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},
}
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},
}
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},
}
@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},
}
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.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。
@misc{cheng2026cmp,
title = {{CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection}},
author = {Ziyang Cheng and Haoyu Wei and Hang Yin and Xiuwei Xu and Bingyao Yu and Jie Zhou and Jiwen Lu},
year = {2026},
eprint = {2604.07457},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2604.07457},
}
SCOPE gives diffusion trajectory models calibrated uncertainty tubes without Monte Carlo sampling, by distilling score curvature; tested in crowd navigation and Franka manipulation.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。
@misc{xue2026control,
title = {{Control-Ready Uncertainty for Trajectory Diffusion}},
author = {Zhiwei Xue and Jia Yue Kam and Jinhang Qiu and Yifeng Cheng and Ege Gursoy and Jiaming Wang and Vincent Bonnet and Harold Soh},
year = {2026},
eprint = {2610.12431},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.12431},
}
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.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%。
@misc{team2026gigabrain,
title = {{GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning}},
author = {GigaBrain Team and Boyuan Wang and Bohan Li and Chaojun Ni and Guan Huang and Guosheng Zhao and Hao Li and Jie Li and Jindi Lv and Jingyu Liu and Lv Feng and Mingming Yu and Peng Li and Qiuping Deng and Tianze Liu and Xinyu Zhou and Xinze Chen and Xiaofeng Wang and Yang Wang and Yifan Li and Yifei Nie and Yilong Li and Yukun Zhou and Yun Ye and Zhichao Liu and Zheng Zhu},
year = {2026},
eprint = {2602.12099},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2602.12099},
}
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.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。
@misc{gao2026hexashell,
title = {{HexaShell: A Universal Harness for Robot Coding Agents in Simulation and the Real World}},
author = {Hongcheng Gao and Alex Zhou and Jay Zhu and Zelin Zheng and Shijia Ge and Xiping Li and Di Wu and Yazhe Wang and Jianshu Zeng and Xuan Shangguan and Yongli Wang and Yexing Wan and Lingyu He and Zhiqi Jia and Sihang Wu and Xiao He},
year = {2026},
eprint = {2610.23479},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.23479},
}
Co-training on heterogeneous data lets a mobile manipulator clean kitchens and bedrooms in homes it has never seen.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。
@misc{intelligence2025vision,
title = {{$π_{0.5}$: a Vision-Language-Action Model with Open-World Generalization}},
author = {Physical Intelligence and Kevin Black and Noah Brown and James Darpinian and Karan Dhabalia and Danny Driess and Adnan Esmail and Michael Equi and Chelsea Finn and Niccolo Fusai and Manuel Y. Galliker and Dibya Ghosh and Lachy Groom and Karol Hausman and Brian Ichter and Szymon Jakubczak and Tim Jones and Liyiming Ke and Devin LeBlanc and Sergey Levine and Adrian Li-Bell and Mohith Mothukuri and Suraj Nair and Karl Pertsch and Allen Z. Ren and Lucy Xiaoyang Shi and Laura Smith and Jost Tobias Springenberg and Kyle Stachowicz and James Tanner and Quan Vuong and Homer Walke and Anna Walling and Haohuan Wang and Lili Yu and Ury Zhilinsky},
year = {2025},
eprint = {2504.16054},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2504.16054},
}
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.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。
@misc{team2025gigabrain,
title = {{GigaBrain-0: A World Model-Powered Vision-Language-Action Model}},
author = {GigaBrain Team and Angen Ye and Boyuan Wang and Chaojun Ni and Guan Huang and Guosheng Zhao and Haoyun Li and Jie Li and Jiagang Zhu and Lv Feng and Peng Li and Qiuping Deng and Runqi Ouyang and Wenkang Qin and Xinze Chen and Xiaofeng Wang and Yang Wang and Yifan Li and Yilong Li and Yiran Ding and Yuan Xu and Yun Ye and Yukun Zhou and Zhehao Dong and Zhenan Wang and Zhichao Liu and Zheng Zhu},
year = {2025},
eprint = {2510.19430},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2510.19430},
}
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.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。
@misc{chen2025robotwin,
title = {{RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation}},
author = {Tianxing Chen and Zanxin Chen and Baijun Chen and Zijian Cai and Yibin Liu and Zixuan Li and Qiwei Liang and Xianliang Lin and Yiheng Ge and Zhenyu Gu and Weiliang Deng and Yubin Guo and Tian Nian and Xuanbing Xie and Qiangyu Chen and Kailun Su and Tianling Xu and Guodong Liu and Mengkang Hu and Huan-ang Gao and Kaixuan Wang and Zhixuan Liang and Yusen Qin and Xiaokang Yang and Ping Luo and Yao Mu},
year = {2025},
eprint = {2506.18088},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2506.18088},
}
Upgraded ALOHA bimanual rig (stronger grippers, gravity compensation, sturdier frame) with open hardware designs and a MuJoCo model for simulation.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。
@misc{team2024aloha,
title = {{ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation}},
author = {ALOHA 2 Team and Jorge Aldaco and Travis Armstrong and Robert Baruch and Jeff Bingham and Sanky Chan and Kenneth Draper and Debidatta Dwibedi and Chelsea Finn and Pete Florence and Spencer Goodrich and Wayne Gramlich and Torr Hage and Alexander Herzog and Jonathan Hoech and Thinh Nguyen and Ian Storz and Baruch Tabanpour and Leila Takayama and Jonathan Tompson and Ayzaan Wahid and Ted Wahrburg and Sichun Xu and Sergey Yaroshenko and Kevin Zakka and Tony Z. Zhao},
year = {2024},
eprint = {2405.02292},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2405.02292},
}
Puts ALOHA on a mobile base; co-training with static ALOHA data boosts mobile tasks like cooking and opening cabinets.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。
@misc{fu2024mobile,
title = {{Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation}},
author = {Zipeng Fu and Tony Z. Zhao and Chelsea Finn},
year = {2024},
eprint = {2401.02117},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2401.02117},
}
@misc{liu2024rdt,
title = {{RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation}},
author = {Songming Liu and Lingxuan Wu and Bangguo Li and Hengkai Tan and Huayu Chen and Zhengyi Wang and Ke Xu and Hang Su and Jun Zhu},
year = {2024},
eprint = {2410.07864},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.07864},
}
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.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。
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.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。
@misc{li2026ace,
title = {{ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining}},
author = {Hao Li and Ganlong Zhao and Yufei Liu and Haotian Hou and Guoquan Ye and Tongyan Fang and Chunxiao Liu and Siyuan Huang and Jianbo Liu and Xiaogang Wang and Hongsheng Li},
year = {2026},
eprint = {2606.17200},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.17200},
}
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.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。
@misc{ma2026acts,
title = {{ACTS: Learning Visuo-Tactile World Models from Human Physical Interaction}},
author = {Yuxiang Ma and Weihan Xu and Pengfei Ye and Anna Mei and Yuxin Ray Song and Tingle Li and Haonan Chen and Edward H. Adelson},
year = {2026},
eprint = {2610.24285},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.24285},
}
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.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。
@misc{liu2026agentic,
title = {{Agentic World Action Models}},
author = {Zhuoyang Liu and Dantong Niu and Botong Zhang and Boning Shao and Jing Wang and Ruijie Zheng and Yuvan Sharma and Runyu Lu and Jiaming Liu and Renrui Zhang and Roei Herzig and Yutong Bai and Ken Goldberg and Danfei Xu and Yuke Zhu and Guanzhi Wang and Linxi "Jim" Fan and Trevor Darrell},
year = {2026},
eprint = {2610.30136},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.30136},
}
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.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。
@misc{buchner2026articulated,
title = {{Articulated 3D Scene Graphs for Open-World Mobile Manipulation}},
author = {Martin Büchner and Adrian Röfer and Tim Engelbracht and Tim Welschehold and Zuria Bauer and Hermann Blum and Marc Pollefeys and Abhinav Valada},
year = {2026},
eprint = {2602.16356},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2602.16356},
}
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.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。
@misc{wang2026bidex,
title = {{BiDex-100: A Comprehensive Benchmark for Bimanual Dexterous Manipulation Across 100+ Simulation Tasks}},
author = {Yong Wang and Kecheng Zheng and Xing Zhu and Chao Xu and Yujun Shen and Shengze Cai},
year = {2026},
eprint = {2610.25459},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25459},
}
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.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。
@misc{lin2026bidexgrasp,
title = {{BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes}},
author = {Mu Lin and Yi-Lin Wei and Jiaxuan Chen and Yuhao Lin and Shuoyu Chen and Jiangran Lyu and Jiayi Chen and Yansong Tang and He Wang and Wei-Shi Zheng},
year = {2026},
eprint = {2604.06589},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2604.06589},
}
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.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。
@misc{shen2026biroad,
title = {{BiRoAD: Learning Shared and Role-Adaptive Representations for Bimanual Manipulation}},
author = {Yan Shen and Yuchen Liu and Feng Jiang and Hangtian Hu and Xiaoqi Li and Shu Chen and Ruihai Wu and Hao Dong},
year = {2026},
eprint = {2609.23445},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.23445},
}
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.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%。
@misc{palma2026bimanual,
title = {{Bimanual Robot Manipulation via Multi-Agent In-Context Learning}},
author = {Alessio Palma and Indro Spinelli and Vignesh Prasad and Luca Scofano and Yufeng Jin and Georgia Chalvatzaki and Fabio Galasso},
year = {2026},
eprint = {2604.20348},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2604.20348},
}
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.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%。
@misc{mittal2026calibrated,
title = {{Calibrated for Which Policy? Conformal Uncertainty for Fixed World Models Under Intervention}},
author = {Ayush Mittal and Dhruv Gupta},
year = {2026},
eprint = {2610.35877},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2610.35877},
}
