数据：6 种机器人、1000 件 T 恤、12 万条轨迹。报告仅用其训练的策略在未见过的真实 T 恤上成功率超过 90%。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2609.12433.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2606.27375", "title": "Scalable Behavior Cloning with Open Data, Training, and Evaluation", "type": "paper", "arxiv": "2606.27375", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/amazon-far/abc", "project": "https://abc.bot", "topics": [ "datasets" ], "tags": [ "teleoperation", "open-source", "behavior-cloning", "sim-real-cotraining", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Fully open behavior-cloning stack: ABC-130K (3,500 hours, 130K episodes, 195 tasks), hardware, training code and a sim co-training recipe whose sim scores track real-world results.", "takeaway_zh": "完全开源的行为克隆全栈：ABC-130K 遥操作数据集（3500 小时、13 万条、195 个任务），外加硬件、训练代码和仿真协同训练方案，其仿真评测结果与真机表现相关。", "github_stars": 398, "page": "https://embodied-ai-radar.com/e/arxiv-2606.27375.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2605.31196", "title": "Probing Collision Grounding in Vision-Language Models for Safe Human-Robot Collaboration", "type": "paper", "arxiv": "2605.31196", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/jun25336-bit/TouchSafeBench", "project": "https://jun25336-bit.github.io/TouchSafeBench/", "topics": [ "datasets" ], "tags": [ "benchmark", "safety", "vlm", "collision", "habitat" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "TouchSafeBench: 2,940 Habitat 3.0 episodes testing whether VLMs can tell if a robot is colliding or about to collide with people or the scene. Best average Macro-F1 stays below 50%.", "takeaway_zh": "TouchSafeBench：基于 Habitat 3.0 的 2940 个场景，测试 VLM 能否判断机器人正在或即将与人或环境碰撞。最佳平均 Macro-F1 仍低于 50%。", "github_stars": 0, "page": "https://embodied-ai-radar.com/e/arxiv-2605.31196.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2603.05504", "title": "RoboPocket: Improve Robot Policies Instantly with Your Phone", "type": "paper", "arxiv": "2603.05504", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://robo-pocket.github.io", "topics": [ "datasets" ], "tags": [ "data-collection", "smartphone", "augmented-reality", "dagger", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Smartphone app for robot-free policy iteration: shows the current policy's predicted trajectory in AR so users collect corrective data where it fails. Reports up to 2x sample efficiency over offline demos.", "takeaway_zh": "基于手机的免机器人策略迭代系统：用 AR 显示当前策略的预测轨迹，用户可在策略薄弱处采集纠正数据。报告样本效率最高为纯离线演示的 2 倍。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.05504.html", "labs": [], "code_license": null }, { "id": "arxiv-2509.21651", "title": "Can AI Perceive Physical Danger and Intervene?", "type": "paper", "arxiv": "2509.21651", "year": 2025, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://asimov-benchmark.github.io/v2", "topics": [ "datasets" ], "tags": [ "benchmark", "safety", "foundation-models", "injury-data", "video" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "ASIMOV v2: physical-safety benchmark built from real injury narratives turned into generated images and videos, testing whether foundation models notice danger and intervene, plus safety-reasoning post-training.", "takeaway_zh": "ASIMOV v2：把真实伤害事故描述转成生成的图像与视频，构建可持续扩展的物理安全基准，测试基础模型能否察觉危险并干预；并提出安全推理后训练方法。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2509.21651.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.17694", "title": "HelpMeOut: A Neuropsychology-Grounded Benchmark for Error-Aware Monitoring of Household Task Execution", "type": "paper", "arxiv": "2610.17694", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://whrcn.github.io/HelpMeOut-Official/", "topics": [ "datasets" ], "tags": [ "benchmark", "assistive", "error-monitoring", "household", "llm" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "HelpMeOut: 20,000 paired household activity episodes with nine neuropsychology-based error types; agents must spot when a person's task execution goes wrong. Learned, symbolic and LLM monitors all do poorly.", "takeaway_zh": "HelpMeOut：2 万对家务活动片段，涵盖九类基于神经心理学的执行错误，要求智能体尽早发现人做事出错。学习型、符号型与大模型监控方法表现都较差。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.17694.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.08209", "title": "Monkey See, Can Monkey Do? A Benchmark for Evaluating Robot Skill Learning by Observation", "type": "paper", "arxiv": "2609.08209", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://roboreel.github.io", "topics": [ "datasets" ], "tags": [ "benchmark", "learning-from-observation", "human-video", "long-horizon", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "RoboReel: a unified benchmark for policies learned from human videos, bundling real human demos, simulated robot trajectories and ten tasks with four test suites; sim trends hold on real-robot recreations.", "takeaway_zh": "RoboReel：从人类视频学习策略的统一基准，打包真实人类演示视频、仿真机器人轨迹与十个任务及四套测试；在真机复现中仿真结论保持一致。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.08209.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.16222", "title": "HiPHI: A Large-Scale Benchmark for High-Precision Human Motion and Object-Interaction", "type": "paper", "arxiv": "2608.16222", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://noitom-robotics.github.io/hiphi/", "topics": [ "datasets" ], "tags": [ "human-motion", "mocap", "humanoid", "object-interaction", "benchmark" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "600+ hours of whole-body human motion and object interaction from optical mocap with sub-millimetre marker accuracy, organised by FrameNet primitives, with a benchmark suite for humanoid learning.", "takeaway_zh": "600 余小时的高精度全身人体运动与物体交互数据，由光学动捕采集（亚毫米级标记精度），按 FrameNet 动作基元组织，并配套面向人形机器人学习的评测套件。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2608.16222.html", "labs": [], "code_license": null }, { "id": "arxiv-2608.22301", "title": "The Imitator Game: Benchmarking Robot Imitative Ability Beyond Action Prediction", "type": "paper", "arxiv": "2608.22301", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/imitator-game/The-Imitator-Game", "project": "https://imitator-game.github.io", "topics": [ "datasets" ], "tags": [ "benchmark", "imitation", "human-video", "intent", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Four-level benchmark that widens the gap between a human demo and the robot's scene, plus IG-10K (20,000+ paired human-robot episodes). Nine models collapse at the hardest level; all stay below 13% zero-shot.", "takeaway_zh": "四级基准逐步拉大人类演示与机器人场景的差异，并发布 IG-10K（2 万余条人-机配对片段）。九个模型在最高级别全面失效，未见任务零样本成功率均低于 13%。", "github_stars": 66, "page": "https://embodied-ai-radar.com/e/arxiv-2608.22301.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2607.15641", "title": "IMBench: A Benchmark for Intuitive Robotic Manipulation", "type": "paper", "arxiv": "2607.15641", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://imbench.org/", "topics": [ "datasets" ], "tags": [ "benchmark", "physical-reasoning", "tool-use", "vla", "vlm" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "IMBench: 35 tasks and 14K trajectories testing reasoning and execution together under explicit constraints. VLMs reason partly but fail to plan executable steps; VLAs struggle with the constraints.", "takeaway_zh": "IMBench：35 个任务、1.4 万条轨迹，在显式约束下同时考察物理推理与动作执行。VLM 有部分推理能力但给不出可执行计划，VLA 难以满足任务约束。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.15641.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.19333", "title": "Do as I Do: Dexterous Manipulation Data from Everyday Human Videos", "type": "paper", "arxiv": "2606.19333", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/malik-group/do-as-i-do", "project": "https://do-as-i-do.com/", "topics": [ "datasets" ], "tags": [ "human-video", "retargeting", "dexterous", "hand-object", "monocular" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Reconstructs hand-object interaction from ordinary monocular human videos and retargets it into executable multi-fingered robot actions; beats prior work on hand-object estimation and gives a data-collection playbook.", "takeaway_zh": "从普通单目人类视频重建手-物交互，并重定向为多指机器人手可执行的动作序列；手-物估计优于已有方法，并给出采集人类数据的实践建议。", "github_stars": 455, "page": "https://embodied-ai-radar.com/e/arxiv-2606.19333.