Robotics papers from our feed that made Hugging Face Daily Papers in the last 14 days, by community upvotes.最近 14 天登上 Hugging Face Daily Papers 的本站收录论文，按社区点赞数排序。
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.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。
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.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。
@misc{yan2026simduoreal,
title = {{SimDuoReal: Object-Centric Bimanual Policy for Emergent Dexterity}},
author = {Qiyang Yan and Yulin Liu and Akash Sharma and Haozhi Qi and Guanya Shi and C. Karen Liu},
year = {2026},
eprint = {2611.10410},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10410},
}
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.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%。
@misc{chen2026dexforesight,
title = {{DexForesight: Grounding Dexterous Policies with Implicit Spatial Foresight}},
author = {Zhongxi Chen and Yi Yang and Xinyi Chen and Mingzhe Ye and Yaping Li and Zhihong Liu and Congsheng Xu and Haonan Wang and Zhijie Deng},
year = {2026},
eprint = {2611.11166},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.11166},
}
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.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。
@misc{chen2026objectworld,
title = {{ObjectWorld: Scaling Object-Centric World Models with A Large-Scale 3D Full-Object Flow Dataset}},
author = {Haonan Chen and Sicheng Isabella Lu and Haitian Li and Jiajin Wu and Hexiang Wei and Jiawei Zhang and Xin Fei and Yiwen Hou and Chenrui Tie and Chongkai Gao and Sheng Wang and Lin Shao},
year = {2026},
eprint = {2611.11085},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.11085},
}
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.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。
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.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%。
@misc{liang2026mosu,
title = {{MOSU2: Long-range Navigation System with Multi-modal Scene Understanding}},
author = {Jing Liang and Zhenyu Hou and Senthurbavan Kirubaharan and Md Safwan Mondal and Ashok Gudivada and Xuesu Xiao and Dinesh Manocha},
year = {2026},
eprint = {2611.11148},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.11148},
}
GPU-parallel motion generation for arms — many trajectory optimizations run at once, giving collision-free, smooth plans in milliseconds.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。
@misc{sundaralingam2023curobo,
title = {{cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation}},
author = {Balakumar Sundaralingam and Siva Kumar Sastry Hari and Adam Fishman and Caelan Garrett and Karl Van Wyk and Valts Blukis and Alexander Millane and Helen Oleynikova and Ankur Handa and Fabio Ramos and Nathan Ratliff and Dieter Fox},
year = {2023},
eprint = {2310.17274},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2310.17274},
}
1M+ trajectories from 22 robot types pooled by 30+ labs; RT-X models trained on it beat single-robot baselines.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。
@misc{collaboration2023open,
title = {{Open X-Embodiment: Robotic Learning Datasets and RT-X Models}},
author = {Open X-Embodiment Collaboration and Abby O'Neill and Abdul Rehman and Abhinav Gupta and Abhiram Maddukuri and Abhishek Gupta and Abhishek Padalkar and Abraham Lee and Acorn Pooley and Agrim Gupta and Ajay Mandlekar and Ajinkya Jain and Albert Tung and Alex Bewley and Alex Herzog and Alex Irpan and Alexander Khazatsky and Anant Rai and Anchit Gupta and Andrew Wang and Andrey Kolobov and Anikait Singh and Animesh Garg and Aniruddha Kembhavi and Annie Xie and Anthony Brohan and Antonin Raffin and Archit Sharma and Arefeh Yavary and Arhan Jain and Ashwin Balakrishna and Ayzaan Wahid and Ben Burgess-Limerick and Beomjoon Kim and Bernhard Schölkopf and Blake Wulfe and Brian Ichter and Cewu Lu and Charles Xu and Charlotte Le and Chelsea Finn and Chen Wang and Chenfeng Xu and Cheng Chi and Chenguang Huang and Christine Chan and Christopher Agia and Chuer Pan and Chuyuan Fu and Coline Devin and others},