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.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。
@misc{huang2026clothdojo,
title = {{ClothDojo: Learned Data Generation and Benchmarking for Bimanual Garment Flattening and Folding}},
author = {Zilong Huang and Zipeng Ye and Caicheng Wang and Yuchen Xie and Jiaxiang Luo},
year = {2026},
eprint = {2611.01190},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.01190},
}
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.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。
@misc{zhang2026core,
title = {{CoRE-VLA: Towards Scalable and Robust Vision-Language-Action Modeling via Conditional Routing of Experts}},
author = {Haozhe Zhang and Sixian Li and Yifei Zhang and Zezheng Huai and Hao Chen and Chunhua Shen and Jingjing Gong and Xipeng Qiu},
year = {2026},
eprint = {2607.03693},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.03693},
}
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.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。
@misc{dao2026direct,
title = {{DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?}},
author = {Jadelynn Dao and Milan Ganai and Yasmina Abukhadra and Ajay Sridhar and Mozhgan Nasr Azadani and Katie Luo and Clark Barrett and Jiajun Wu and Chelsea Finn and Marco Pavone},
year = {2026},
eprint = {2606.12402},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.12402},
}
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.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%。
@misc{sun2026donic,
title = {{DONIC: Generalist Dexterous Bimanual Tracking Control from Human Demonstrations}},
author = {Mingze Sun and Ziang Cao and Yuan Fei and Qiankuan Tao and Zihan Wang and Tianqi Liu and Junhao Chen and Haiwen Diao and Fangzhou Hong and Zhaoxi Chen and Ziwei Liu},
year = {2026},
eprint = {2610.29997},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29997},
}
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.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。
@misc{ping2026dexarena,
title = {{DexArena: A Unified Benchmark for Long-Horizon Dexterous Manipulation}},
author = {Haonan Ping and Jianshu Hu and Yimin Wang and Yi Zhu and Pai Liang and Qiyan Jin and Qirui Bao and Ying Qian and Xinyang Ding and Hongfei Qiao and Tianle Xue and Yucheng Bai and Soo Wei Jie and Shuguang Li and Yutong Ban},
year = {2026},
eprint = {2611.02629},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.02629},
}
@misc{houssaini2026dexsuite,
title = {{DexSuite: An All-in-One Framework for Dexterous Manipulation Across Arms, Hands, and Tasks}},
author = {Anas Houssaini and Hugo He and Junming Shi and Amin Abyaneh and Ricardo Chahine and Shuo Wen and Mohamad H. Danesh and Charles-Étienne Fréchette and Mariana Sosa Guzman and Mathias Desrochers and Rayan Houssaini and Adam Landa and Theophile Soulie and Charlotte Morissette and Annie Tims and Martino Russi and Jonathan Lussier and Gregory Dudek and Doina Precup and Julen Urain and Hsiu-Chin Lin and David Meger},
year = {2026},
eprint = {2610.37799},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37799},
}
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).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）。
@misc{liu2026elevate,
title = {{ELEVATE: Agentic Controller Acquisition from Task Failure for Quadrupedal Manipulation}},
author = {Shiqi Liu and Radu Corcodel and Ding Zhao},
year = {2026},
eprint = {2610.38388},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.38388},
}
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.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%。
@misc{yang2026eventvla,
title = {{EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies}},
author = {Ganlin Yang and Zhangzheng Tu and Yuqiang Yang and Sitong Mao and Junyi Dong and Tianxing Chen and Jiaqi Peng and Jing Xiong and Jiafei Cao and Jifeng Dai and Wengang Zhou and Yao Mu and Tai Wang},
year = {2026},
eprint = {2606.20092},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2606.20092},
}
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.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。
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.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。
@misc{peng2026fact,
title = {{FACT: Failure-Aware Causal Training for World-Action Models}},
author = {Quanquan Peng and Yutong Liang and Rui Yan and Nicklas Hansen and Xiaolong Wang},
year = {2026},
eprint = {2608.10232},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2608.10232},
}
World Models世界模型Robot arm机械臂world-action-modelfailure-dataprogress-estimationbimanualreal
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.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。
@misc{oh2026factr,
title = {{FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning}},
author = {Steven Oh and Jason Jingzhou Liu and Tony Tao and Philip Han and Kenneth Shaw and Satoshi Funabashi and Ruslan Salakhutdinov and Deepak Pathak},
year = {2026},
eprint = {2606.12406},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.12406},
}
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.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。
@misc{zhong2026ft,
title = {{FT-WBC: Learning Fault-Tolerant Whole-Body Control for Legged Loco-Manipulation}},
author = {Yudong Zhong and Pengfei Mai and Sikai Guo and Jiahang Cao and Zhihai Bi and Qiuyue Liu and Ziyan Feng and Jinni Zhou and Jun Ma},
year = {2026},
eprint = {2606.24466},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.24466},
}
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.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。
@misc{lee2026flexible,
title = {{Flexible Language-Guided Representation Editing for Adaptive Robot Behavior}},
author = {Audrey Lee and Nathaniel Dennler and Ilia Sucholutsky and Andreea Bobu},
year = {2026},
eprint = {2610.28893},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.28893},
}
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.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%。
@misc{shi2026flowdpg,
title = {{FlowDPG: Deterministic Policy Gradient on Flow Matching Policies for Real-World Manipulation}},
author = {Kexin Shi and Junyao Shi and Poorvi Hebbar and Zhuolun Zhao and Tarun Amarnath and Yifan Su and Shikhar Bahl and Deepak Pathak},
year = {2026},
eprint = {2606.22303},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.22303},
}
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 2.0.参数不到 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。
@misc{liu2026gawm,
title = {{GAWM: A Compact Latent World-Action Model for Multi-Task Robot Manipulation}},
author = {Yang Liu and Xiaoli Liu and Liuzhao Li and Yuduo Song and Muyuan Liu and Xiang Gao},
year = {2026},
eprint = {2610.37091},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37091},
}
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.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。
@misc{liu2026grasp,
title = {{GRASP: Gradient-Guided Sampling for Contact-Rich Physics-Based Motion Retargeting}},
author = {Yang Liu and Tong Wang and Yiming Bao and Heng Yang},
year = {2026},
eprint = {2611.05754},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05754},
}
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).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）。
@misc{zhou2026gaze,
title = {{Gaze Prompts: Temporally Dense Human Attention for Vision-Language-Action Fine-Tuning}},
author = {Yihan Zhou and Rui Yan and Mingcong Li and Zheyuan Huang and Xu Yang and Xueyang Guo and Yilin Mo},
year = {2026},
eprint = {2609.34550},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.34550},
}
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.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。
@misc{yu2026hugs,
title = {{HUGS: Guiding Unified Dexterous Grasp Synthesis Across Modes and Scales via Learned Human Priors}},
author = {Mingrui Yu and Yongpeng Jiang and Yongyi Jia and Kangchen Lv and Li Huang and Yi Ren and Xiang Li},
year = {2026},
eprint = {2607.04554},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.04554},
}
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.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。
@misc{zhang2026hierarchical,
title = {{Hierarchical Planning with Latent World Models}},
author = {Wancong Zhang and Basile Terver and Artem Zholus and Soham Chitnis and Harsh Sutaria and Mido Assran and Randall Balestriero and Amir Bar and Yann LeCun and Nicolas Ballas},
year = {2026},
eprint = {2604.03208},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2604.03208},
}
World Models世界模型Robot arm机械臂planninglatent-world-modelhierarchical-mpcmacro-actionsreal
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.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。
@misc{cheng2026human,
title = {{Human2Any: Human-to-Robot Transfer via Constraint-Aware Compositional Planning}},
author = {Shuo Cheng and Chuye Zhang and Alfred Cueva and Caelan Garrett and Ajay Mandlekar and Danfei Xu},
year = {2026},
eprint = {2606.28813},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.28813},
}
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.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。
@misc{lee2026keeping,
title = {{Keeping Contact: Task-Adaptive Friction Margins for Bimanual Non-Prehensile Manipulation}},
author = {Qian Ying Lee and Lin Yang and Domenico Campolo},
year = {2026},
eprint = {2610.36961},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36961},
}
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.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。
@misc{agarwal2026knowing,
title = {{Knowing Where to Look: Interactive Object Search via Spatial-Semantic Fusion}},
author = {Aditya Agarwal and Lucy Cai and Kelly Lu and Travis Manderson and Nicholas Roy and Tomás Lozano-Pérez and Leslie Pack Kaelbling},
year = {2026},
eprint = {2611.00499},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.00499},
}
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.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。
@misc{wang2026latent,
title = {{Latent RL Priors for Data-Efficient Dexterous Manipulation}},
author = {Yixiao Wang and Peiyi Chen and Mustafa Mukadam and Shreekant Gayaka and Masayoshi Tomizuka and Son Tran},
year = {2026},
eprint = {2611.08458},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.08458},
}
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.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。
@misc{hu2026learn,
title = {{Learn the Task, Not the Schedule: Reducing Spurious Cross-Arm Coupling in Bimanual Imitation Learning}},
author = {Shuyuan Hu and Deng-Ke Wu and Yifan Jin and Qianhang Luo and Mingchen Ju and Kai Ye and Bo Zhao},
year = {2026},
eprint = {2610.26507},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.26507},
}
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.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%。