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.22142", "title": "RoboLineage: Agent-Native Data Lifecycle Governance Across Robot Policy Iterations", "type": "paper", "arxiv": "2606.22142", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/robolineage/robolineage", "project": "https://robolineage.github.io/", "topics": [ "datasets" ], "tags": [ "data-management", "agents", "policy-iteration", "lineage", "open-source" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Open-source data lifecycle layer for robot policy iteration: rollouts, reviews, datasets, training runs and evaluations become typed lineage artifacts that agents maintain, making iteration faster and auditable.", "takeaway_zh": "开源的机器人策略迭代数据生命周期管理层：把执行记录、审核、数据集决策、训练与评测都表示为带类型的溯源工件，由智能体维护，让迭代更快、更可追溯。", "github_stars": 8, "page": "https://embodied-ai-radar.com/e/arxiv-2606.22142.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2606.10244", "title": "YUBI: Yielding Universal Bidigital Interface for Bimanual Dexterous Manipulation at Scale", "type": "paper", "arxiv": "2606.10244", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/airoa-org/yubi-sw", "project": "https://yubi.airoa.io/", "topics": [ "datasets" ], "tags": [ "umi", "data-collection", "bimanual", "gripper", "open-source" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Finger-driven handheld gripper for UMI-style data collection plus an 8,434-hour, 1.20M-episode, 119-task dataset; one policy transfers across UR, Franka and ELEY by mounting the gripper. Hardware and data released.", "takeaway_zh": "手指驱动的手持夹爪，用于 UMI 式数据采集，并发布 8434 小时、120 万条、119 个任务的数据集；只需装上同款夹爪，同一策略即可迁移到 UR、Franka 与 ELEY。开源硬件与数据。", "github_stars": 59, "page": "https://embodied-ai-radar.com/e/arxiv-2606.10244.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2607.13472", "title": "EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal", "type": "paper", "arxiv": "2607.13472", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/leggedrobotics/egohtr", "project": "https://egohtr.github.io", "topics": [ "datasets" ], "tags": [ "human-motion", "egocentric", "terrain", "humanoid", "unitree-g1" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "55 scene-aligned 4D human motion sequences (150k+ frames) on rough terrain from egocentric wearables and a portable scanner, with an open pipeline; used to train perceptive locomotion run on a Unitree G1.", "takeaway_zh": "利用第一人称可穿戴设备与便携式扫描仪采集 55 段与场景对齐的复杂地形 4D 人体运动（超过 15 万帧），并开源重建流程；用于训练感知式运动策略，部署到 Unitree G1。", "github_stars": 11, "page": "https://embodied-ai-radar.com/e/arxiv-2607.13472.html", "labs": [], "code_license": "" }, { "id": "arxiv-2602.16444", "title": "RoboGene: Diversity-Driven Agentic Task Generation Framework for VLA Pre-training", "type": "paper", "arxiv": "2602.16444", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": "https://robogene-boost-vla.github.io/", "topics": [ "datasets" ], "tags": [ "task-generation", "agents", "data-collection", "vla-pretraining", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Agentic generator of diverse, feasible manipulation tasks for data collection, with self-reflection and human-verified memory; 18k real trajectories collected. VLAs trained on its data generalize better.", "takeaway_zh": "为数据采集自动生成多样且可执行的操作任务的智能体框架，结合自我反思与人工验证记忆；据此采集 1.8 万条真机轨迹，用其训练的 VLA 成功率与泛化更好。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2602.16444.html", "labs": [], "code_license": null }, { "id": "arxiv-2607.19695", "title": "NavVerse: Benchmarking Indoor-to-Outdoor Embodied Navigation in Continuous Robot Simulation", "type": "paper", "arxiv": "2607.19695", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "benchmark", "navigation", "indoor-outdoor", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Physics-based benchmark for navigating from buildings to streets in one episode: 200 scenes, 10,000 ObjectNav / VLN / PlaceNav episodes. Zero-shot VLAs succeed most; the modular method is safest.", "takeaway_zh": "物理仿真基准，考察在一个连续回合中从室内走到街道：200 个场景、1 万个 ObjectNav / VLN / 地点导航回合。零样本测试中端到端 VLA 成功率最高，模块化方法最安全。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2607.19695.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.23689", "title": "AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection", "type": "paper", "arxiv": "2606.23689", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "dexterous-grasping", "data-collection", "automation", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "AutoDex runs real dexterous-grasp trials, labels outcomes and resets objects with little human help: 3,500+ trials on 100 objects (Allegro, Inspire); about 0.2 human-hours per 500 trajectories vs 9.3 by teleop.", "takeaway_zh": "AutoDex 自动执行真机灵巧抓取、标注结果并复位物体，几乎无需人工：在 100 个物体上用 Allegro 与 Inspire 手采集 3500 多次试验；每 500 条轨迹约需 0.2 人时，遥操作需 9.3 人时。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.23689.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.06279", "title": "IM-ENGINE: Image Editing for Embodied Data Generation", "type": "paper", "arxiv": "2609.06279", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "data-generation", "image-editing", "dexterous-grasping", "sim" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Uses image editing of simulator renders as a middle step: edit in task semantics, recover 3D state from simulator priors, refine in physics, and turn it into robot supervision, e.g. dexterous grasps and goal states.", "takeaway_zh": "以对仿真渲染图的图像编辑为中间表示：先编辑注入任务语义，再借助仿真先验恢复 3D 状态并在物理中细化，最后转为机器人可执行的监督，如灵巧抓取与目标状态。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.06279.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.16208", "title": "ATHENA: Accelerated Multi-Task Heterogeneous Influence Functions for Robot Data Curation", "type": "paper", "arxiv": "2606.16208", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "data-curation", "influence-functions", "vla", "robotwin" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Influence functions at billion-parameter VLA scale (about 313x faster via Kronecker structure and a rank-r Hessian approximation) for multi-task data curation; matches full-data fine-tuning with 50% of demos in sim.", "takeaway_zh": "将影响函数扩展到十亿参数级 VLA（借助 Kronecker 结构与秩 r 的 Hessian 近似提速约 313 倍）用于多任务数据筛选；在仿真中只用 50% 示教即可达到全量数据微调的效果。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.16208.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.24778", "title": "H2RBench: A Real-to-Sim Benchmark for Evaluating Human-to-Robot Transfer", "type": "paper", "arxiv": "2609.24778", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "benchmark", "human-to-robot", "real2sim", "human-video" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Real-to-sim benchmark for human-to-robot transfer: real human video demos plus simulated robot demos on four tasks, with a fixed protocol to compare how H2R methods scale with more human data.", "takeaway_zh": "评估“人到机器人”迁移的 Real2Sim 基准：四个任务配有真实人类视频示教和仿真机器人示教，以统一协议比较各方法随人类数据增加的扩展能力。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.24778.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.13497", "title": "SPARC: Reliable Spatial Annotations from Robot Demonstrations at Scale", "type": "paper", "arxiv": "2606.13497", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "annotation", "spatial-labels", "reliability", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Auto-labels robot demos with boxes, object trajectories and phase labels, plus a reliability score from the task's spatio-temporal structure, so fewer noisy labels are kept and fewer good ones are thrown out.", "takeaway_zh": "为机器人示教自动标注边界框、物体轨迹与操作阶段，并利用任务的时空结构为每个标注给出可靠性分数，从而少留噪声标签、少丢有用样本。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.13497.