year = {2023},
eprint = {2310.08864},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2310.08864},
}
The clipped policy-gradient method behind most GPU-parallel sim-to-real locomotion and dexterity training.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。
Thousands of parallel GPU environments train a quadruped walking policy in minutes; legged_gym became a standard starter.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。
@misc{rudin2021learning,
title = {{Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning}},
author = {Nikita Rudin and David Hoeller and Philipp Reist and Marco Hutter},
year = {2021},
eprint = {2109.11978},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2109.11978},
}
Generating action sequences by denoising handles multimodal demos far better than regression; now a standard IL baseline.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。
@misc{chi2023diffusion,
title = {{Diffusion Policy: Visuomotor Policy Learning via Action Diffusion}},
author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song},
year = {2023},
eprint = {2303.04137},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2303.04137},
}
@misc{ahn2022do,
title = {{Do As I Can, Not As I Say: Grounding Language in Robotic Affordances}},
author = {Michael Ahn and Anthony Brohan and Noah Brown and Yevgen Chebotar and Omar Cortes and Byron David and Chelsea Finn and Chuyuan Fu and Keerthana Gopalakrishnan and Karol Hausman and Alex Herzog and Daniel Ho and Jasmine Hsu and Julian Ibarz and Brian Ichter and Alex Irpan and Eric Jang and Rosario Jauregui Ruano and Kyle Jeffrey and Sally Jesmonth and Nikhil J Joshi and Ryan Julian and Dmitry Kalashnikov and Yuheng Kuang and Kuang-Huei Lee and Sergey Levine and Yao Lu and Linda Luu and Carolina Parada and Peter Pastor and Jornell Quiambao and Kanishka Rao and Jarek Rettinghouse and Diego Reyes and Pierre Sermanet and Nicolas Sievers and Clayton Tan and Alexander Toshev and Vincent Vanhoucke and Fei Xia and Ted Xiao and Peng Xu and Sichun Xu and Mengyuan Yan and Andy Zeng},
year = {2022},
eprint = {2204.01691},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2204.01691},
}
The go-to feature-based SLAM library — monocular, stereo and RGB-D, with IMU fusion and multi-map relocalization.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。
@misc{campos2020orb,
title = {{ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM}},
author = {Carlos Campos and Richard Elvira and Juan J. Gómez Rodríguez and José M. M. Montiel and Juan D. Tardós},
year = {2020},
eprint = {2007.11898},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2007.11898},
}
@misc{lambeta2020digit,
title = {{DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation}},
author = {Mike Lambeta and Po-Wei Chou and Stephen Tian and Brian Yang and Benjamin Maloon and Victoria Rose Most and Dave Stroud and Raymond Santos and Ahmad Byagowi and Gregg Kammerer and Dinesh Jayaraman and Roberto Calandra},
year = {2020},
eprint = {2005.14679},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2005.14679},
}
A VLM backbone with a flow-matching action expert, trained across many robots, doing long dexterous tasks like laundry folding.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。
@misc{black2024vision,
title = {{$π_0$: A Vision-Language-Action Flow Model for General Robot Control}},
author = {Kevin Black and Noah Brown and Danny Driess and Adnan Esmail and Michael Equi and Chelsea Finn and Niccolo Fusai and Lachy Groom and Karol Hausman and Brian Ichter and Szymon Jakubczak and Tim Jones and Liyiming Ke and Sergey Levine and Adrian Li-Bell and Mohith Mothukuri and Suraj Nair and Karl Pertsch and Lucy Xiaoyang Shi and James Tanner and Quan Vuong and Anna Walling and Haohuan Wang and Ury Zhilinsky},
year = {2024},
eprint = {2410.24164},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2410.24164},
}
DreamerV3 — one set of hyperparameters works across 150+ tasks, including collecting diamonds in Minecraft from scratch.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。
@misc{hafner2023mastering,