@misc{zhu2026learning,
title = {{Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration}},
author = {Xinghao Zhu and Zixi Liu and Shalin Jain and Chenran Li and Milad Noori and Michael Andres Lin and Huihua Zhao and John Welsh and Mrinal Verghese and Wei Liu and Tingwu Wang and Xingye Da and Zhengyi Luo and Vishal Kulkarni and Naema Bhatti and Yuke Zhu and Linxi Fan and Bowen Wen and Danfei Xu and Soha Pouya and Yan Chang},
year = {2026},
eprint = {2607.00033},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.00033},
}
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.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%。
@misc{wang2026learning,
title = {{Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies}},
author = {Yi Wang and Xinchen Li and Pengwei Xie and Pu Yang and Buqing Nie and Yunuo Cai and Qinglin Zhang and Chendi Qu and Jeffrey Wu and Jianheng Song and Xinlin Ren and Jingshun Huang and Mingjie Pan and Siyuan Feng and Zhi Chen and Jianlan Luo},
year = {2026},
eprint = {2605.00416},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2605.00416},
}
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.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%。
@misc{chen2026lima,
title = {{LiMA: Bridging Long-term Imagination to Real-time Dexterous Manipulation via Asynchronous Diffusion}},
author = {Ning Chen and Yankai Fu and Junkai Zhao and Qianpu Sun and Guocai Yao and Pengwei Wang and Zhongyuan Wang and Shanghang Zhang},
year = {2026},
eprint = {2609.28431},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.28431},
}
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.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。
@misc{liu2026long,
title = {{Long-Horizon Manipulation via Trace-Conditioned VLA Planning}},
author = {Isabella Liu and An-Chieh Cheng and Rui Yan and Geng Chen and Ri-Zhao Qiu and Xueyan Zou and Sha Yi and Hongxu Yin and Xiaolong Wang and Sifei Liu},
year = {2026},
eprint = {2604.21924},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2604.21924},
}
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.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。
@misc{cheng2026mabel,
title = {{MABEL: An Open Anthropomorphic Mobile Bimanual Platform for Teleoperation and Learning}},
author = {Qilong Cheng and Rooholla Khorrambakht and Mohamed Yassine Kabouri and Quang Nam Nguyen and Luca Macesanu and Haoran Zhou and Yipeng Wang and Ludovic Righetti},
year = {2026},
eprint = {2611.04083},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.04083},
}
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%.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。
@misc{wilcox2026mesa,
title = {{MESA: An Evaluation Framework for Compositional, Semantic, and Spatial Generalization in Robotics}},
author = {Albert Wilcox and Frank Chang and Nhi Nguyen and Aishani Chakraborty and Jeremy A. Collins and Vaibhav Saxena and Benjamin Joffe and Siddharth Karamcheti and Animesh Garg},
year = {2026},
eprint = {2611.05567},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05567},
}
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.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%。
@misc{banwasi2026messymem,
title = {{MessyMem: Learning-from-Doing Memory for Mobile Manipulation}},
author = {Anuva Banwasi and William Muckelroy and Priya Sundaresan and Linfeng Zhao and Jeannette Bohg and Cherie Ho},
year = {2026},
eprint = {2609.15976},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.15976},
}
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.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。
@misc{llontop2026messynav,
title = {{MessyNav: Zero-Shot Navigation via Manipulation in Messy Environments}},
author = {Kenneth Llontop and Cherie Ho and Dylan Zhou and Anuva Banwasi and Priya Sundaresan and Carlota Parés-Morlans and Jeannette Bohg},
year = {2026},
eprint = {2611.07417},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.07417},
}
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.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。
@misc{wang2026mixture,
title = {{Mixture of Frames Policy: Multi-Frame Action Denoising for Bimanual Mobile Manipulation}},
author = {Dian Wang and Jisang Park and Xiaomeng Xu and Han Zhang and Shuran Song and Jeannette Bohg},
year = {2026},
eprint = {2607.11884},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.11884},
}
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.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。
@misc{fang2026molmoact,
title = {{MolmoAct2: Action Reasoning Models for Real-world Deployment}},
author = {Haoquan Fang and Jiafei Duan and Donovan Clay and Sam Wang and Shuo Liu and Weikai Huang and Xiang Fan and Wei-Chuan Tsai and Shirui Chen and Yi Ru Wang and Shanli Xing and Jaemin Cho and Jae Sung Park and Ainaz Eftekhar and Peter Sushko and Karen Farley and Angad Wadhwa and Cole Harrison and Winson Han and Ying-Chun Lee and Eli VanderBilt and Rose Hendrix and Suveen Ellawela and Lucas Ngoo and Joyce Chai and Zhongzheng Ren and Ali Farhadi and Dieter Fox and Ranjay Krishna},
year = {2026},
eprint = {2605.02881},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2605.02881},
}
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.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%。
@misc{kim2026object,
title = {{Object-Centric Residual RL for Zero-Shot Sim-to-Real VLA Enhancement}},
author = {Kinam Kim and Namiko Saito and Heecheol Kim and Katsushi Ikeuchi and Jaegul Choo and Yasuyuki Matsushita},
year = {2026},
eprint = {2606.18953},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.18953},
}
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.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。
@misc{kaura2026partialbigrasp,
title = {{PartialBiGrasp: Inferring Hidden Local Geometry for Bimanual Grasping from Partial Views}},
author = {Ayush Kaura and Vignesh Vembar and Md Faizal Karim and Keshab Patra and K Madhava Krishna},
year = {2026},
eprint = {2608.19188},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2608.19188},
}
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.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。
@misc{liu2026policyik,
title = {{PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning}},
author = {Yuhan Liu and Xinqi Liu and Zixuan Dong and Keith W. Ross and Shenji Wan},
year = {2026},
eprint = {2611.05372},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05372},
}
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.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。
@misc{hou2026rtpolicy,
title = {{RTPolicy: Real-Time (32.9 Hz) Multi-View Visuomotor Policy for Robotic Manipulation on Edge Devices}},
author = {Xianbao Hou and Zehao Ni and Yonghao He and Lingfeng Qian and Xin Shi and Hu Su and Wei Sui and Cong Yang},
year = {2026},
eprint = {2610.41625},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.41625},
}
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.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。
@misc{he2026reconstruction,
title = {{Reconstruction-Augmented Visuomotor Pre-Training of 3D-Grounded Bimanual Dexterity from RGB-Only Human Videos}},
author = {Xingyi He and Zhiwen Qiu and Adhitya Polavaram and Yunhao Cao and Cory Fan and Julius Samwer and Saleh Kalantari and Kuan Fang},
year = {2026},
eprint = {2610.29716},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29716},
}
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.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。
@misc{zhu2026resmerge,
title = {{ResMerge: Residual Policy Learning and Merging for Continual Adaptation of Pre-trained Robot Policies}},
author = {Kefei Zhu and Lingyun Xu and Yue Xin and Xiaoqi Li and Caixin Guo and Xincheng Du and Yuanpei Chen and Yaodong Yang and Hao Dong},
year = {2026},
eprint = {2610.41663},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.41663},
}
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.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。
@misc{wang2026roboaug,
title = {{RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation}},
author = {Xinhua Wang and Kun Wu and Zhen Zhao and Hu Cao and Yinuo Zhao and Zhiyuan Xu and Meng Li and Shichao Fan and Di Wu and Yixue Zhang and Ning Liu and Zhengping Che and Jian Tang},
year = {2026},
eprint = {2602.14032},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2602.14032},
}
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.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。
@misc{wu2026robostd,
title = {{RoboSTD: Zero-Shot Single-to-Dual Transfer via Sagittal Mirroring for Bimanual Learning}},
author = {Hongyu Wu and Chong Liu and Shaoxuan Xie and Shaqi Luo and Hanyu Feng and Shihan Wu and Guocai Yao},
year = {2026},
eprint = {2610.36685},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36685},
}
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.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%。
@misc{xu2026scaling,
title = {{Scaling Dexterous Manipulation via Action Representation Pretraining}},
author = {Yanjinghao Xu and Jiexin Zhang and Huan Zhao and Jianjia Mo and Zhenghao Yin and Dingkang Liang and Xiang Bai and Han Ding},
year = {2026},
eprint = {2610.34453},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34453},
}
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.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%。
@misc{jawale2026schedulegen,
title = {{ScheduleGen: Demonstration Generation via Skill Scheduling for Multi-Arm Imitation Learning}},
author = {Neel Jawale and Stephan Pleines and Soha Pouya and Yuke Zhu and Ajay Mandlekar and Caelan Garrett},
year = {2026},
eprint = {2610.39429},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.39429},
}
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.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。
@misc{fang2026sim,
title = {{Sim-and-Human Co-training for Data-Efficient and Scene-Generalizable Bimanual Manipulation}},
author = {Kaipeng Fang and Weiqing Liang and Yuyang Li and Ji Zhang and Pengpeng Zeng and Heng Tao Shen and Jingkuan Song and Lianli Gao},
year = {2026},
eprint = {2601.19406},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2601.19406},
}
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},
}
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.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。
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.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。
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.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。
@misc{chen2026supersuit,
title = {{SuperSuit: An Isomorphic Bimodal Interface for Scalable Mobile Manipulation}},
author = {Tongqing Chen and Hang Wu and Jiasen Wang and Xiaotao Li},
year = {2026},
eprint = {2603.06280},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2603.06280},
}