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.03091", "title": "KlaskTron-IL: A Contact-Rich Adversarial Imitation Learning Benchmark with a Validated Sim-to-Real Bridge", "type": "paper", "arxiv": "2611.03091", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets" ], "tags": [ "benchmark", "adversarial", "imitation-learning", "open-hardware", "sim-to-real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "An imitation-learning benchmark on the open KlaskTron table game robot: 80k real human demo steps, augmented and synthetic data, and baselines exposing BC and GAIL failure modes; policies run on hardware unchanged.", "takeaway_zh": "基于开源 KlaskTron 桌面对抗游戏机器人的模仿学习基准：8 万步真实人类示教、对称增强与合成数据，以及揭示 BC 和 GAIL 失效模式的基线；训练出的策略可直接上真机运行。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03091.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.09036", "title": "ToolDex: An Ego-Exo Human Demonstration Dataset for Creative Physical Tool Use", "type": "paper", "arxiv": "2611.09036", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://tooluse-dataset.github.io/", "topics": [ "datasets", "manipulation" ], "tags": [ "tool-use", "ego-exo", "human-demonstration", "benchmark" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-09", "takeaway_en": "Human demos of creative tool use (repurposing objects when the usual tool is missing) from 1 ego + 8 exo cameras with hand, tool and object labels; benchmarks reasoning, 3D/4D reconstruction and policy learning.", "takeaway_zh": "记录人类创造性使用工具（缺少常规工具时改用其他物体）的示范，含一路第一视角和八路第三视角相机及手、工具、物体标注；基准涵盖推理、三维/四维重建与策略学习。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.09036.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.08946", "title": "Flex-RSI: Benchmarking Recursive Self-Improvement of Frontier Agents", "type": "paper", "arxiv": "2611.08946", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/flex-rsi/rsi-bench", "project": "https://flex-rsi.github.io", "topics": [ "datasets" ], "tags": [ "agents", "self-improvement", "benchmark", "harness" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-09", "takeaway_en": "Agents revise their own harness under a fixed budget on vision and robotics tasks; self-improvement helps most when the agent builds its own solution; open-weight models lag most under strict physical checks.", "takeaway_zh": "评测智能体在固定预算下改写自身框架的能力，任务涵盖视觉与机器人；发现智能体自建解法时自我改进收益最大，开源权重模型在需通过物理稳定性等严格检查的任务上落后最多。", "github_stars": 10, "page": "https://embodied-ai-radar.com/e/arxiv-2611.08946.html", "labs": [], "code_license": "" }, { "id": "arxiv-2611.11123", "title": "HARP-Bench: Benchmarking Robot Manipulation Around Active Humans", "type": "paper", "arxiv": "2611.11123", "year": 2026, "venue": "CoRL 2026", "org": "UMass Amherst", "url": null, "code": "https://github.com/UMass-Embodied-AGI/harp", "project": null, "topics": [ "datasets", "manipulation" ], "tags": [ "benchmark", "human-robot-interaction", "safety", "genesis", "sim" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-10", "takeaway_en": "Manipulation benchmark where simulated humans cue goals, finish parts of the task, interrupt or share the workspace. Reports that VLA and IL policies drop when humans act, and higher success does not mean safer motion.", "takeaway_zh": "让仿真人类参与其中的操作基准：人会提示目标、完成部分任务、打断或与机器人共处同一工作区。报告显示 VLA 与模仿学习策略在有人活动时性能下降，且成功率更高并不意味着更安全。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2611.11123.html", "labs": [], "code_license": "" }, { "id": "arxiv-2611.10994", "title": "RoboMoCap: A Large-Scale Whole-Body Motion Capture Dataset with Egocentric Vision for Robot Learning", "type": "paper", "arxiv": "2611.10994", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://bitrobot-foundation.github.io/robomocap/", "topics": [ "datasets", "locomotion" ], "tags": [ "dataset", "motion-capture", "egocentric", "humanoid", "loco-manipulation", "retargeting" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-10", "takeaway_en": "200+ hours from 14 people: six head cameras, wrist vision and IMU, plus 120 Hz optical full-body mocap, covering walking, terrain and carrying. Benchmarks body reconstruction, foot contact and humanoid retargeting.", "takeaway_zh": "14 名参与者、200 多小时数据：六个头戴相机、腕部视觉与惯性传感，加 120 Hz 光学全身动捕，覆盖行走、地形穿越与搬运等。提供全身重建、足部接触估计与人形重定向三项基准。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.10994.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.12804", "title": "Benchmarking Real-World Robotic Manipulation through Stochastic Challenges", "type": "paper", "arxiv": "2611.12804", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "datasets", "manipulation" ], "tags": [ "benchmark", "real-world", "reproducibility", "mobile-manipulation" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-10", "takeaway_en": "'Stochastic challenges': reproducible real scenes with tasks sampled from a shared distribution, so robots in different labs compare by success rate like a hidden test set. Shown with two robots in two labs.", "takeaway_zh": "提出“随机挑战”评测范式：可复现的真实场景 + 从同一分布随机抽取任务，让不同实验室的机器人能像隐藏测试集一样按成功率比较。在一个移动操作基准上用两个实验室的两台机器人做了演示。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.12804.html", "labs": [], "code_license": null }, { "id": "arxiv-2309.06440", "title": "LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning", "type": "hardware", "arxiv": "2309.06440", "year": 2023, "venue": null, "org": "CMU", "url": null, "code": "https://github.com/leap-hand/LEAP_Hand_API", "project": "https://leaphand.com/", "topics": [ "hardware", "manipulation" ], "tags": [ "hand", "low-cost", "open-source" ], "robot_types": [ "hand" ], "highlight": true, "rating": 3, "added": null, "takeaway_en": "A 16-DoF dexterous hand buildable for about $2,000 from off-the-shelf parts.", "takeaway_zh": "16 自由度灵巧手，用现成零件约 2000 美元即可搭建。", "github_stars": 179, "page": "https://embodied-ai-radar.com/e/arxiv-2309.06440.html", "labs": [ "cmu" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2309.13037", "title": "GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators", "type": "hardware", "arxiv": "2309.13037", "year": 2023, "venue": null, "org": "UC Berkeley", "url": null, "code": "https://github.com/wuphilipp/gello_software", "project": null, "topics": [ "hardware" ], "tags": [ "teleoperation", "low-cost", "3d-printed" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "3D-printed kinematic replicas of robot arms for cheap, intuitive joint-space teleoperation.", "takeaway_zh": "3D 打印的机械臂运动学复刻件，实现低成本、直观的关节空间遥操作。", "github_stars": 562, "page": "https://embodied-ai-radar.com/e/arxiv-2309.13037.html", "labs": [ "berkeley" ], "code_license": "MIT" }, { "id": "arxiv-2407.01512", "title": "Open-TeleVision: Teleoperation with Immersive Active Visual Feedback", "type": "hardware", "arxiv": "2407.01512", "year": 2024, "venue": null, "org": "UC San Diego, MIT", "url": null, "code": "https://github.com/OpenTeleVision/TeleVision", "project": null, "topics": [ "hardware", "locomotion" ], "tags": [ "teleoperation", "vr", "humanoid" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "VR teleoperation with an actively steered stereo camera, used to collect humanoid manipulation data.", "takeaway_zh": "带主动转向立体相机的 VR 遥操作系统，用于采集人形机器人操作数据。", "github_stars": 1321, "page": "https://embodied-ai-radar.com/e/arxiv-2407.01512.html", "labs": [ "mit", "ucsd" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2407.21781", "title": "Berkeley Humanoid: A Research Platform for Learning-based Control", "type": "hardware", "arxiv": "2407.21781", "year": 2024, "venue": null, "org": "UC Berkeley", "url": null, "code": null, "project": null, "topics": [ "hardware", "locomotion" ], "tags": [ "humanoid", "low-cost" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A mid-size, low-cost humanoid designed for easy sim-to-real transfer of RL locomotion.", "takeaway_zh": "中等尺寸、低成本的人形机器人，专为强化学习运动策略的 sim-to-real 迁移设计。