title = {{Mastering Diverse Domains through World Models}},
author = {Danijar Hafner and Jurgis Pasukonis and Jimmy Ba and Timothy Lillicrap},
year = {2023},
eprint = {2301.04104},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2301.04104},
}
PyTorch library with datasets, pretrained policies (ACT, Diffusion, SmolVLA) and drivers for low-cost arms.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。
@misc{liu2023libero,
title = {{LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning}},
author = {Bo Liu and Yifeng Zhu and Chongkai Gao and Yihao Feng and Qiang Liu and Yuke Zhu and Peter Stone},
year = {2023},
eprint = {2306.03310},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2306.03310},
}
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.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。
@misc{tao2026pixel,
title = {{Pixel: A Co-Designed Full-Stack Robot System for Learning General-Purpose Mobile Manipulation}},
author = {Tony Tao and Jason Jingzhou Liu and Ruslan Salakhutdinov and Deepak Pathak},
year = {2026},
eprint = {2611.03648},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.03648},
}
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.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。
@misc{luo2024precise,
title = {{Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning}},
author = {Jianlan Luo and Charles Xu and Jeffrey Wu and Sergey Levine},
year = {2024},
eprint = {2410.21845},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.21845},
}
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.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%。
@misc{hu2026perceptive,
title = {{Perceptive Humanoid Motion from Simulation}},
author = {Siyuan Hu and Abulikemu Abuduweili and Yue Meng and Shipeng Liu and Quentin Leboutet and Chen Chen and Matthias Müller and Vladlen Koltun},
year = {2026},
eprint = {2611.06908},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.06908},
}
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.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。
@misc{liao2025beyondmimic,
title = {{BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion}},
author = {Qiayuan Liao and Takara E. Truong and Xiaoyu Huang and Yuman Gao and Guy Tevet and Koushil Sreenath and C. Karen Liu},
year = {2025},
eprint = {2508.08241},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2508.08241},
}
Tests scaling on humanoid control: more network, data and compute yield a generalist motion-tracking foundation model, with no manual reward engineering.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。
@misc{luo2025sonic,
title = {{SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control}},
author = {Zhengyi Luo and Ye Yuan and Tingwu Wang and Chenran Li and Fernando Castañeda and Sirui Chen and Zi-Ang Cao and Jiefeng Li and David Minor and Qingwei Ben and Jinhyung Park and David Sami and Zi Wang and Xingye Da and Runyu Ding and Cyrus Hogg and Lina Song and Edy Lim and Eugene Jeong and Tairan He and Haoru Xue and Wenli Xiao and Simon Yuen and Jan Kautz and Yan Chang and Umar Iqbal and Linxi "Jim" Fan and Yuke Zhu},
year = {2025},
eprint = {2511.07820},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2511.07820},
}
@misc{radosavovic2023real,
title = {{Real-World Humanoid Locomotion with Reinforcement Learning}},
author = {Ilija Radosavovic and Tete Xiao and Bike Zhang and Trevor Darrell and Jitendra Malik and Koushil Sreenath},
year = {2023},
eprint = {2303.03381},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2303.03381},
}
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.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。
@misc{peng2018deepmimic,
title = {{DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills}},
author = {Xue Bin Peng and Pieter Abbeel and Sergey Levine and Michiel van de Panne},
year = {2018},
eprint = {1804.02717},
archivePrefix = {arXiv},
primaryClass = {cs.GR},
url = {https://arxiv.org/abs/1804.02717},
}
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.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。
@misc{wu2026airoa,