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.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%。
@misc{ma2026taccorl,
title = {{TacCoRL: Integrating Tactile Feedback into VLA via Simulation}},
author = {Siyu Ma and Yuqi Liang and Chang Yu and Yunuo Chen and Hao Su and Yixin Zhu and Yin Yang and Chenfanfu Jiang},
year = {2026},
eprint = {2606.11743},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.11743},
}
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.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。
@misc{he2026towards,
title = {{Towards Human-like Physical Intelligence: Lifelong Vision-Language-Action Learning for Robotic Manipulation}},
author = {Yao He and Gan Sun and Wenqi Liang and Fazeng Li and Yang Cong},
year = {2026},
eprint = {2607.14852},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.14852},
}
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.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。
@misc{gu2026training,
title = {{Training in Deployment: Human-in-the-loop Online Post-Training for Robot Manipulation}},
author = {Chun Gu and Xiaofei Wei and Yiming Chen and Bozhou Zhang and Juntong Zhang and Xiatian Zhu and Li Zhang},
year = {2026},
eprint = {2610.29264},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29264},
}
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.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。
@misc{velamoor2026umi,
title = {{UMI-Live for In-The-Wild Bimanual Robotics Data Collection}},
author = {Govind Velamoor and Adrian Lin and Sam Jung and Spencer Mateega and Brandon Y. Yang and Amish Sethi},
year = {2026},
eprint = {2610.39385},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.39385},
}
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.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。
@misc{ding2026unilatent,
title = {{UniLatent: Cross-Embodiment Transfer via Latent Observation Alignment}},
author = {Yufei Ding and Yi Du and Weiduo Yuan and Hao Jiang and Dylan Goetting and Lawrence Yunliang Chen and Daniel Seita and Yue Wang},
year = {2026},
eprint = {2611.10061},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10061},
}
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.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。
@misc{liang2026universal,
title = {{Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback}},
author = {Litian Liang and Jingxi Xu and Xinda Qi and Yujun Cai and Houzhu Ding and Luqi Wang and Zhixin Sun and Jyh-Herng Chow and Ming Yang and Mark Cutkosky},
year = {2026},
eprint = {2606.14218},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.14218},
}
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.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。
@misc{chen2026warp,
title = {{WARP: Whole-Body Retargeting for Learning from Offline Human Demonstrations}},
author = {Zhenyang Chen and Chuizheng Kong and Chuye Zhang and Yuanshao Yang and Lawrence Y. Zhu and Shreyas Kousik and Danfei Xu},
year = {2026},
eprint = {2606.29940},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.29940},
}
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.“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%。
@misc{yi2026watch,
title = {{Watch, Recall, Act: Always-On Robots in Concurrent Embodied Streams}},
author = {Ding Yi and Peiwen Sun and Chenchu Rong and Jianan Wang and Xili Dai and Xiangyu Yue and Xi Lin},
year = {2026},
eprint = {2609.28429},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.28429},
}
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.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。
@misc{ohkawa2026yubi,
title = {{YUBI: Yielding Universal Bidigital Interface for Bimanual Dexterous Manipulation at Scale}},
author = {Takehiko Ohkawa and Jumpei Arima and Yuki Noguchi and Masatoshi Tateno and Makoto Sugiura and Takuya Okubo and Kengo Ikeuchi and Yuma Shin and Hiroki Nishizawa and Naoaki Kanazawa and Yuki Wakayama and Daiki Fukunaga and Koshi Makihara and Tomohiro Motoda and Floris Erich and Yukiyasu Domae and Tatsuya Matsushima and Yohishiro Okumatsu and Kei Ota},
year = {2026},
eprint = {2606.10244},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.10244},
}
A million-scale real-robot dataset collected in purpose-built scenes, with the GO-1 generalist policy trained on it.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。
@misc{agibotworldcontributors2025agibot,
title = {{AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems}},
author = {AgiBot-World-Contributors and Qingwen Bu and Jisong Cai and Li Chen and Xiuqi Cui and Yan Ding and Siyuan Feng and Shenyuan Gao and Xindong He and Xuan Hu and Xu Huang and Shu Jiang and Yuxin Jiang and Cheng Jing and Hongyang Li and Jialu Li and Chiming Liu and Yi Liu and Yuxiang Lu and Jianlan Luo and Ping Luo and Yao Mu and Yuehan Niu and Yixuan Pan and Jiangmiao Pang and Yu Qiao and Guanghui Ren and Cheng Ruan and Jiaqi Shan and Yongjian Shen and Chengshi Shi and Mingkang Shi and Modi Shi and Chonghao Sima and Jianheng Song and Huijie Wang and Wenhao Wang and Dafeng Wei and Chengen Xie and Guo Xu and Junchi Yan and Cunbiao Yang and Lei Yang and Shukai Yang and Maoqing Yao and Jia Zeng and Chi Zhang and Qinglin Zhang and Bin Zhao and Chengyue Zhao and others},
year = {2025},
eprint = {2503.06669},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2503.06669},
}
A whole-body teleoperation interface plus a learning method for a wheeled bimanual robot doing long household tasks that need reach, balance and navigation.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。
@misc{jiang2025behavior,
title = {{BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities}},
author = {Yunfan Jiang and Ruohan Zhang and Josiah Wong and Chen Wang and Yanjie Ze and Hang Yin and Cem Gokmen and Shuran Song and Jiajun Wu and Li Fei-Fei},
year = {2025},
eprint = {2503.05652},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2503.05652},
}
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.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。
@misc{wen2025dexvla,
title = {{DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control}},
author = {Junjie Wen and Yichen Zhu and Jinming Li and Zhibin Tang and Chaomin Shen and Feifei Feng},
year = {2025},
eprint = {2502.05855},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.05855},
}
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.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。
@misc{cheang2025gr,
title = {{GR-3 Technical Report}},
author = {Chilam Cheang and Sijin Chen and Zhongren Cui and Yingdong Hu and Liqun Huang and Tao Kong and Hang Li and Yifeng Li and Yuxiao Liu and Xiao Ma and Hao Niu and Wenxuan Ou and Wanli Peng and Zeyu Ren and Haixin Shi and Jiawen Tian and Hongtao Wu and Xin Xiao and Yuyang Xiao and Jiafeng Xu and Yichu Yang},
year = {2025},
eprint = {2507.15493},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2507.15493},
}
~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.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。
@misc{jiang2025galaxea,
title = {{Galaxea Open-World Dataset and G0 Dual-System VLA Model}},
author = {Tao Jiang and Tianyuan Yuan and Yicheng Liu and Chenhao Lu and Jianning Cui and Xiao Liu and Shuiqi Cheng and Jiyang Gao and Huazhe Xu and Hang Zhao},
year = {2025},
eprint = {2509.00576},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2509.00576},
}
@misc{team2025gemini,
title = {{Gemini Robotics: Bringing AI into the Physical World}},
author = {Gemini Robotics Team and Saminda Abeyruwan and Joshua Ainslie and Jean-Baptiste Alayrac and Montserrat Gonzalez Arenas and Travis Armstrong and Ashwin Balakrishna and Robert Baruch and Maria Bauza and Michiel Blokzijl and Steven Bohez and Konstantinos Bousmalis and Anthony Brohan and Thomas Buschmann and Arunkumar Byravan and Serkan Cabi and Ken Caluwaerts and Federico Casarini and Oscar Chang and Jose Enrique Chen and Xi Chen and Hao-Tien Lewis Chiang and Krzysztof Choromanski and David D'Ambrosio and Sudeep Dasari and Todor Davchev and Coline Devin and Norman Di Palo and Tianli Ding and Adil Dostmohamed and Danny Driess and Yilun Du and Debidatta Dwibedi and Michael Elabd and Claudio Fantacci and Cody Fong and Erik Frey and Chuyuan Fu and Marissa Giustina and Keerthana Gopalakrishnan and Laura Graesser and Leonard Hasenclever and Nicolas Heess and Brandon Hernaez and Alexander Herzog and R. Alex Hofer and Jan Humplik and Atil Iscen and Mithun George Jacob and Deepali Jain and others},
year = {2025},
eprint = {2503.20020},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2503.20020},
}
H-RDT pretrains a diffusion transformer on human manipulation data to work around the morphology mismatch of cross-robot datasets.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。
@misc{bi2025h,
title = {{H-RDT: Human Manipulation Enhanced Bimanual Robotic Manipulation}},
author = {Hongzhe Bi and Lingxuan Wu and Tianwei Lin and Hengkai Tan and Zhizhong Su and Hang Su and Jun Zhu},
year = {2025},
eprint = {2507.23523},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2507.23523},
}
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.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。
@misc{hu2025slac,
title = {{SLAC: Safe and Efficient Real-Robot Reinforcement Learning via Unsupervised Simulation Pre-Training}},
author = {Jiaheng Hu and Peter Stone and Roberto Martín-Martín},
year = {2025},
eprint = {2506.04147},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2506.04147},
}
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.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。
@misc{im2025twinvla,
title = {{TwinVLA: Data-Efficient Bimanual Manipulation with Twin Single-Arm Vision-Language-Action Models}},
author = {Hokyun Im and Euijin Jeong and Andrey Kolobov and Jianlong Fu and Youngwoon Lee},
year = {2025},
eprint = {2511.05275},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2511.05275},
}
@misc{wang2025vq,
title = {{VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers}},
author = {Yating Wang and Haoyi Zhu and Mingyu Liu and Jiange Yang and Hao-Shu Fang and Tong He},
year = {2025},
eprint = {2507.01016},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2507.01016},
}
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.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。
@misc{hu2024video,
title = {{Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations}},
author = {Yucheng Hu and Yanjiang Guo and Pengchao Wang and Xiaoyu Chen and Yen-Jen Wang and Jianke Zhang and Koushil Sreenath and Chaochao Lu and Jianyu Chen},
year = {2024},
eprint = {2412.14803},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2412.14803},
}
Active perception learned from human demonstrations: a 6-DoF robotic neck and a VR interface let bimanual robots search, track and focus.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。
@misc{xiong2025vision,