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2407.21781.html", "labs": [ "berkeley" ], "code_license": null }, { "id": "arxiv-2504.17249", "title": "Demonstrating Berkeley Humanoid Lite: An Open-source, Accessible, and Customizable 3D-printed Humanoid Robot", "type": "hardware", "arxiv": "2504.17249", "year": 2025, "venue": "RSS 2025", "org": "UC Berkeley", "url": null, "code": "https://github.com/HybridRobotics/Berkeley-Humanoid-Lite", "project": "https://lite.berkeley-humanoid.org", "topics": [ "hardware", "locomotion" ], "tags": [ "humanoid", "open-source", "3d-printed", "low-cost" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Open-source humanoid with 3D-printed modular gearboxes, buildable for under US$5,000; hardware designs, embedded code and training code are released.", "takeaway_zh": "开源人形机器人，采用 3D 打印的模块化减速器，在美国可用不到 5000 美元搭建；硬件设计、嵌入式代码与训练框架均已开源。", "github_stars": 2010, "page": "https://embodied-ai-radar.com/e/arxiv-2504.17249.html", "labs": [ "berkeley" ], "code_license": "MIT" }, { "id": "so-arm100-so-101", "title": "SO-ARM100 / SO-101", "type": "hardware", "arxiv": null, "year": 2024, "venue": null, "org": "The Robot Studio, Hugging Face", "url": null, "code": "https://github.com/TheRobotStudio/SO-ARM100", "project": null, "topics": [ "hardware" ], "tags": [ "arm", "low-cost", "3d-printed", "open-source" ], "robot_types": [], "highlight": false, "rating": 1, "added": null, "takeaway_en": "An open 3D-printed 6-DoF arm (leader + follower) costing roughly $100–200 per arm; the default LeRobot starter kit.", "takeaway_zh": "开源 3D 打印 6 自由度机械臂（主从臂），每只约 100–200 美元，是 LeRobot 的默认入门套件。", "github_stars": 8077, "page": "https://embodied-ai-radar.com/e/so-arm100-so-101.html", "labs": [ "hugging-face" ], "code_license": "Apache-2.0" }, { "id": "arxiv-2502.00893", "title": "ToddlerBot: Open-Source ML-Compatible Humanoid Platform for Loco-Manipulation", "type": "hardware", "arxiv": "2502.00893", "year": 2025, "venue": null, "org": "Stanford", "url": null, "code": "https://github.com/hshi74/toddlerbot", "project": "https://toddlerbot.github.io/", "topics": [ "hardware", "locomotion" ], "tags": [ "humanoid", "low-cost", "open-source", "3d-printed" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A small, 3D-printed open-source humanoid with a matching simulation model, built so that ML policies and teleoperated data transfer to the real robot.", "takeaway_zh": "3D 打印的小型开源人形机器人，配套仿真模型，便于机器学习策略与遥操作数据迁移到真机。", "github_stars": 804, "page": "https://embodied-ai-radar.com/e/arxiv-2502.00893.html", "labs": [ "stanford" ], "code_license": "MIT" }, { "id": "arxiv-2503.05652", "title": "BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities", "type": "hardware", "arxiv": "2503.05652", "year": 2025, "venue": null, "org": "Stanford", "url": null, "code": null, "project": "https://behavior-robot-suite.github.io/", "topics": [ "hardware", "manipulation" ], "tags": [ "mobile-manipulation", "whole-body", "teleoperation", "bimanual" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A whole-body teleoperation interface plus a learning method for a wheeled bimanual robot doing long household tasks that need reach, balance and navigation.", "takeaway_zh": "面向轮式双臂机器人的全身遥操作接口与学习方法，用于需要伸展、平衡与导航的长程家务任务。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2503.05652.html", "labs": [ "stanford" ], "code_license": null }, { "id": "arxiv-2405.02292", "title": "ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation", "type": "hardware", "arxiv": "2405.02292", "year": 2024, "venue": null, "org": "Google DeepMind", "url": null, "code": "https://github.com/tonyzhaozh/aloha", "project": "https://aloha-2.github.io/", "topics": [ "hardware", "manipulation" ], "tags": [ "bimanual", "teleoperation", "low-cost", "open-source" ], "robot_types": [ "arm" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Upgraded ALOHA bimanual rig (stronger grippers, gravity compensation, sturdier frame) with open hardware designs and a MuJoCo model for simulation.", "takeaway_zh": "ALOHA 双臂平台的升级版（更强夹爪、重力补偿、更稳固的框架），开源硬件设计并提供 MuJoCo 仿真模型。", "github_stars": 2365, "page": "https://embodied-ai-radar.com/e/arxiv-2405.02292.html", "labs": [ "google-deepmind" ], "code_license": "MIT" }, { "id": "arxiv-2109.10892", "title": "The Design of Stretch: A Compact, Lightweight Mobile Manipulator for Indoor Human Environments", "type": "hardware", "arxiv": "2109.10892", "year": 2021, "venue": null, "org": "Hello Robot", "url": null, "code": null, "project": null, "topics": [ "hardware", "navigation" ], "tags": [ "mobile-manipulator", "home", "assistive" ], "robot_types": [ "arm", "mobile" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "The design of Hello Robot's Stretch — a slim, lightweight mobile manipulator with a telescoping arm on a lift, built for homes and used widely in home-robot research.", "takeaway_zh": "Hello Robot 的 Stretch 设计：在升降柱上装伸缩臂的轻巧移动操作机器人，面向家庭环境，被大量家庭机器人研究采用。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2109.10892.html", "labs": [], "code_license": null }, { "id": "arxiv-2504.04259", "title": "ORCA: An Open-Source, Reliable, Cost-Effective, Anthropomorphic Robotic Hand for Uninterrupted Dexterous Task Learning", "type": "hardware", "arxiv": "2504.04259", "year": 2025, "venue": null, "org": "ETH Zurich", "url": null, "code": null, "project": "https://www.orcahand.com", "topics": [ "hardware", "manipulation", "tactile" ], "tags": [ "hand", "open-source", "tendon-driven", "low-cost" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A 3D-printable, 17-DoF tendon-driven hand with fingertip tactile sensing, under CHF 2,000 in materials; design files and code are open, and the paper reports long continuous runs for teleoperation and learning.", "takeaway_zh": "可 3D 打印的 17 自由度腱驱动灵巧手，指尖带触觉传感，材料成本低于 2000 瑞士法郎；设计文件与代码开源，论文报告了可长时间连续运行用于遥操作与学习。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2504.04259.html", "labs": [ "eth" ], "code_license": null }, { "id": "arxiv-2502.13013", "title": "HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit", "type": "hardware", "arxiv": "2502.13013", "year": 2025, "venue": "RSS 2025", "org": "Shanghai AI Lab · CUHK", "url": null, "code": "https://github.com/InternRobotics/OpenHomie", "project": "https://homietele.github.io/", "topics": [ "hardware", "locomotion" ], "tags": [ "teleoperation", "humanoid", "exoskeleton", "low-cost" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Open-source whole-body humanoid teleoperation — an arm exoskeleton plus motion-sensing gloves, with a foot pedal steering an RL walking controller; the cockpit hardware costs about US$500.", "takeaway_zh": "开源的人形机器人全身遥操作系统：手臂外骨骼加动作捕捉手套，脚踏板驱动强化学习行走控制器；整套座舱硬件约 500 美元。", "github_stars": 626, "page": "https://embodied-ai-radar.com/e/arxiv-2502.13013.html", "labs": [ "shanghai-ai-lab", "cuhk" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2307.04577", "title": "AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System", "type": "hardware", "arxiv": "2307.04577", "year": 2023, "venue": "RSS 2023", "org": "UC San Diego, NVIDIA", "url": null, "code": null, "project": null, "topics": [ "hardware", "manipulation" ], "tags": [ "teleoperation", "hand", "vision-based", "retargeting" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Camera-only teleoperation for arm + dexterous hand pairs - tracks the operator's hand and retargets it to many different robot hands, in sim or on real hardware.", "takeaway_zh": "仅用相机即可遥操作\"机械臂 + 灵巧手\"：跟踪操作者手部并重定向到多种不同的机器人手，可用于仿真或真机。