title = {{AIRoA-0: Long-Horizon, Precise, Cross-Embodiment Manipulation from Handheld Data}},
author = {Yueh-Hua Wu and Kei Ota and Hanlong Li and Hayata Yuasa and Jason Wang and Kengo Ikeuchi and Kensuke Chiyoma and Makoto Sugiura and Masafumi Takahashi and Masatoshi Tateno and Naoaki Kanazawa and Nicola Scianca and Petr Khrapchenkov and Satoshi Otsubo and Takehiko Ohkawa and Takuya Okubo and Tatsuya Matsushima and Yuki Noguchi and Yuma Shin and Zixuan Zhong},
year = {2026},
eprint = {2611.03815},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.03815},
}
@misc{chi2024universal,
title = {{Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots}},
author = {Cheng Chi and Zhenjia Xu and Chuer Pan and Eric Cousineau and Benjamin Burchfiel and Siyuan Feng and Russ Tedrake and Shuran Song},
year = {2024},
eprint = {2402.10329},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2402.10329},
}
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.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。
@misc{ze2024generalizable,
title = {{Generalizable Humanoid Manipulation with 3D Diffusion Policies}},
author = {Yanjie Ze and Zixuan Chen and Wenhao Wang and Tianyi Chen and Xialin He and Ying Yuan and Xue Bin Peng and Jiajun Wu},
year = {2024},
eprint = {2410.10803},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.10803},
}
@misc{zhao2023learning,
title = {{Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware}},
author = {Tony Z. Zhao and Vikash Kumar and Sergey Levine and Chelsea Finn},
year = {2023},
eprint = {2304.13705},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2304.13705},
}
SAM — a promptable segmentation model (points, boxes, masks) trained on 1B+ masks; a standard building block for object masks in robot perception pipelines.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。
@misc{kirillov2023segment,
title = {{Segment Anything}},
author = {Alexander Kirillov and Eric Mintun and Nikhila Ravi and Hanzi Mao and Chloe Rolland and Laura Gustafson and Tete Xiao and Spencer Whitehead and Alexander C. Berg and Wan-Yen Lo and Piotr Dollár and Ross Girshick},
year = {2023},
eprint = {2304.02643},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2304.02643},
}
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%.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%。
@misc{zhou2026when,
title = {{When the Simulator Fails, Not the Policy: Auditing Contact Artifacts in Simulated Robot Benchmarks}},
author = {Alex Zhou and Junhao Wu and Xueyang Zhou and Yixiao Huo and Hongcheng Gao and Di Zhu and Zhenghao Zhang and Yongchao Chen},
year = {2026},
eprint = {2610.41857},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.41857},
}
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.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。
@misc{nguyen2026robocolosseum,
title = {{RoboColosseum: Evaluating Physical Intelligence at Scale In-The-Wild}},
author = {Tan Sang Nguyen and Yi Ru Wang and Shailesh Shailesh and Gang Yang and Jiamo Hu and Jianuo Jiang and Haoquan Fang and Zhongzheng Ren and Zhongyi Luo and Ian Chong and Aaron Tung and Santiago Pravisani and Minghui Lin and Yao Mo and Ishneet Singh and Benjamin Tee and Harold Soh and Siddhartha Srinivasa and Dieter Fox and David Hsu and Haoming Song and Maxime Ellerbach and Thomas Wolf and Pepijn Kooijmans and Ruijie He and Michael Cho and Jiafei Duan},
year = {2026},
eprint = {2611.02497},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.02497},
}
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.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。
@misc{wu2026pragmatic,
title = {{A Pragmatic VLA Foundation Model}},
author = {Wei Wu and Fan Lu and Yunnan Wang and Shuai Yang and Shi Liu and Fangjing Wang and Qian Zhu and He Sun and Yong Wang and Shuailei Ma and Yiyu Ren and Kejia Zhang and Hui Yu and Jingmei Zhao and Shuai Zhou and Zhenqi Qiu and Houlong Xiong and Ziyu Wang and Zechen Wang and Ran Cheng and Yong-Lu Li and Yongtao Huang and Xing Zhu and Yujun Shen and Kecheng Zheng},
year = {2026},