title = {{Vision in Action: Learning Active Perception from Human Demonstrations}},
author = {Haoyu Xiong and Xiaomeng Xu and Jimmy Wu and Yifan Hou and Jeannette Bohg and Shuran Song},
year = {2025},
eprint = {2506.15666},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2506.15666},
}
@misc{huang2024d,
title = {{3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing}},
author = {Binghao Huang and Yixuan Wang and Xinyi Yang and Yiyue Luo and Yunzhu Li},
year = {2024},
eprint = {2410.24091},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.24091},
}
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.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。
@misc{yang2024ace,
title = {{ACE: A Cross-Platform Visual-Exoskeletons System for Low-Cost Dexterous Teleoperation}},
author = {Shiqi Yang and Minghuan Liu and Yuzhe Qin and Runyu Ding and Jialong Li and Xuxin Cheng and Ruihan Yang and Sha Yi and Xiaolong Wang},
year = {2024},
eprint = {2408.11805},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2408.11805},
}
Benchmark of 40 home tasks for mobile bimanual robots, from reaching to kitchen cleaning, each with human-collected demonstrations.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。
@misc{chernyadev2024bigym,
title = {{BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark}},
author = {Nikita Chernyadev and Nicholas Backshall and Xiao Ma and Yunfan Lu and Younggyo Seo and Stephen James},
year = {2024},
eprint = {2407.07788},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.07788},
}
@misc{shaw2024bimanual,
title = {{Bimanual Dexterity for Complex Tasks}},
author = {Kenneth Shaw and Yulong Li and Jiahui Yang and Mohan Kumar Srirama and Ray Liu and Haoyu Xiong and Russell Mendonca and Deepak Pathak},
year = {2024},
eprint = {2411.13677},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2411.13677},
}
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.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。
@misc{ding2024bunny,
title = {{Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning}},
author = {Runyu Ding and Yuzhe Qin and Jiyue Zhu and Chengzhe Jia and Shiqi Yang and Ruihan Yang and Xiaojuan Qi and Xiaolong Wang},
year = {2024},
eprint = {2407.03162},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.03162},
}
@misc{khazatsky2024droid,
title = {{DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset}},
author = {Alexander Khazatsky and Karl Pertsch and Suraj Nair and Ashwin Balakrishna and Sudeep Dasari and Siddharth Karamcheti and Soroush Nasiriany and Mohan Kumar Srirama and Lawrence Yunliang Chen and Kirsty Ellis and Peter David Fagan and Joey Hejna and Masha Itkina and Marion Lepert and Yecheng Jason Ma and Patrick Tree Miller and Jimmy Wu and Suneel Belkhale and Shivin Dass and Huy Ha and Arhan Jain and Abraham Lee and Youngwoon Lee and Marius Memmel and Sungjae Park and Ilija Radosavovic and Kaiyuan Wang and Albert Zhan and Kevin Black and Cheng Chi and Kyle Beltran Hatch and Shan Lin and Jingpei Lu and Jean Mercat and Abdul Rehman and Pannag R Sanketi and Archit Sharma and Cody Simpson and Quan Vuong and Homer Rich Walke and Blake Wulfe and Ted Xiao and Jonathan Heewon Yang and Arefeh Yavary and Tony Z. Zhao and Christopher Agia and Rohan Baijal and Mateo Guaman Castro and Daphne Chen and Qiuyu Chen and others},
year = {2024},
eprint = {2403.12945},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.12945},
}
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.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。
@misc{jiang2024dexmimicgen,
title = {{DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning}},
author = {Zhenyu Jiang and Yuqi Xie and Kevin Lin and Zhenjia Xu and Weikang Wan and Ajay Mandlekar and Linxi Fan and Yuke Zhu},
year = {2024},
eprint = {2410.24185},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.24185},
}
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.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。
@misc{kareer2024egomimic,
title = {{EgoMimic: Scaling Imitation Learning via Egocentric Video}},
author = {Simar Kareer and Dhruv Patel and Ryan Punamiya and Pranay Mathur and Shuo Cheng and Chen Wang and Judy Hoffman and Danfei Xu},
year = {2024},
eprint = {2410.24221},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.24221},
}
MoMa-LLM grounds LLMs in dynamic scene graphs for long-horizon object search with a mobile manipulator in unexplored spaces.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。
@misc{honerkamp2024language,
title = {{Language-Grounded Dynamic Scene Graphs for Interactive Object Search with Mobile Manipulation}},
author = {Daniel Honerkamp and Martin Büchner and Fabien Despinoy and Tim Welschehold and Abhinav Valada},
year = {2024},
eprint = {2403.08605},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.08605},
}
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.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。
@misc{lin2024learning,
title = {{Learning Visuotactile Skills with Two Multifingered Hands}},
author = {Toru Lin and Yu Zhang and Qiyang Li and Haozhi Qi and Brent Yi and Sergey Levine and Jitendra Malik},
year = {2024},
eprint = {2404.16823},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2404.16823},
}
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.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。
@misc{wu2024robomind,
title = {{RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation}},
author = {Kun Wu and Chengkai Hou and Jiaming Liu and Zhengping Che and Xiaozhu Ju and Zhuqin Yang and Meng Li and Yinuo Zhao and Zhiyuan Xu and Guang Yang and Shichao Fan and Xinhua Wang and Fei Liao and Zhen Zhao and Guangyu Li and Zhao Jin and Lecheng Wang and Jilei Mao and Ning Liu and Pei Ren and Qiang Zhang and Yaoxu Lyu and Mengzhen Liu and Jingyang He and Yulin Luo and Zeyu Gao and Chenxuan Li and Chenyang Gu and Yankai Fu and Di Wu and Xingyu Wang and Sixiang Chen and Zhenyu Wang and Pengju An and Siyuan Qian and Shanghang Zhang and Jian Tang},
year = {2024},
eprint = {2412.13877},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2412.13877},
}
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.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。
@misc{dass2024telemoma,
title = {{TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation}},
author = {Shivin Dass and Wensi Ai and Yuqian Jiang and Samik Singh and Jiaheng Hu and Ruohan Zhang and Peter Stone and Ben Abbatematteo and Roberto Martín-Martín},
year = {2024},
eprint = {2403.07869},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.07869},
}
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.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。
@misc{wu2024tidybot,
title = {{TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning}},
author = {Jimmy Wu and William Chong and Robert Holmberg and Aaditya Prasad and Yihuai Gao and Oussama Khatib and Shuran Song and Szymon Rusinkiewicz and Jeannette Bohg},
year = {2024},
eprint = {2412.10447},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2412.10447},
}
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.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。
@misc{wen2024tinyvla,
title = {{TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation}},
author = {Junjie Wen and Yichen Zhu and Jinming Li and Minjie Zhu and Kun Wu and Zhiyuan Xu and Ning Liu and Ran Cheng and Chaomin Shen and Yaxin Peng and Feifei Feng and Jian Tang},
year = {2024},
eprint = {2409.12514},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2409.12514},
}
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.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。
@misc{fang2023airexo,
title = {{AirExo: Low-Cost Exoskeletons for Learning Whole-Arm Manipulation in the Wild}},
author = {Hongjie Fang and Hao-Shu Fang and Yiming Wang and Jieji Ren and Jingjing Chen and Ruo Zhang and Weiming Wang and Cewu Lu},
year = {2023},
eprint = {2309.14975},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2309.14975},
}
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.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。
@misc{qin2023anyteleop,
title = {{AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System}},
author = {Yuzhe Qin and Wei Yang and Binghao Huang and Karl Van Wyk and Hao Su and Xiaolong Wang and Yu-Wei Chao and Dieter Fox},
year = {2023},
eprint = {2307.04577},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2307.04577},
}
@misc{wu2023gello,
title = {{GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators}},
author = {Philipp Wu and Yide Shentu and Zhongke Yi and Xingyu Lin and Pieter Abbeel},
year = {2023},
eprint = {2309.13037},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2309.13037},
}
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.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。
@misc{fan2022arctic,
title = {{ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation}},
author = {Zicong Fan and Omid Taheri and Dimitrios Tzionas and Muhammed Kocabas and Manuel Kaufmann and Michael J. Black and Otmar Hilliges},
year = {2022},
eprint = {2204.13662},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2204.13662},
}
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.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。
@misc{yang2022oakink,
title = {{OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction}},
author = {Lixin Yang and Kailin Li and Xinyu Zhan and Fei Wu and Anran Xu and Liu Liu and Cewu Lu},
year = {2022},
eprint = {2203.15709},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2203.15709},
}
Adds fast rigid-body physics and an interactive apartment dataset (ReplicaCAD) to Habitat, plus a home-rearrangement benchmark for mobile manipulators.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。
@misc{szot2021habitat,
title = {{Habitat 2.0: Training Home Assistants to Rearrange their Habitat}},
author = {Andrew Szot and Alex Clegg and Eric Undersander and Erik Wijmans and Yili Zhao and John Turner and Noah Maestre and Mustafa Mukadam and Devendra Chaplot and Oleksandr Maksymets and Aaron Gokaslan and Vladimir Vondrus and Sameer Dharur and Franziska Meier and Wojciech Galuba and Angel Chang and Zsolt Kira and Vladlen Koltun and Jitendra Malik and Manolis Savva and Dhruv Batra},
year = {2021},
eprint = {2106.14405},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2106.14405},
}
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.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。
@misc{kemp2021design,
title = {{The Design of Stretch: A Compact, Lightweight Mobile Manipulator for Indoor Human Environments}},
author = {Charles C. Kemp and Aaron Edsinger and Henry M. Clever and Blaine Matulevich},
year = {2021},
eprint = {2109.10892},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2109.10892},
}
Emanuele Cuzzocrea, Giuseppina Iannotti, Luca Rossini, Arturo Laurenzi et al.