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2307.04577.html", "labs": [ "nvidia", "ucsd" ], "code_license": null }, { "id": "arxiv-2309.14975", "title": "AirExo: Low-Cost Exoskeletons for Learning Whole-Arm Manipulation in the Wild", "type": "hardware", "arxiv": "2309.14975", "year": 2023, "venue": "ICRA 2024", "org": "Shanghai Jiao Tong University", "url": null, "code": "https://github.com/AirExo/collector", "project": null, "topics": [ "hardware", "manipulation" ], "tags": [ "exoskeleton", "teleoperation", "low-cost", "data-collection" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A portable dual-arm exoskeleton (about $600 in parts, per the paper) for joint-level teleop and cheap in-the-wild demos; reports few-minute teleop plus wild data rivals much longer teleop-only training.", "takeaway_zh": "便携式双臂外骨骼（论文称零件成本约 600 美元），可做关节级遥操作并低成本采集野外示教；报告称少量遥操作数据加野外数据即可达到甚至超过长时间纯遥操作训练的效果。", "github_stars": 50, "page": "https://embodied-ai-radar.com/e/arxiv-2309.14975.html", "labs": [ "sjtu" ], "code_license": "MIT" }, { "id": "arxiv-2503.03081", "title": "AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons", "type": "hardware", "arxiv": "2503.03081", "year": 2025, "venue": "CoRL 2025", "org": "Shanghai Jiao Tong University", "url": null, "code": "https://github.com/AirExo/AirExo-2", "project": null, "topics": [ "hardware", "manipulation", "learning" ], "tags": [ "exoskeleton", "in-the-wild", "imitation-learning" ], "robot_types": [], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Exoskeleton demos are converted into pseudo-robot demonstrations; the paper reports its RISE-2 policy trained on them alone, with no robot data, performs comparably to one trained on teleoperated robot data.", "takeaway_zh": "将外骨骼示教转换为“伪机器人”示教；论文报告仅用这些数据（无任何机器人数据）训练的 RISE-2 策略，效果与用遥操作机器人数据训练的策略相当。", "github_stars": 44, "page": "https://embodied-ai-radar.com/e/arxiv-2503.03081.html", "labs": [ "sjtu" ], "code_license": "NOASSERTION" }, { "id": "arxiv-2407.03162", "title": "Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning", "type": "hardware", "arxiv": "2407.03162", "year": 2024, "venue": null, "org": "UC San Diego, HKU", "url": null, "code": "https://github.com/Dingry/BunnyVisionPro", "project": null, "topics": [ "hardware", "manipulation" ], "tags": [ "teleoperation", "vr", "hand", "bimanual", "haptics" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Apple Vision Pro teleoperation of two dexterous robot hands with real-time retargeting, collision and singularity avoidance, and low-cost vibration haptics for the operator.", "takeaway_zh": "用 Apple Vision Pro 实时遥操作双灵巧手，带实时重定向、碰撞与奇异点规避，并为操作者提供低成本振动触觉反馈。", "github_stars": 359, "page": "https://embodied-ai-radar.com/e/arxiv-2407.03162.html", "labs": [ "ucsd", "hku" ], "code_license": "MIT" }, { "id": "arxiv-2403.07869", "title": "TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation", "type": "hardware", "arxiv": "2403.07869", "year": 2024, "venue": null, "org": "UT Austin, Stanford, Sony AI", "url": null, "code": "https://github.com/UT-Austin-RobIn/telemoma", "project": "https://robin-lab.cs.utexas.edu/telemoma-web/", "topics": [ "hardware", "manipulation" ], "tags": [ "teleoperation", "mobile-manipulation", "whole-body" ], "robot_types": [ "arm", "mobile" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "One modular teleop interface that mixes RGB-D body tracking, VR controllers, keyboard and joystick for whole-body mobile manipulators (Tiago++, HSR, Fetch), in sim and real.", "takeaway_zh": "模块化遥操作接口，可组合 RGB-D 人体追踪、VR 手柄、键盘与摇杆，控制 Tiago++、HSR、Fetch 等移动操作机器人做全身动作，仿真和真机都支持。", "github_stars": 88, "page": "https://embodied-ai-radar.com/e/arxiv-2403.07869.html", "labs": [ "stanford", "ut-austin" ], "code_license": "" }, { "id": "arxiv-1910.00093", "title": "An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research", "type": "hardware", "arxiv": "1910.00093", "year": 2019, "venue": null, "org": "MPI-IS, NYU (Open Dynamic Robot Initiative)", "url": null, "code": "https://github.com/open-dynamic-robot-initiative/open_robot_actuator_hardware", "project": null, "topics": [ "hardware", "locomotion" ], "tags": [ "quadruped", "open-source", "torque-control", "low-cost" ], "robot_types": [ "legged" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "The open-source Solo quadruped and its modular torque-controlled actuators, built from off-the-shelf and 3D-printed parts so labs can reproduce a dynamic legged robot.", "takeaway_zh": "开源四足机器人 Solo 及其模块化力矩控制关节，采用现成件和 3D 打印件，便于各实验室复现动态腿式机器人。", "github_stars": 1524, "page": "https://embodied-ai-radar.com/e/arxiv-1910.00093.html", "labs": [ "nyu" ], "code_license": "BSD-3-Clause" }, { "id": "arxiv-2505.21864", "title": "DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation", "type": "paper", "arxiv": "2505.21864", "year": 2025, "venue": "CoRL 2025", "org": "Stanford, Columbia, CMU, NVIDIA", "url": null, "code": "https://github.com/real-stanford/DexUMI", "project": "https://dex-umi.github.io/", "topics": [ "hardware", "manipulation" ], "tags": [ "real", "exoskeleton", "data-collection", "dexterous-hand", "inpainting" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "DexUMI — an exoskeleton matched to the robot hand's fingertip workspace, plus inpainting that swaps the human hand for the robot's; reports 86% average success (Inspire, XHand) and ~3.2× faster collection than teleop.", "takeaway_zh": "DexUMI：按目标机器人手的指尖工作空间设计的可穿戴手部外骨骼，再用视频修补把画面中的人手换成机器人手；论文报告在 Inspire 与 XHand 上平均成功率 86%，采集效率约为遥操作的 3.2 倍。", "github_stars": 273, "page": "https://embodied-ai-radar.com/e/arxiv-2505.21864.html", "labs": [ "stanford", "nvidia", "cmu", "columbia" ], "code_license": "MIT" }, { "id": "arxiv-2408.11805", "title": "ACE: A Cross-Platform Visual-Exoskeletons System for Low-Cost Dexterous Teleoperation", "type": "paper", "arxiv": "2408.11805", "year": 2024, "venue": "CoRL 2024", "org": null, "url": null, "code": "https://github.com/ACETeleop/ACETeleop", "project": "https://ace-teleop.github.io/", "topics": [ "hardware" ], "tags": [ "real", "teleoperation", "exoskeleton", "low-cost", "humanoid" ], "robot_types": [ "arm", "hand", "humanoid", "legged" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "ACE — a 3D-printed bimanual exoskeleton for wrist pose plus hand-facing webcams for finger pose (about $600); one setup teleoperates arm-hand systems, humanoids and quadruped-mounted arms.", "takeaway_zh": "ACE：3D 打印的双臂外骨骼获取手腕位姿、朝向手部的摄像头估计手指姿态（约 600 美元）；同一套设备可遥操作机械臂+灵巧手、人形机器人以及四足搭载的机械臂。", "github_stars": 136, "page": "https://embodied-ai-radar.com/e/arxiv-2408.11805.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2404.16823", "title": "Learning Visuotactile Skills with Two Multifingered Hands", "type": "hardware", "arxiv": "2404.16823", "year": 2024, "venue": "ICRA 2025", "org": "UC Berkeley", "url": null, "code": "https://github.com/ToruOwO/hato", "project": "https://toruowo.github.io/hato/", "topics": [ "hardware", "tactile", "manipulation" ], "tags": [ "bimanual", "dexterous-hand", "teleoperation", "touch", "low-cost" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "HATO — bimanual setup from two UR5e arms and repurposed prosthetic hands with fingertip touch sensors, VR teleop for data collection, and visuotactile policies for tasks like pouring and handovers.", "takeaway_zh": "HATO：用两台 UR5e 机械臂和改装的假肢手（指尖带触觉传感器）搭建双臂系统，用 VR 遥操作采集数据，并训练视觉-触觉策略完成倒酒、交接等任务。", "github_stars": 174, "page": "https://embodied-ai-radar.com/e/arxiv-2404.16823.html", "labs": [ "berkeley" ], "code_license": "" }, { "id": "openarm", "title": "OpenArm", "type": "hardware", "arxiv": null, "year": 2025, "venue": null, "org": "Enactic", "url": null, "code": "https://github.com/enactic/openarm", "project": "https://openarm.dev", "topics": [ "hardware", "manipulation" ], "tags": [ "arm", "humanoid", "teleoperation", "force-feedback", "open-source" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 1, "added": null, "takeaway_en": "Fully open-source 7-DoF humanoid arm for contact-rich work — CAD, electronics, firmware, ROS 2 and MuJoCo/Genesis models, plus leader–follower force-feedback teleop and gravity compensation.", "takeaway_zh": "完全开源的 7 自由度人形机械臂，面向接触丰富的研究：CAD、电路、固件、ROS 2/MoveIt 2 以及 MuJoCo/Genesis 模型全部公开，支持主从力反馈遥操作与重力补偿（软件 Apache-2.0，硬件 CERN-OHL-S）。", "github_stars": 3689, "page": "https://embodied-ai-radar.com/e/openarm.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2412.10447", "title": "TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning", "type": "hardware", "arxiv": "2412.10447", "year": 2024, "venue": "CoRL 2024", "org": "Princeton, Stanford", "url": null, "code": "https://github.com/jimmyyhwu/tidybot2", "project": null, "topics": [ "hardware", "manipulation" ], "tags": [ "mobile-manipulator", "holonomic", "open-source", "teleoperation", "low-cost" ], "robot_types": [ "arm", "mobile" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "An inexpensive open-source mobile base with powered casters, so it is fully holonomic; carries arbitrary arms and comes with phone teleoperation for collecting mobile-manipulation demos for imitation learning.", "takeaway_zh": "低成本开源移动底盘，采用主动脚轮实现全向（完整约束）运动，可搭载任意机械臂，并配有手机遥操作界面，方便为模仿学习采集移动操作示教数据。", "github_stars": 623, "page": "https://embodied-ai-radar.com/e/arxiv-2412.10447.html", "labs": [ "stanford", "princeton" ], "code_license": "MIT" }, { "id": "arxiv-2407.10353", "title": "UMI-on-Legs: Making Manipulation Policies Mobile with Manipulation-Centric Whole-body Controllers", "type": "paper", "arxiv": "2407.10353", "year": 2024, "venue": "CoRL 2024", "org": null, "url": null, "code": "https://github.com/real-stanford/umi-on-legs", "project": null, "topics": [ "hardware", "locomotion", "manipulation" ], "tags": [ "quadruped", "umi", "whole-body-control", "loco-manipulation", "sim", "real" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Robot-free UMI gripper demos train the manipulation policy; a sim-trained whole-body controller tracks its end-effector trajectories on a quadruped. Reports >70% success on all tasks and zero-shot reuse of an arm policy.", "takeaway_zh": "用手持 UMI 夹爪采集无需机器人的示教来训练操作策略，再由仿真训练的全身控制器在四足机器人上跟踪其输出的末端轨迹；报告所有任务成功率超过 70%，并能零样本复用原为固定机械臂训练的策略。", "github_stars": 567, "page": "https://embodied-ai-radar.com/e/arxiv-2407.10353.