eprint = {2601.18692},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2601.18692},
}
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.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。
@misc{fan2026from,
title = {{From Pixels to Actions - Towards Native Vision-Language-Action Policies}},
author = {Weichen Fan and Haiwen Diao and Penghao Wu and Siyuan Hu and Ziqi Huang and Tianqi Liu and Mingze Sun and Ziyi Wang and Zhongang Cai and Hanming Deng and Quan Wang and Lei Yang and Lewei Lu and Fangzhou Hong and Zhaoxi Chen and Dahua Lin and Ziwei Liu},
year = {2026},
eprint = {2611.06918},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2611.06918},
}
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.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%。
@misc{ma2026open,
title = {{Open-HumanWAM: An Open Recipe for Scaling Human and Robot Experience for Embodied Foundation Models}},
author = {Juncheng Ma and Jiaxin Li and Jiankai Tu and Bo Liang and Guodong Lv and Yufan Deng and Junzhe Tang and Liangzhu Yang and Chen Bian and Zicong Cao and Baohao Zhu and Jianxin Bi and Tengfei Yan and Yinyuan Zhang and Kewei Zhang and Xinglong Chen and Kaiqi Chen and Duomin Wang and Xiaotian Tang and Liang Tan and Li Li and Yuan Xu and Haosen Yang and Yuqi Wang and Jingfei Xia and Songyan Huang and Zhiyang Dou and Zhen Dong and Enze Xie and Tat-Seng Chua and Daquan Zhou},
year = {2026},
eprint = {2611.06817},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2611.06817},
}
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.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。
@misc{intelligence2025vla,
title = {{π*0.6: a VLA That Learns From Experience}},
author = {Physical Intelligence and Ali Amin and Raichelle Aniceto and Ashwin Balakrishna and Kevin Black and Ken Conley and Grace Connors and James Darpinian and Karan Dhabalia and Jared DiCarlo and Danny Driess and Michael Equi and Adnan Esmail and Yunhao Fang and Chelsea Finn and Catherine Glossop and Thomas Godden and Ivan Goryachev and Lachy Groom and Hunter Hancock and Karol Hausman and Gashon Hussein and Brian Ichter and Szymon Jakubczak and Rowan Jen and Tim Jones and Ben Katz and Liyiming Ke and Chandra Kuchi and Marinda Lamb and Devin LeBlanc and Sergey Levine and Adrian Li-Bell and Yao Lu and Vishnu Mano and Mohith Mothukuri and Suraj Nair and Karl Pertsch and Allen Z. Ren and Charvi Sharma and Lucy Xiaoyang Shi and Laura Smith and Jost Tobias Springenberg and Kyle Stachowicz and Will Stoeckle and Alex Swerdlow and James Tanner and Marcel Torne and Quan Vuong and Anna Walling and others},
year = {2025},
eprint = {2511.14759},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2511.14759},
}
A 7B open VLA built on Llama 2 + DINOv2/SigLIP; the de-facto open baseline for VLA research and LoRA fine-tuning.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。
@misc{kim2024openvla,
title = {{OpenVLA: An Open-Source Vision-Language-Action Model}},
author = {Moo Jin Kim and Karl Pertsch and Siddharth Karamcheti and Ted Xiao and Ashwin Balakrishna and Suraj Nair and Rafael Rafailov and Ethan Foster and Grace Lam and Pannag Sanketi and Quan Vuong and Thomas Kollar and Benjamin Burchfiel and Russ Tedrake and Dorsa Sadigh and Sergey Levine and Percy Liang and Chelsea Finn},
year = {2024},
eprint = {2406.09246},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2406.09246},
}
@misc{brohan2023rt,
title = {{RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control}},
author = {Anthony Brohan and Noah Brown and Justice Carbajal and Yevgen Chebotar and Xi Chen and Krzysztof Choromanski and Tianli Ding and Danny Driess and Avinava Dubey and Chelsea Finn and Pete Florence and Chuyuan Fu and Montse Gonzalez Arenas and Keerthana Gopalakrishnan and Kehang Han and Karol Hausman and Alexander Herzog and Jasmine Hsu and Brian Ichter and Alex Irpan and Nikhil Joshi and Ryan Julian and Dmitry Kalashnikov and Yuheng Kuang and Isabel Leal and Lisa Lee and Tsang-Wei Edward Lee and Sergey Levine and Yao Lu and Henryk Michalewski and Igor Mordatch and Karl Pertsch and Kanishka Rao and Krista Reymann and Michael Ryoo and Grecia Salazar and Pannag Sanketi and Pierre Sermanet and Jaspiar Singh and Anikait Singh and Radu Soricut and Huong Tran and Vincent Vanhoucke and Quan Vuong and Ayzaan Wahid and Stefan Welker and Paul Wohlhart and Jialin Wu and Fei Xia and Ted Xiao and others},