Abstract摘要
Robots are increasingly expected to execute natural-language manipulation tasks in real-world environments. This is particularly relevant in household assistance and logistics, where clutter requires reasoning about both task objectives and execution feasibility. This challenge becomes even more pronounced for bimanual humanoid platforms, where coordinating two manipulators introduces additional interaction and kinematic constraints. We present a zero-shot vision-language framework for execution-aware bimanual manipulation that jointly reasons about task objectives and physical feasibility. …
Gabriel da Silva Lima, Made Widhi Surya Atman, Wallace Moreira Bessa
Abstract摘要
Control barrier function (CBF) filters depend on dynamic models that can be inaccurate when their parameters and external forces are uncertain. This paper investigates a radial basis function (RBF) neural network compensator whose output is shared by a Cartesian tracking controller and a joint-space CBF filter. An auxiliary state driven by the difference between requested and applied torques corrects the learning signal, reducing the direct influence of filter intervention on adaptation. A two-link manipulator is evaluated through constrained regulation, ablations, matched uncertainty and …
@misc{lima2026safety,
title = {{Safety-Constrained Intelligent Control of a 2-DOF Robotic Manipulator Using Control Barrier Functions}},
author = {Gabriel da Silva Lima and Made Widhi Surya Atman and Wallace Moreira Bessa},
year = {2026},
eprint = {2611.12378},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.12378},
}
Rumi Loghmani, Abraham George, Amir Barati Farimani
Abstract摘要
Dexterous grasping in robotics relies on distinct hand configurations depending on object geometry. We introduce BetaGrasp, a 3D diffusion policy that conditions a DP3-style point-cloud policy on a four-class grasp label (crimp, sloper, pinch, and jug), running on a Franka arm with a 16-DoF LEAP hand. We also introduce a climbing-hold benchmark: a spring testbed whose ratchet gives a slip-force reading in newtons. In our analysis the policy's point-cloud encoder did not separate the four holds, so the scan alone gave it little to choose a grasp from. Without the label (a DP3-style baseline), …
@misc{loghmani2026grasp,
title = {{Grasp-Taxonomy Conditioning as a Mode Switch for Dexterous Grasping of Climbing Holds}},
author = {Rumi Loghmani and Abraham George and Amir Barati Farimani},
year = {2026},
eprint = {2611.12017},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.12017},
}
Flipping is a fundamental robotic manipulation skill that requires reorienting a target object in a stable pose on a planar support surface by rotating it 180$^\circ$ about an axis parallel to the support surface -- think of flipping a coin from heads to tails. Although flipping is a common objective in industrial tasks such as parts inspection, mechanical assembly, and tool use, coding a bimanual robot with grippers to flip diverse objects is a significant challenge. In this paper, we show that a coding agent can autonomously develop and refine object-flipping strategies through iterative …
The CoRL 2026 RoboEval challenge has eight bimanual tasks, and it ranks a policy not only on whether it succeeds but on how: every stage of the task, done safely, efficiently and with both arms in coordination, scored against human demonstrations. My solution has two parts. The generic part, carried over from my LeHome challenge solution, is off-policy RL on a $π_{0.5}$-based policy that is also its own value function. It is trained with AWR and RECAP on advantages of the exact official score, in a loop where every rollout is at the same time a Thompson-sampling trial of about 20 inference …
Advances in spatial understanding have enabled frontier models to power coding agents that control a real robot arm zero-shot and end-to-end, without task-specific demonstrations or retraining. This brings two new safety challenges. First, the extensive knowledge of frontier models lets these agents perform tasks that earlier VLA and WAM paradigms could not, opening the door to new forms of capability misuse. Second, coding agents can access digital resources such as web pages and emails. Threats in these resources, such as indirect prompt injection, can carry over into the physical world …
@misc{sun2026when,
title = {{When Coding Agents Get Arms: Safety Risks Across the Digital-Physical Boundary}},
author = {Yiming Sun and Mi Zhang},
year = {2026},
eprint = {2611.11438},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.11438},
}
Huseyin Tugcan Dinc, Ersin Daş, Nikolas Thelenberg, Christian Ott
Abstract摘要
Variable impedance control enhances the interaction capability of robot manipulators by adapting impedance parameters online. However, time-varying impedance parameters may inject energy into the system and violate passivity, particularly when tasks demand rapid stiffness changes. Existing methods address this by increasing damping or limiting the stiffness profile, which often degrades the dynamic response. In this work, we propose a quadratic programming-based parameter optimization framework that guarantees passivity by modulating the desired inertia to compensate for energy injected by …
@misc{dinc2026passivity,
title = {{Passivity-Guaranteed Variable Impedance and Compliance Control via Online Parameter Optimization}},
author = {Huseyin Tugcan Dinc and Ersin Daş and Nikolas Thelenberg and Christian Ott},
year = {2026},
eprint = {2611.10778},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10778},
}
Qiyang Yan, Yulin Liu, Akash Sharma, Haozhi Qi et al.
Abstract摘要
Bimanual dexterous manipulation requires coordinated arm and hand control, yet specifying such coordination manually is challenging and often task-specific. In this work, we explore whether complex bimanual dexterity can emerge from a simple object-centric learning objective. We train reinforcement learning policies under a shared object-centric reward, varying their training environments rather than tuning separate rewards for each behavior. We find that the policies discover pre-grasp manipulation, grasping and regrasping, in-hand adjustment and handover to reach object-pose goals without …
@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},
}
This note describes a multi-view video dataset of a deformable object driven by a measured input, with the measured pose of every camera, and a first 3D result obtained on it with LaGSplat. LaGSplat learns from video a low-dimensional latent state that is both the generalized coordinate of a learned dissipative Lagrangian and the conditioning variable of a Gaussian Splatting decoder; fitting the decoder in 3D calls for synchronized multi-view footage, which the original paper only had from synthetic renderings. Here a robot arm moves a flexible strip by oscillating along a one-dimensional …
@misc{pottier2026four,
title = {{A four-view smartphone video dataset of a robot-held flexible strip, with camera poses and a 3D LaGSplat baseline}},
author = {Louen Pottier},
year = {2026},
eprint = {2611.09959},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2611.09959},
}
Chen Chen, Omid Bagherieh, Naveen Nikamanth Appiah Balaji, Jesse T. Gonzalez et al.
Abstract摘要
Recent work on robot manipulation has focused on specialist policies trained on demonstrations. We show that generalist AI models can control manipulators more reliably than specialist policies such as vision-language-action models (VLAs) and world-action models (WAMs). We systematically analyze agentic manipulation policies (AMPs), in which a generalist AI model commands the manipulator directly, adapting its actions as the task unfolds. We present AMP1, a reference harness that runs unchanged across single-arm, bimanual, and mobile manipulation. AMP1 achieves state-of-the-art results on the …
@misc{chen2026agentic,
title = {{On Agentic Manipulation Policies}},
author = {Chen Chen and Omid Bagherieh and Naveen Nikamanth Appiah Balaji and Jesse T. Gonzalez and Negin Heravi and Fahad Islam and Xueting Li and Jean-Baptiste Regli and others},
year = {2026},
eprint = {2611.09892},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.09892},
}
Loco-manipulation, which requires mobile manipulators to coordinate locomotion and manipulation over long horizons, is a key capability for large-object rearrangement in applications such as service robotics and warehouse automation. Despite recent progress, reliably executing these tasks remains challenging because performance depends on complex robot-object and object-environment interaction dynamics. Existing approaches address this challenge either through model-based methods, which often require accurate dynamics models, or through learning-based methods, which reduce this requirement …
@misc{tu2026grasp,
title = {{Grasp-TAGS: Task-Aware Grasp Selection via Critic Distillation for Prehensile Loco-Manipulation}},
author = {Xun Tu and Minghao Zou and Karthik Desingh},
year = {2026},
eprint = {2611.09864},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.09864},
}
Chang Rajani, Luke Sebastien Marsden, Stephan Kunz, Claudio Mucignat et al.
Abstract摘要
Particle image velocimetry (PIV) reconstructs a flow field from the motion of tracer particles but the reconstruction quality is bounded by how well the flow structure of interest is resolved in the images. The acquisition position is typically fixed by the operator before the measurement. We cast camera placement as a sequential decision-making problem and train a reinforcement learning agent to control a 6-DoF manipulator equipped with a camera, enabling it to locate, frame and resolve a hydrofoil wake in a water tunnel. The agent commands the camera pose and observes the scene only through …
@misc{rajani2026autonomous,
title = {{Autonomous Particle Image Velocimetry Using Zero-Shot Sim-to-Real Reinforcement Learning}},
author = {Chang Rajani and Luke Sebastien Marsden and Stephan Kunz and Claudio Mucignat and Ivan Lunati},
year = {2026},
eprint = {2611.09679},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.09679},
}
Ao Li, Zhihao Yuan, Chubin Zhang, Kunrui Huang et al.
Abstract摘要
Egocentric video captures human-object interaction at scale but rarely includes tactile labels for hidden contact and pressure. We introduce TouchAnnotator, an ego-only bimanual tactile annotator trained with multi-source tactile supervision and privileged-view distillation. A canonical hand field unifies glove pressure, geometry-derived human contact, and robot fingertip deformation from six datasets and three embodiments, while source-aware masks preserve valid targets. During training, an EMA teacher observes synchronized ego and wrist views and distills hand-local representations and …
@misc{li2026touchannotator,
title = {{TouchAnnotator: Multi-Source Tactile Supervision with Privileged-View Distillation}},
author = {Ao Li and Zhihao Yuan and Chubin Zhang and Kunrui Huang and Sheng Xu and Zengjue Chen and Junjie Wang and Jiawei Li and others},
year = {2026},
eprint = {2611.09129},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2611.09129},
}
ZhenYu Zou, Xing Liu, ShiChuang Wang, YunLong Dong et al.