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2502.07730", "title": "DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove", "type": "hardware", "arxiv": "2502.07730", "year": 2025, "venue": "RSS 2025", "org": "Tsinghua University", "url": null, "code": "https://github.com/TEA-Lab/DOGlove", "project": "https://do-glove.github.io/", "topics": [ "hardware", "manipulation", "tactile" ], "tags": [ "glove", "teleoperation", "force-feedback", "haptics", "dexterous-hand", "open-source", "low-cost" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "An open-source teleoperation glove buildable in hours for under $600, with 21-DoF hand capture, 5-DoF cable-driven force feedback and fingertip haptics; reports the feedback matters for contact-rich tasks without vision.", "takeaway_zh": "开源遥操作手套，几小时即可组装、成本低于 600 美元：21 自由度手部动捕、5 自由度绳驱力反馈和指尖振动触觉；报告在无视觉输入时力/触觉反馈对接触密集任务至关重要，采集数据可训练模仿学习策略。", "github_stars": 147, "page": "https://embodied-ai-radar.com/e/arxiv-2502.07730.html", "labs": [ "tsinghua" ], "code_license": "MIT" }, { "id": "arxiv-2504.13165", "title": "RUKA: Rethinking the Design of Humanoid Hands with Learning", "type": "hardware", "arxiv": "2504.13165", "year": 2025, "venue": "RSS 2025", "org": "NYU", "url": null, "code": "https://github.com/ruka-hand/RUKA", "project": "https://ruka-hand.github.io/", "topics": [ "hardware", "manipulation" ], "tags": [ "hand", "tendon-driven", "3d-printed", "open-source", "low-cost" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "A human-sized, tendon-driven hand (5 fingers, 15 underactuated DoF) from 3D-printed and off-the-shelf parts; joint-to-actuator control is learned from MANUS-glove mocap data instead of hand-modelled.", "takeaway_zh": "人手大小的腱驱动灵巧手（5 指、15 个欠驱动自由度），由 3D 打印件和现成零件制成；关节到电机的控制映射用 MANUS 数据手套的动捕数据学习得到，而非手工建模。", "github_stars": 202, "page": "https://embodied-ai-radar.com/e/arxiv-2504.13165.html", "labs": [ "nyu" ], "code_license": "MIT" }, { "id": "arxiv-2411.13677", "title": "Bimanual Dexterity for Complex Tasks", "type": "hardware", "arxiv": "2411.13677", "year": 2024, "venue": "CoRL 2024", "org": "CMU", "url": null, "code": null, "project": "https://bidex-teleop.github.io/", "topics": [ "hardware", "manipulation" ], "tags": [ "teleoperation", "bimanual", "hand", "mocap-glove", "low-cost" ], "robot_types": [ "arm", "hand" ], "highlight": false, "rating": 0, "added": null, "takeaway_en": "Bidex — a portable, low-latency bimanual dexterous teleop rig (mocap gloves + teacher arms) for 50+ DoF robots; reports better data, collected faster, than Vision Pro or SteamVR teleop.", "takeaway_zh": "Bidex：便携、低延迟的双臂灵巧手遥操作系统（动捕手套 + 示教臂），可控制 50+ 自由度的机器人；论文报告其采集的数据质量和速度均优于 Vision Pro 与 SteamVR 遥操作。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2411.13677.html", "labs": [ "cmu" ], "code_license": null }, { "id": "arxiv-2610.32969", "title": "DexPort: A Hand-Held Skill Interface for Dexterous Hand Data Collection", "type": "paper", "arxiv": "2610.32969", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/amazon-far/dexport", "project": "https://amberxie88.github.io/dexport/", "topics": [ "hardware", "manipulation", "datasets" ], "tags": [ "data-collection", "dexterous-hand", "portable", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "A portable hand-held rig for collecting demonstrations with a high-DoF robot hand, using the same hand hardware for collection and deployment, so there is no retargeting from human hands and no full robot setup needed.", "takeaway_zh": "便携手持式数据采集装置，面向高自由度机器人灵巧手；采集与部署使用同一只机械手，无需从人手动作重定向，也无需搭建完整机器人系统。", "github_stars": 6, "page": "https://embodied-ai-radar.com/e/arxiv-2610.32969.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2610.29030", "title": "PHD-Hand: A Printable Humanoid Dexterous Hand with Linkage-Driven 3-DOA Digits", "type": "paper", "arxiv": "2610.29030", "year": 2026, "venue": "Humanoids 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "hardware", "manipulation" ], "tags": [ "dexterous-hand", "linkage-driven", "3d-printable", "low-cost", "humanoid" ], "robot_types": [ "hand", "humanoid" ], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "Fully linkage-driven humanoid hand with 3 actuated DoF per finger; a flexure hinge and spring-steel drive rods replace many tiny parts, so it prints on a budget 3D printer at near human-hand size.", "takeaway_zh": "完全连杆驱动的人形灵巧手，每根手指 3 个主动自由度；用柔性铰链和弹簧钢驱动杆取代大量微型零件，可用入门级 3D 打印机制造，尺寸接近真人手。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.29030.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.27998", "title": "LocoPilot: Single-Operator Bilateral Teleoperation for Loco-Manipulation Policy Learning", "type": "paper", "arxiv": "2610.27998", "year": 2026, "venue": null, "org": "IIT", "url": null, "code": "https://github.com/iit-DLSLab/LocoPilot", "project": null, "topics": [ "hardware", "manipulation", "locomotion" ], "tags": [ "teleoperation", "data-collection", "quadruped", "mobile-manipulation", "haptics", "ros2", "real" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-10-26", "takeaway_en": "One operator drives a Unitree Go2 + Piper arm with a leader arm and two handheld controllers, with sensorless force feedback; records a unified 11-D action reused at deployment (shown by fine-tuning MolmoAct2).", "takeaway_zh": "单人即可同时操控 Unitree Go2 四足与 Piper 机械臂：主端机械臂加两个手柄，无需力传感器即可力反馈；ROS 2 管线记录与部署共用的 11 维统一动作，并通过微调 MolmoAct2 验证从采集到部署的全流程。", "github_stars": 9, "page": "https://embodied-ai-radar.com/e/arxiv-2610.27998.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.36598", "title": "OpenJaws: An Open Hardware UMI Framework with Stereo Localization for Generalist Manipulation", "type": "paper", "arxiv": "2610.36598", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://open-jaws.github.io/", "topics": [ "hardware", "manipulation", "datasets" ], "tags": [ "umi", "data-collection", "gripper", "stereo", "open-hardware", "3d-printed" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-28", "takeaway_en": "Fully open UMI-style handheld gripper with a low-cost stereo camera for metric, low-drift trajectories, plus UMI-Loc-Bench-v1 for localization accuracy; hardware, software and data under a permissive license.", "takeaway_zh": "完全开源的 UMI 式手持夹爪：3D 打印、标准 ISO 接口，用低成本双目相机获得公制尺度、低漂移的轨迹，并提出手持采集设备定位精度基准 UMI-Loc-Bench-v1；硬件、软件与数据以可商用许可证发布。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.36598.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.37582", "title": "EgoThumb: A Vision-Driven Supernumerary Robotic Thumb for Autonomous Grasp and Holding Assistance", "type": "paper", "arxiv": "2610.37582", "year": 2026, "venue": "IROS 2026", "org": null, "url": null, "code": "https://github.com/JayeFu/ego_thumb_firmware", "project": "https://ego-thumb.github.io", "topics": [ "hardware", "manipulation" ], "tags": [ "wearable", "supernumerary-finger", "tendon-driven", "imitation-learning", "egocentric", "open-hardware", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-10-29", "takeaway_en": "Open-source wearable extra thumb: a tendon-driven finger with motors moved to the forearm; an imitation policy maps palm-camera images to joint positions, inferring intent without explicit triggers.", "takeaway_zh": "开源的可穿戴“第六指”：紧凑的双腱驱动手指，电机移到前臂以减轻末端重量；模仿学习策略直接把掌心相机图像映射为关节位置，从视觉中推断使用者意图，无需显式手势触发，软硬件全部开源。", "github_stars": 1, "page": "https://embodied-ai-radar.com/e/arxiv-2610.37582.