year = {2023},
eprint = {2307.15818},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2307.15818},
}
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.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。
@misc{shen2026world,
title = {{World Action Models: A Survey}},
author = {Qiuhong Shen and Shihua Zhang and Yue Liao and Qi Li and Zhenxiong Tan and Shizun Wang and Shuicheng Yan and Xinchao Wang},
year = {2026},
eprint = {2606.20781},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.20781},
}
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.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。
@misc{nvidia2026cosmos,
title = {{Cosmos 3: Omnimodal World Models for Physical AI}},
author = {NVIDIA and : and Aditi and Niket Agarwal and Arslan Ali and Jon Allen and Martin Antolini and Adeline Aubame and Alisson Azzolini and Junjie Bai and Maciej Bala and Yogesh Balaji and Josh Bapst and Aarti Basant and Mukesh Beladiya and Mohammad Qazim Bhat and Zaid Pervaiz Bhat and Dan Blick and Vanni Brighella and Han Cai and Tiffany Cai and Eric Cameracci and Jiaxin Cao and Yulong Cao and Mark Carlson and Carlos Casanova and Ting-Yun Chang and Yan Chang and Yu-Wei Chao and Prithvijit Chattopadhyay and Roshan Chaudhari and Chieh-Yun Chen and Junyu Chen and Ke Chen and Qizhi Chen and Wenkai Chen and Xiaotong Chen and Yu Chen and An-Chieh Cheng and Click Cheng and Xiu Chia and Jeana Choi and Chaeyeon Chung and Wenyan Cong and Yin Cui and Magdalena Dadela and Nalin Dadhich and Wenliang Dai and Joyjit Daw and Alperen Degirmenci and others},
year = {2026},
eprint = {2606.02800},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2606.02800},
}
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.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。
@misc{gao2026dreamdojo,
title = {{DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos}},
author = {Shenyuan Gao and William Liang and Kaiyuan Zheng and Ayaan Malik and Seonghyeon Ye and Sihyun Yu and Wei-Cheng Tseng and Yuzhu Dong and Kaichun Mo and Chen-Hsuan Lin and Qianli Ma and Seungjun Nah and Loic Magne and Jiannan Xiang and Yuqi Xie and Ruijie Zheng and Dantong Niu and You Liang Tan and K. R. Zentner and George Kurian and Suneel Indupuru and Pooya Jannaty and Jinwei Gu and Jun Zhang and Jitendra Malik and Pieter Abbeel and Ming-Yu Liu and Yuke Zhu and Joel Jang and Linxi "Jim" Fan},
year = {2026},
eprint = {2602.06949},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2602.06949},
}
World Models世界模型Dexterous hand灵巧手human-videodexterouslatent-action
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.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。
@misc{wang2026openwam,
title = {{OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining}},
author = {Yuran Wang and Siqiao Huang and Mingleyang Li and Chenhao Zhang and Jiaqi Liang and Weiyang Jin and Yue Chen and Xuemin Chi and Donghao Zhou and Qize Yu and Yu-Kai Wang and Yuhan Rui and Shenzhe Yao and Zhen Yuan and Zhenhao Shen and Kefei Zhu and Zijie Zhu and Ning Gao and Xiaowei Chi and Guanqi He and Shanghang Zhang and Hao Dong and Lin Shao and Hang Zhao},
year = {2026},
eprint = {2609.07398},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.07398},
}
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.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。
@misc{zholus2026robojepa,
title = {{RoboJEPA: Scaling Robotic Latent World Models}},
author = {Artem Zholus and Nicolas Beltran-Velez and Jianhao Yuan and Sarath Chandar and Tushar Nagarajan and Daniel Severo and Koustuv Sinha and Michal Drozdzal and Adriana Romero Soriano and Jeannette Bohg and Nicolas Ballas and Mahmoud Assran},
year = {2026},
eprint = {2610.10515},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2610.10515},
}
Heng Fang, Zehui Guo, Yuehan Zhang, Mingyang Du et al.