Abstract摘要
Humanoid robots are expected to take over strenuous material handling in manufacturing, logistics, and healthcare, where operating existing wheeled carts would let them move heavy loads without modifying the workplace. However, humanoid cart pushing remains challenging because the bimanual grasp closes a force-coupled loop in which the contact wrenches that drive the cart also disturb the robot's balance, and payload changes alter the coupled dynamics. We propose ForPC, a force-aware whole-body control framework that regulates cart velocity through two-stage reinforcement learning. A …
@misc{zou2026forpc,
title = {{ForPC: Force-Aware Whole-Body Control for Precise Humanoid Cart Pushing}},
author = {ZhenYu Zou and Xing Liu and ShiChuang Wang and YunLong Dong and LiNan Deng and Feng Hua and Jun Wan and Cheng Cheng},
year = {2026},
eprint = {2611.08664},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.08664},
}
Robots that manipulate moving objects act on observations that are outdated when the chosen skill starts, and the best skill can change within a few hundred milliseconds. Skill competence models, however, predict success for a launch at the moment of observation. We propose time-indexed skill success models, which predict whole-task success when a skill is launched after a given delay. Trained on rollouts branched at a grid of launch delays, they let a receding-horizon planner select both the skill and its launch time under fixed or random latency. In a MuJoCo study in which a UR5e captures a …
@misc{omidi2026time,
title = {{Time-Indexed Skill Success Models for Latency-Aware Skill Selection and Timing in Dynamic Manipulation}},
author = {Mohsen Omidi and Maria Chiara Leva and Brian Vaughan},
year = {2026},
eprint = {2611.07964},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.07964},
}
Mohammadreza Firoozalizadeh, Zakiye Rostamirad, Arman Barghi, Ahmad Kalhor et al.
Abstract摘要
Object rearrangement in cluttered tabletop scenes is challenging, especially in wide workspaces where dual-arm systems improve coverage and efficiency. Limited free space often necessitates buffering, while objects near or across the shared reachability region of the manipulators must be transferred under careful coordination to avoid inter-arm collisions. Conventional inter-arm transfer typically relies on a fixed handoff configuration, which constrains where and how objects are exchanged, enforces spatiotemporal synchronization of the arms, and reduces flexibility in dense layouts. This …
@misc{firoozalizadeh2026dual,
title = {{Dual-Arm Tabletop Rearrangement with Movable Stacking and Gap-Aware Push Transfers}},
author = {Mohammadreza Firoozalizadeh and Zakiye Rostamirad and Arman Barghi and Ahmad Kalhor and Mohammadreza Nayeri and Mehdi Tale Masouleh},
year = {2026},
eprint = {2611.07726},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.07726},
}
Xuenmin Hu, Qingxue Qian, Wei Luo, Benyi Ma et al.
Abstract摘要
Legged robots equipped with manipulators have shown significant potential in the fields of daily life and industry in complex environments. However, existing methods mainly focus on global navigation or manipulation independently, while overlooking the last-mile navigation problem, where the robot must jointly determine the base placement, approach direction, and grasp execution to achieve reliable object grasping, and different manipulation stages require distinct control behaviors. To address these challenges, we propose Last-Mile Grasp-Navigation (LMGN), a unified policy learning model for …
@misc{hu2026lmgn,
title = {{LMGN: A Unified Policy Learning Model for Last-Mile Grasp-Navigation}},
author = {Xuenmin Hu and Qingxue Qian and Wei Luo and Benyi Ma and Bo Tang and Long Chen},
year = {2026},
eprint = {2611.07515},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.07515},
}
Mobile manipulators deployed in everyday human environments, such as homes for assistive care or flexible automation, often need to locate specific objects on demand from accumulated observations. Equally important, they must reliably determine when a target is absent before committing to navigation or interaction. Existing approaches demand either slow exhaustive re-processing or expensive 3D representations, and are evaluated on recall and accuracy rather than specificity, leaving their ability to abstain on missing targets unmeasured. We find existing systems that search using text or …
@misc{paul2026fast,
title = {{Fast and Reliable Open-Vocabulary Object Search with Absence Detection from Robot Memory}},
author = {Nicholas Paul and Devson Butani and Eric Martinson},
year = {2026},
eprint = {2611.07493},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.07493},
}
This paper presents a layered control framework that recasts high-dimensional system dynamics as a composition of dynamically interconnected subsystems, with dynamic dual-arm manipulation on the humanoid serving as a representative application. The humanoid is decomposed into a single rigid body (SRB) representing the torso and floating-base dynamic models representing the articulated arms and lower-body. The rigid connections between arm and torso and the closed kinematic chain formed between the end-effectors during cooperative manipulation are represented as holonomic constraints. The …
An ordered sequence of end-effector poses can give a redundant robot arm multiple valid joint configurations at each waypoint. Inverse kinematics (IK) computes these configurations. Choosing a configuration only for the next waypoint can leave the arm poorly placed for later waypoints. This may increase planned motion time or cause a later planning failure. This paper presents Future-Window Inverse Kinematics (FWIK), an online method that selects IK solutions by considering a small window of future waypoints. FWIK generates several IK candidates, connects them in a layered joint-space graph, …
Yuhan Liu, Xinqi Liu, Zixuan Dong, Keith W. Ross et al.
Abstract摘要
Inverse kinematics (IK) converts end-effector pose commands into joint motion, and many vision-language-action (VLA) policies rely on it to act on a given arm and to transfer across embodiments. Precise IK remains laborious: redundancy and singularities complicate the inverse mapping, execution depends on hand-tuned controllers, and training data on physical robots are costly to collect. We present PolicyIK, a goal-conditioned reinforcement learning policy for high-precision IK. It updates joint-position references from pose feedback and observes its accumulated reference relative to the …
@misc{liu2026policyik,
title = {{PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning}},
author = {Yuhan Liu and Xinqi Liu and Zixuan Dong and Keith W. Ross and Shenji Wan},
year = {2026},
eprint = {2611.05372},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05372},
}
Ali Imran, Abdalwhab Bakheet Mohammed Abdalwhab, Rafael Gomes Braga, H\'el\`ene Duval et al.
Abstract摘要
Moving robot swarms from simulation into the physical world can be difficult because small discrepancies in hardware, timing and communication can cascade across interacting robots and alter group behaviour. This challenge is known as the reality gap. When swarms operate in human environments, a second challenge emerges: a perceptual gap between what the swarm physically does and what human onlookers actually perceive. This study examines the same decentralised coordination code deployed across three robotic swarms-tabletop, aerial and human-scale mobile manipulators-during live artistic …
@misc{imran2026reality,
title = {{Reality and Perceptual Gaps in Expressive Swarm Robotics}},
author = {Ali Imran and Abdalwhab Bakheet Mohammed Abdalwhab and Rafael Gomes Braga and H\'el\`ene Duval and Audrey Rochette and Vivek Shankar Varadhajan and Yann Bouteiller and Jean Mazerolle and others},
year = {2026},
eprint = {2611.05244},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05244},
}
Donggeon Kim, Hyungho Chris Choi, Donghyuk Choi, Jeongsoo Park et al.
Abstract摘要
This paper presents humanoid manipulator hardware designed specifically for human-level performance throughout its broad domain of tasks, from heavy payloads to highly dynamic motions. Unlike conventional designs targeting uniform workspace capability, the proposed approach derives force, speed, and endurance targets from published measurements of task-relevant human upper-limb performance. These targets guide the actuator, mechanism, and thermal design toward torque transparency and sustained operation. The platform integrates force-oriented upper-arm components, speed-oriented forearm …
@misc{kim2026raipal,
title = {{RAIPAL: A Powerful and Agile Humanoid Arm for Human Tasks}},
author = {Donggeon Kim and Hyungho Chris Choi and Donghyuk Choi and Jeongsoo Park and Choongin Lee and Chan Park and Jemin Hwangbo},
year = {2026},
eprint = {2611.04334},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.04334},
}
This paper presents an innovative guidance and control method for a space manipulator that actively maneuvers its base to hold a tumbling object stationary relative to the base during proximity operations. This simplifies motion planning, reduces collision risk, and enables impact-free capture using single- or multi-arm configurations, with or without predefined grasping interfaces. The proposed approach involves two phases. In the pre-grasping phase, a synchronization control aligns the target's center of mass (CoM) with the system's instantaneous center of rotation (ICR), virtually …
Qilong Cheng, Rooholla Khorrambakht, Mohamed Yassine Kabouri, Quang Nam Nguyen et al.
Abstract摘要
As robot learning moves beyond the tabletop, many tasks require mobility, adjustable reach, active perception, and bimanual dexterity. Commercial platforms combining these capabilities are costly, while affordable open-source alternatives cover only a subset and require software integration. We present MABEL, an open-source anthropomorphic mobile bimanual robot with 56 degrees of freedom (DOF) and a US$8.1k parts cost. It combines two 7-DOF arms with 17-DOF hands, a pitching torso, a lift, a 3-DOF neck, and a holonomic base. Back-drivable arms and torso support compliant interaction, with …
@misc{cheng2026mabel,
title = {{MABEL: An Open Anthropomorphic Mobile Bimanual Platform for Teleoperation and Learning}},
author = {Qilong Cheng and Rooholla Khorrambakht and Mohamed Yassine Kabouri and Quang Nam Nguyen and Luca Macesanu and Haoran Zhou and Yipeng Wang and Ludovic Righetti},
year = {2026},
eprint = {2611.04083},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.04083},
}
Aditya Agarwal, William Shen, Sahit Chintalapudi, Lucy Cai et al.