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2610.39385", "title": "UMI-Live for In-The-Wild Bimanual Robotics Data Collection", "type": "paper", "arxiv": "2610.39385", "year": 2026, "venue": null, "org": null, "url": null, "code": "https://github.com/AfterQuery-Research/umi-live", "project": "https://umi-live.github.io/", "topics": [ "hardware", "datasets", "manipulation" ], "tags": [ "umi", "bimanual", "data-collection", "lidar-inertial", "slam", "open-hardware", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "Open-source bimanual UMI with camera, LiDAR-inertial sensing, compute and power on board: no external tracking or calibration; parallel SLAM outputs training-ready data. Releases 40 h of data and hardware files.", "takeaway_zh": "全栈开源的双手 UMI 野外数据采集系统：手持设备集成相机、激光雷达-惯性传感、板载算力与电源，无需外部追踪或现场标定；异步 SLAM 流水线直接产出可训练数据集，并发布 40 小时数据、硬件设计与复现指南，用 π0.5 和 MolmoAct2 微调验证数据质量。", "github_stars": 4, "page": "https://embodied-ai-radar.com/e/arxiv-2610.39385.html", "labs": [], "code_license": "" }, { "id": "arxiv-2610.40646", "title": "SwapBot: Tool-Centric Robot Manipulation via Interchangeable End-Effectors and Tactile Sensing", "type": "paper", "arxiv": "2610.40646", "year": 2026, "venue": null, "org": "University of Washington", "url": null, "code": null, "project": "https://personalrobotics.github.io/SwapBot", "topics": [ "hardware", "manipulation", "tactile" ], "tags": [ "end-effector", "tool-use", "tool-swapping", "teleoperation", "imitation-learning", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-01", "takeaway_en": "Instead of one universal gripper: a fixed robot-side coupling plus cheap tool-side adapters with tactile sensors turn household tools into swappable end effectors; imitation policies cook tofu and flip meat.", "takeaway_zh": "不追求万能夹爪，而是用固定的机器人侧接口加低成本、带触觉传感器的工具侧适配器，把日常厨具变成可自动更换的末端执行器，兼容多种机器人平台；配套遥操作采集示范，模仿学习策略完成煎豆腐、拌沙拉、翻肉等工具任务。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.40646.html", "labs": [ "uw" ], "code_license": null }, { "id": "arxiv-2611.03648", "title": "Pixel: A Co-Designed Full-Stack Robot System for Learning General-Purpose Mobile Manipulation", "type": "paper", "arxiv": "2611.03648", "year": 2026, "venue": null, "org": null, "url": null, "code": null, "project": "https://pixel-robot.com/pixel", "topics": [ "hardware", "manipulation" ], "tags": [ "mobile-manipulation", "wheeled-humanoid", "low-cost", "teleoperation", "real" ], "robot_types": [ "humanoid", "mobile" ], "highlight": true, "rating": 2, "added": "2026-11-03", "takeaway_en": "Hardware, data and learning designed together: a low-cost wheeled humanoid with compliant grippers, teleoperation and robot-free human data collection, one whole-body controller and a shared policy pipeline.", "takeaway_zh": "把硬件、数据和学习放在一起设计：低成本轮式人形、柔顺夹爪，遥操作与免机器人的人类数据采集，统一的全身控制器，以及人类与机器人示范共用的策略学习流程。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03648.html", "labs": [], "code_license": null }, { "id": "arxiv-2611.03138", "title": "KlaskTron: An Open-Source Platform for Physical Adversarial Multi-Agent RL", "type": "paper", "arxiv": "2611.03138", "year": 2026, "venue": null, "org": "ETH Zurich", "url": null, "code": "https://github.com/IDSCETHZurich/klask_rl", "project": "https://idscethzurich.github.io/klask_hardware", "topics": [ "hardware", "learning" ], "tags": [ "open-hardware", "multi-agent", "low-cost", "sim-to-real", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-03", "takeaway_en": "A desktop testbed under $2,500 for adversarial multi-agent RL, built around the tabletop game KLASK: open CAD and BOM, an Isaac Lab digital twin, and a sim-to-real baseline with a learned actuator model.", "takeaway_zh": "基于桌游 KLASK 的桌面级对抗多智能体强化学习平台，成本低于 2500 美元：开放 CAD 与物料清单，提供 Isaac Lab 数字孪生，以及采用学习型执行器模型的 sim-to-real 基线。", "github_stars": 2, "page": "https://embodied-ai-radar.com/e/arxiv-2611.03138.html", "labs": [ "eth" ], "code_license": "" }, { "id": "arxiv-2508.17684", "title": "MEVITA: Open-Source Bipedal Robot Assembled from E-Commerce Components via Sheet Metal Welding", "type": "hardware", "arxiv": "2508.17684", "year": 2025, "venue": "Humanoids 2025", "org": null, "url": null, "code": "https://github.com/haraduka/mevita", "project": "https://haraduka.github.io/mevita-hardware", "topics": [ "hardware", "locomotion" ], "tags": [ "open-source", "biped", "sheet-metal" ], "robot_types": [ "legged" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Open-source biped built from e-commerce parts by sheet-metal welding, avoiding the size and fragility limits of 3D-printed designs.", "takeaway_zh": "用电商零件和钣金焊接搭建的开源双足机器人，避开 3D 打印方案在尺寸和强度上的限制。", "github_stars": 106, "page": "https://embodied-ai-radar.com/e/arxiv-2508.17684.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2409.19795", "title": "The Duke Humanoid: Design and Control For Energy Efficient Bipedal Locomotion Using Passive Dynamics", "type": "hardware", "arxiv": "2409.19795", "year": 2024, "venue": null, "org": "Duke", "url": null, "code": "https://github.com/generalroboticslab/dukeHumanoidHardwareControl", "project": null, "topics": [ "hardware", "locomotion" ], "tags": [ "open-source", "humanoid", "rl", "energy" ], "robot_types": [ "humanoid", "legged" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Open-source 10-DoF humanoid for locomotion research; an RL policy using passive dynamics cuts cost of transport by up to 50% in sim and 31% on hardware.", "takeaway_zh": "面向运动研究的开源 10 自由度人形；利用被动动力学的强化学习策略将运输成本在仿真中最多降低 50%，实机降低 31%。", "github_stars": 26, "page": "https://embodied-ai-radar.com/e/arxiv-2409.19795.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2611.04083", "title": "MABEL: An Open Anthropomorphic Mobile Bimanual Platform for Teleoperation and Learning", "type": "paper", "arxiv": "2611.04083", "year": 2026, "venue": null, "org": "NYU", "url": null, "code": null, "project": "https://robotmabel.github.io/website/", "topics": [ "hardware", "manipulation" ], "tags": [ "open-hardware", "bimanual", "mobile-manipulation" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "An open-source 56-DOF mobile bimanual robot with 7-DOF arms and 17-DOF hands for about US$8.1k in parts, with Apple Vision Pro teleoperation and a demonstrated imitation learning pipeline.", "takeaway_zh": "开源 56 自由度移动双臂机器人，含 7 自由度手臂和 17 自由度灵巧手，零件成本约 8100 美元，支持 Apple Vision Pro 遥操作，并演示了模仿学习全流程。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.04083.html", "labs": [ "nyu" ], "code_license": null }, { "id": "arxiv-2611.04334", "title": "RAIPAL: A Powerful and Agile Humanoid Arm for Human Tasks", "type": "paper", "arxiv": "2611.04334", "year": 2026, "venue": null, "org": "KAIST", "url": null, "code": null, "project": null, "topics": [ "hardware", "locomotion" ], "tags": [ "humanoid", "arm", "actuators" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "A humanoid arm whose force, speed and endurance targets come from human upper-limb measurements; reports a 20 kg peak payload (2.25x its own mass) and 20.6 m/s endpoint speed.", "takeaway_zh": "人形手臂，其力量、速度和耐力指标取自人类上肢测量数据；报告峰值负载 20 公斤（自重的 2.25 倍），末端速度 20.6 米每秒。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2611.04334.html", "labs": [ "kaist" ], "code_license": null }, { "id": "arxiv-2606.00702", "title": "Shape Your Body: Value Gradients for Multi-Embodiment Robot Design", "type": "paper", "arxiv": "2606.00702", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "hardware", "learning" ], "tags": [ "co-design", "value-gradients", "multi-embodiment" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-04", "takeaway_en": "Reuses a frozen multi-robot value function as a differentiable surrogate for robot design, avoiding a new co-design RL loop per robot; handles over 1100 design parameters.", "takeaway_zh": "把冻结的多机器人价值函数作为可微代理模型来优化机器人设计，无需为每台机器人重跑协同设计 RL；可处理 1100 多个设计参数。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.00702.