Abstract摘要
Trained action policies write manipulation skill into network weights, tying what is learned to the distribution it is learned from. In-context learning (ICL) avoids this coupling but has not matched trained policies, because a demonstration recorded in another scene carries no feedback from the world the robot currently faces. We observe that what an agent learns in context is semantic, recording task steps and object roles rather than visual appearance, so experience gathered in one world transfers to another. Based on this insight, we propose World In-Context Learning (WICL), a paradigm in …
@misc{fang2026world,
title = {{World In-Context Learning for Agentic Robot Manipulation}},
author = {Heng Fang and Zehui Guo and Yuehan Zhang and Mingyang Du and Dingkang Liang and Xiang Bai},
year = {2026},
eprint = {2611.12885},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2611.12885},
}
Yu Zhou, Xinyuan Xia, Xuankun Yang, Yinghao Yang et al.
Abstract摘要
World action models jointly predict video and actions conditioned on language. Existing studies focus primarily on architectures and training mechanisms, typically treating language-video alignment acquired through video pretraining as an established foundation. However, introducing action learning may impair this alignment. Principled guidance on data and supervision design for maintaining language-video alignment during action learning remains limited. To investigate this, we curate a pool of approximately 47,000 hours of general videos, egocentric videos, UMI demonstrations, and robot …
@misc{zhou2026swam,
title = {{SWAM: Scaling World Action Models with Cross-Modal Alignment}},
author = {Yu Zhou and Xinyuan Xia and Xuankun Yang and Yinghao Yang and Wendi Chen and Bowen Liu and Jianhan Ma and Weicheng Duan and others},
year = {2026},
eprint = {2611.12870},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2611.12870},
}
George Nehma, Patrick Quinn, Madhur Tiwari, Camilo Riano-Rios
Abstract摘要
Soft powered descent landings are becoming evermore important in the field of space exploration and scientific advancement, because of their ability to deliver mission critical payloads, both highly accurately and safely. The high efficiency restriction on planetary landings due to the severely limited fuel supply results in the need for optimal guidance trajectories and control. Model based reinforcement learning is proving to be an important tool in simplifying the highly complex process of generating optimal guidance trajectories and control. In this work, we present DreamLander, a state …
@misc{nehma2026dreamlander,
title = {{DreamLander: A 6-DOF World Model based Reinforcement Learning Planetary Lander}},
author = {George Nehma and Patrick Quinn and Madhur Tiwari and Camilo Riano-Rios},
year = {2026},
eprint = {2611.12824},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.12824},
}
Amit Manicka, Laurent Coulon, Darwin Ang, Michael Loushin et al.
Abstract摘要
In ultrasound-guided femoral access, probe pressure deforms the tissue and changes the vascular anatomy a robot observes. A simulator must reproduce these mechanics fast enough for robotic interaction, yet the computational effort needed for a clinically meaningful signal like vein collapse before artery is unclear. We test the feasibility of a physics-based simulation in NVIDIA's Isaac Sim for Healthcare, with Stable Neo-Hookean tissue solved by Vertex Block Descent on one GPU. A simulation of a low-fidelity cylindrical benchtop phantom with known geometry and mechanical properties, pressed …
@misc{manicka2026simulating,
title = {{Simulating Vein Compressibility for Ultrasound-Guided Femoral Access: Speed, Accuracy, and Phantom Validation}},
author = {Amit Manicka and Laurent Coulon and Darwin Ang and Michael Loushin and Timothy M. Kowalewski},
year = {2026},
eprint = {2611.12818},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.12818},
}
Bing Han, Yichuan Peng, Haoyu Dong, Yuanmeng Hu et al.