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.22923", "title": "PanoVine: Whole-Body Visuomotor Control for Soft Growing Vine Robot", "type": "paper", "arxiv": "2606.22923", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/xxm19/panovine", "project": "https://panovine-bot.github.io", "topics": [ "hardware" ], "tags": [ "soft-robot", "vine-robot", "visuomotor", "multi-camera", "real" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "First autonomous vine (soft growing) robot: 19 cameras along its body feed an end-to-end visuomotor policy learned from demos, which steers through branches, up slopes and into confined spaces.", "takeaway_zh": "首个自主藤蔓（软体生长）机器人系统：沿机身分布的 19 个摄像头为从演示学到的端到端视觉运动策略提供反馈，能穿过分叉结构、爬坡并进入狭窄空间。", "github_stars": 5, "page": "https://embodied-ai-radar.com/e/arxiv-2606.22923.html", "labs": [], "code_license": "MIT" }, { "id": "arxiv-2607.25798", "title": "Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-design", "type": "paper", "arxiv": "2607.25798", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/real-stanford/transformer-transformer", "project": "https://transformer-transformer.github.io", "topics": [ "hardware" ], "tags": [ "co-design", "diffusion-transformer", "embodiment-generation", "aloha", "fabrication" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Diffusion transformer over a shared tokenization of robot bodies, states and actions that generates robot designs to track target motions. A fabricated optimised ALOHA design cut tracking error by over 70%.", "takeaway_zh": "基于统一的机器人本体/状态/动作 token 表示的扩散 Transformer，按目标末端轨迹生成机器人设计。实际制作的优化版 ALOHA 跟踪误差降低 70% 以上。", "github_stars": 204, "page": "https://embodied-ai-radar.com/e/arxiv-2607.25798.html", "labs": [], "code_license": "NOASSERTION" }, { "id": "arxiv-2606.14218", "title": "Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback", "type": "paper", "arxiv": "2606.14218", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": "https://github.com/inclusionAI/Universal-Manipulation-Exoskeleton", "project": "https://ume-exo.github.io", "topics": [ "hardware" ], "tags": [ "exoskeleton", "teleoperation", "torque-feedback", "compliance", "real" ], "robot_types": [ "arm" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Low-cost upper-limb exoskeleton that records whole-arm poses and joint torques with real-time haptic torque feedback; retargets to OpenArm, Franka and xArm for learning compliant whole-body policies.", "takeaway_zh": "低成本上肢外骨骼：记录整臂构型与关节力矩，并提供实时力矩触觉反馈；可重定向遥操作 OpenArm、Franka 和 xArm，用于学习主动柔顺的全身策略。", "github_stars": 23, "page": "https://embodied-ai-radar.com/e/arxiv-2606.14218.html", "labs": [], "code_license": "Apache-2.0" }, { "id": "arxiv-2603.06280", "title": "SuperSuit: An Isomorphic Bimodal Interface for Scalable Mobile Manipulation", "type": "paper", "arxiv": "2603.06280", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "hardware" ], "tags": [ "teleoperation", "wearable", "mobile-manipulation", "data-collection", "real" ], "robot_types": [ "arm", "mobile" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "One wearable both teleoperates a wheeled bimanual robot and records robot-free demos; arms are mechanically aligned, so no IK or retargeting. With a 10-episode robot-grounded seed, 100 robot-free episodes suffice.", "takeaway_zh": "同一套穿戴设备既能遥操作轮式双臂机器人，也能在机器人不在场时录制示教；手臂与机器人机械同构，无需逆运动学或重定向。只需 10 条有机器人参与的种子数据，其余 100 条可脱离机器人录制。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2603.06280.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.07933", "title": "SPOT: Spatial Perception-Oriented Long-Horizon Humanoid Teleoperation", "type": "paper", "arxiv": "2609.07933", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "hardware" ], "tags": [ "teleoperation", "humanoid", "vr", "data-collection" ], "robot_types": [ "humanoid" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "VR humanoid teleoperation for long tasks: a robot-mounted binocular fisheye, wide stereo display, head-decoupled free-looking and stabilization keep the operator aware of the surroundings.", "takeaway_zh": "面向长时任务的人形机器人 VR 遥操作：机载双目鱼眼相机、宽视场立体显示、与机器人运动解耦的自由环视及画面稳定，让操作者持续掌握周围空间信息。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.07933.html", "labs": [], "code_license": null }, { "id": "arxiv-2609.11753", "title": "SEED-UMI: Sharing the Exoskeleton between human and robot for onE-to-one Dexterous demonstration", "type": "paper", "arxiv": "2609.11753", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "hardware" ], "tags": [ "exoskeleton", "dexterous", "data-collection", "umi", "real" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Human and robot wear the same hand exoskeleton, so encoders and wrist cameras match between collection and rollout; retargeting becomes paired supervision. 3.0x faster collection, 70.0% success on five tasks.", "takeaway_zh": "人与机器人佩戴同一套手部外骨骼，采集与执行时关节编码器和腕部相机看到相同的机构，重定向变成成对监督。数据采集效率是外骨骼遥操作的 3.0 倍，五个任务平均成功率 70.0%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2609.11753.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.06033", "title": "RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning", "type": "paper", "arxiv": "2606.06033", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "hardware" ], "tags": [ "umi", "dexterous-hand", "tactile", "teleoperation", "data-collection" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Wearable dexterous UMI: a shared end-effector module (light hand, in-hand camera, fingertip touch) driven by an isomorphic glove, so collected and deployed observations and actions match. Eight real tasks.", "takeaway_zh": "可穿戴的灵巧 UMI：共享的末端模块（轻量灵巧手、手内相机、指尖触觉）由同构手套驱动，采集与部署时的观测和动作完全一致，无需重定向。在八个真机任务上验证。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.06033.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.11657", "title": "YOCO: You Only Calibrate Once! Fast Mocap Calibration for Dexterous Teleoperation", "type": "paper", "arxiv": "2610.11657", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "hardware" ], "tags": [ "teleoperation", "hand-tracking", "calibration", "mano", "dexterous" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Few-shot calibration of biased mocap gloves or hand trackers: a HyperNet reads a few paired poses and predicts LoRA updates for a frozen MANO estimator, with no per-user fine-tuning.", "takeaway_zh": "对存在偏差的动捕手套或手部追踪做少样本校准：HyperNet 读取少量成对姿态，为冻结的 MANO 手部估计模块预测 LoRA 式更新，无需逐用户微调。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.11657.html", "labs": [], "code_license": null }, { "id": "arxiv-2606.20193", "title": "Belt-Finger: An Affordable Soft Belt-Driven Gripper for Dexterous In-Hand Manipulation", "type": "paper", "arxiv": "2606.20193", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "hardware" ], "tags": [ "gripper", "soft", "in-hand", "low-cost", "teleoperation" ], "robot_types": [ "hand" ], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "A cheap drop-in finger module for parallel grippers: two soft belts keep normal open/close and add in-hand translation, pitch and roll; used with MPC, a light 10-DoF teleop interface and learned policies.", "takeaway_zh": "低成本的平行夹爪升级手指模组：双软皮带结构保留常规开合，并增加手内平移、俯仰与翻滚三个自由度；配合 MPC、轻量的 10 自由度遥操作接口和学习策略使用。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2606.20193.html", "labs": [], "code_license": null }, { "id": "arxiv-2610.22977", "title": "Hardware-as-Regularization: Enhancing Data Consistency in Imitation Learning via an Adjustable Passive Compliant Leader Arm", "type": "paper", "arxiv": "2610.22977", "year": 2026, "venue": "CoRL 2026", "org": null, "url": null, "code": null, "project": null, "topics": [ "hardware" ], "tags": [ "teleoperation", "leader-arm", "data-quality", "low-cost" ], "robot_types": [], "highlight": false, "rating": 0, "added": "2026-11-06", "takeaway_en": "Adds a cheap clutch and 3D-printed spring to each joint of a passive leader arm so redundant joints return to a neutral pose, making demos more consistent; block stacking success 73.3% vs 36.7%.", "takeaway_zh": "为被动主臂的每个关节加装低成本离合器和 3D 打印螺旋弹簧，使冗余关节回到中立姿态，让示教数据更一致；堆积木成功率 73.3%，未加时为 36.7%。", "github_stars": null, "page": "https://embodied-ai-radar.com/e/arxiv-2610.22977.html", "labs": [], "code_license": null } ] }