Abstract摘要
Tactile sensing is an indispensable modality for contact-rich dexterous manipulation, a capability that hints at the ultimate frontiers of embodied intelligence. However, commercially available tactile sensors are still at a relatively early stage of development, with each type of sensor offering irreplaceable strengths alongside its own drawbacks. A key challenge is thus to free tactile understanding from sensor-specific physics, so that embodied models can effectively leverage heterogeneous tactile data. To this end, we develop LingBot-Touch, a tactile foundation model characterized by the …
@misc{han2026learning,
title = {{Learning Heterogeneous Tactile Representation for General Dexterous Manipulation}},
author = {Bing Han and Yichuan Peng and Haoyu Dong and Yuanmeng Hu and Yongyi Jia and Fangjing Wang and Kecheng Zheng and Yiming Zhang and others},
year = {2026},
eprint = {2611.12806},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.12806},
}
Elias Gläßer, Gülhan Şikaroğlu, Pavlo Kupyn, Marc Pollefeys et al.
Abstract摘要
A challenging bottleneck in manipulation research is reproducibility and comparability of methods in real-world environments. Since manipulation is an interactive task, it cannot be evaluated on a static dataset of observations. Benchmarking in simulation is a common remedy, but cannot cover the whole complexity of real-world tests. This paper proposes a new paradigm of stochastic challenges. Like the well-known YCB objects, it is based on reproducible real-world scenes and therefore allows system-level innovation and comparability between different platforms. Due to their stochastic nature, …
@misc{glaer2026benchmarking,
title = {{Benchmarking Real-World Robotic Manipulation through Stochastic Challenges}},
author = {Elias Gläßer and Gülhan Şikaroğlu and Pavlo Kupyn and Marc Pollefeys and Zuria Bauer and Hermann Blum},
year = {2026},
eprint = {2611.12804},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.12804},
}
Yanliang Huang, Peng Xie, Zhen Zhang, Zhuoqi Zeng et al.
Abstract摘要
Autonomous vehicles track planned trajectories close to obstacles. Unknown disturbances make them deviate from these trajectories. With a bound on the tracking error that disturbances can cause, a planned trajectory can be certified as safe before execution. Existing set-based methods bound the tracking error by one invariant ellipsoid for the whole trajectory or by intervals, which are both conservative. For aerial and space vehicles, the tracking error evolves on a matrix Lie group with nonlinear dynamics. However, for group-affine systems, the error dynamics are linear in Lie algebra …
@misc{huang2026certified,
title = {{Certified Zonotopic Reachability Analysis for Trajectory Tracking on Lie Groups}},
author = {Yanliang Huang and Peng Xie and Zhen Zhang and Zhuoqi Zeng and Amr Alanwar},
year = {2026},
eprint = {2611.12767},
archivePrefix = {arXiv},
primaryClass = {eess.SY},
url = {https://arxiv.org/abs/2611.12767},
}
Jorge Vicente-Martinez, Francisco José Mañas-Álvarez, Edgar Ramirez-Laboreo
Abstract摘要
This paper analyzes the cooperative transportation of a payload suspended from two drones in which, instead of being attached to two independent ropes, the payload is allowed to slide along a single rope connecting both UAVs. First, a planar nondimensional dynamical model of the sliding motion with viscous friction is presented. Then, considering stationary drones, this configuration is compared with a fixed payload with the same total rope length, both at equilibrium and under disturbances. The analytical results show that the sliding payload equalizes the tension in both rope segments for …
@misc{vicentemartinez2026sliding,
title = {{Sliding into Balance: Benefits of a Sliding Payload for Load Distribution and Disturbance Rejection in Dual-UAV Transportation}},
author = {Jorge Vicente-Martinez and Francisco José Mañas-Álvarez and Edgar Ramirez-Laboreo},
year = {2026},
eprint = {2611.12754},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.12754},
}