Abstract摘要
A mobile manipulator asked to "put the toys away in the toy box" must decide which objects are toys and which container the user means, then find base poses, grasps, and placements it can execute. Because a scene representation is built before the instruction is known, it may lack the attributes, affordances, or concepts needed to resolve the task. We present FEIJOA, a language-model agent that grounds the instruction at test time. It queries a grounding tool over a posed RGB-D scan, inspects the returned candidates, and refines its queries until they resolve the instruction. An …
@misc{agarwal2026feijoa,
title = {{FEIJOA: Task-Driven Grounding and Execution for Mobile Manipulation}},
author = {Aditya Agarwal and William Shen and Sahit Chintalapudi and Lucy Cai and Nishanth Kumar and Aidan Curtis and Travis Manderson and Tom\'as Lozano-P\'erez and others},
year = {2026},
eprint = {2611.04080},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.04080},
}
Yueh-Hua Wu, Kei Ota, Hanlong Li, Hayata Yuasa et al.
Abstract摘要
AIRoA-0 (A-0) is a bimanual manipulation system learned from 16,151 hours of handheld demonstrations, with no robot data collected at any stage. The demonstrations are recorded with YUBI, our open-source handheld gripper, which lets people record manipulation data anywhere with no robot present. From a single instruction, A-0 sorts more than a dozen small parts into their numbered cells and changes the mop pads and brush of a robot vacuum; among other tasks, it unzips a pencil case and folds a towel. It performs these tasks using only a camera on each wrist, with no head or scene camera, at a …
@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 others},
year = {2026},
eprint = {2611.03815},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.03815},
}
Conventional robot arms can be extremely heavy compared with the everyday objects they manipulate, creating a significant inertia mismatch at contact. Even a small accidental bump from a heavy arm can knock a light object out of place. Modern learning methods have nevertheless demonstrated a wide range of successful manipulation behaviors on these platforms, suggesting that even extreme inertia mismatch can be overcome with sufficient data. However, it remains unclear whether high inertia imposes a hidden cost on learning, potentially worsening scaling with data or lowering the performance …
Haonan Ping, Jianshu Hu, Yimin Wang, Yi Zhu et al.
Abstract摘要
Long-horizon dexterous manipulation remains a fundamental challenge for robot learning, requiring the composition of multiple skills, precise bimanual coordination, generalizable visual understanding, and robust skill transitions. Yet existing manipulation benchmarks largely emphasize isolated skills or support only a limited range of end-effectors, making these capabilities difficult to evaluate systematically. We introduce DexArena, a benchmark designed to evaluate learning and generalization in long-horizon bimanual dexterous manipulation. DexArena features 10 long-horizon tasks and 30 …
@misc{ping2026dexarena,
title = {{DexArena: A Unified Benchmark for Long-Horizon Dexterous Manipulation}},
author = {Haonan Ping and Jianshu Hu and Yimin Wang and Yi Zhu and Pai Liang and Qiyan Jin and Qirui Bao and Ying Qian and others},
year = {2026},
eprint = {2611.02629},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.02629},
}
Tan Sang Nguyen, Yi Ru Wang, Shailesh Shailesh, Gang Yang et al.
Abstract摘要
General-purpose robot policies must work across robots, tasks, and environments, yet the variation that makes physical trials informative also makes them difficult to reproduce. We introduce RoboColosseum, a distributed evaluation framework that combines unified policy serving and standardized protocols across a network of locally operated robots in diverse environments. Our initial study evaluates five vision-language-action (VLA) policies and three frontier AI agents across four embodiments (SO-101, Franka, Bimanual-YAM, and G1 Humanoid). The Open Track compares models without task-specific …
@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 others},
year = {2026},
eprint = {2611.02497},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.02497},
}
Latent-space path planning (LSPP) compresses the configuration space of a robotic manipulator into a compact, differentiable manifold learned by a variational autoencoder, enabling fast gradient-based planning without explicit inverse kinematics. Existing LSPP frameworks enforce obstacle avoidance with a binary collision classifier, whose saturated gradients near obstacle boundaries and lack of temporal structure cause the optimizer to stall and provide no forward-invariance guarantee. We replace the classifier with a neural control barrier function (CBF) learned directly in the latent space. …
This paper studies dual path following for a quadrotor carrying a three-degree-of-freedom arm. The center of mass of the combined system must follow a vehicle path and the end effector must follow a second path, while the arm respects its joint limits and avoids its singular postures. The vehicle and the arm are mechanically coupled, so one controller must handle both paths. With the center of mass and the end-effector path coordinates as outputs, and a dynamic extension of the thrust, the system admits a normal form without internal dynamics. We characterize the states where this normal form …
@misc{tariq2026dual,
title = {{Dual Path Following for an Aerial Manipulator Under State and Input Constraints via Transverse Feedback Linearization}},
author = {Hamza Tariq and Adeel Akhtar},
year = {2026},
eprint = {2611.01442},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.01442},
}
Mohammed Al-Ghaili, Yakoop Qasim, Hassan Almubarak
Abstract摘要
High-accuracy repetitive tracking is challenging for position-commanded manipulators whose low-level controllers are inaccessible, while constrained model-based iterative learning control (ILC) can require substantial manual parameter tuning. We present Differentiable Iterative Learning Control (Diff-ILC), an online bilevel framework that adapts the parameters of a constrained ILC update between physical robot executions. A closed-loop differential model is identified once from paired smooth perturbations and subsequently kept fixed. At each trial, a constrained quadratic program computes a …
Zilong Huang, Zipeng Ye, Caicheng Wang, Yuchen Xie et al.
Abstract摘要
Garment flattening and folding require robots to identify garment structure during manipulation and adapt their actions as the cloth deforms. We present ClothDojo, a simulation platform that generates adaptive manipulation trajectories at scale across diverse garments and initial states and evaluates policies in closed loop. ClothDojo encodes each garment's canonical mesh coordinates as an NOCS texture, providing canonical surface-position cues through the visual interface of a pretrained vision-language-action model. We fine-tune the model on human demonstrations and corrections to obtain a …
@misc{huang2026clothdojo,
title = {{ClothDojo: Learned Data Generation and Benchmarking for Bimanual Garment Flattening and Folding}},
author = {Zilong Huang and Zipeng Ye and Caicheng Wang and Yuchen Xie and Jiaxiang Luo},
year = {2026},
eprint = {2611.01190},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.01190},
}
Thomas Pierson, Matthew Mitchell, Kira Perna, Anand Iyer et al.
Abstract摘要
Laser doppler vibrometer (LDV) scans can be used to detect defects and cracks in manufactured parts before they are deployed. This work presents a method for reconstructing the steady-state surface wavefield of a three-dimensional part by combining automated robotic manipulation with neural rendering. A six-degree-of-freedom (6-DOF) robotic manipulator (UFactory xArm6) with an embedded piezoelectric transducer presented an acoustically excited test article to a stationary LDV at a sequence of known orientations, yielding a set of complex-valued 2D scans with poses derived analytically from …
@misc{pierson2026d,
title = {{3D Neural Rendering for Automated Ultrasonic Laser Scans}},
author = {Thomas Pierson and Matthew Mitchell and Kira Perna and Anand Iyer and Eric Flynn and Adam Wachtor},
year = {2026},
eprint = {2611.00527},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2611.00527},
}
Wearable robots such as supernumerary robotic limbs (SRLs) can augment human capability, but their effectiveness depends strongly on the operator's sense of embodiment, comprising the sense of agency (SoA) and the sense of ownership (SoO). When an SRL is detached from the body so that the operator can act in two places at once, both the sense of agency and task performance tend to degrade. This paper presents the Multi-modal Embodiment Stimulation Shoulder Pad (MESSPa), a wearable interface that combines one-to-one motion transfer with spatially distributed pneumatic (balloon) haptic feedback …
@misc{vimolmongkolporn2026messpa,
title = {{MESSPa: A Multi-modal Embodiment Stimulation Shoulder Pad for Preserving the Sense of Agency in a Detachable Robotic Arm}},
author = {Vitvasin Vimolmongkolporn and Guillaume Gourmelen and Yukiko Iwasaki and Hiroyasu Iwata},
year = {2026},
eprint = {2611.00509},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.00509},
}
Francis Fan, Mackenzie A Eagleson, Yvonne Zhang, Hongting Pan et al.
Abstract摘要
Uncontrolled hemorrhage remains a leading cause of preventable trauma mortality. Timely intervention is not always available, making autonomous systems that can aid in bleeding control a promising alternative. Given the safety-critical nature of the task, supervised autonomy, in which the system executes the technical steps while a human retains oversight, represents a necessary intermediate step toward this goal. In this work, we present a supervised autonomous bimanual robotic framework for injury cauterization (eletrosurgical coagulation) in low-grade splenic trauma. The system integrates …
@misc{fan2026supervised,
title = {{Supervised Autonomous Bimanual Suction and Cauterization for Bleeding Control in Low Grade Splenic Trauma}},
author = {Francis Fan and Mackenzie A Eagleson and Yvonne Zhang and Hongting Pan and Lujia Yang and Ethan Kilmer and Jiawei Ge and Nural Yilmaz and others},
year = {2026},
eprint = {2611.00237},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.00237},
}
Hongren Wang, Mingyu You, Zihan Li, Zhipeng Wang et al.
Abstract摘要
Diffusion and flow matching visuomotor policies require iterative denoising or numerical integration for multimodal action generation, introducing latency and execution mismatch. We instead consider a different formulation: conditioned on vision and proprioception, demonstrated bimanual action chunks concentrate in a structured observation-dependent region of joint-action space, making the action target more direct to model than the generative process. Based on this view, we propose the Direct Manifold Action Transformer (DMAT), which reformulates action generation as the direct modeling of …
@misc{wang2026direct,
title = {{Direct Action-Manifold Modeling for Efficient and Robust Bimanual Manipulation}},
author = {Hongren Wang and Mingyu You and Zihan Li and Zhipeng Wang and Shuo Jiang and Bin He},
year = {2026},
eprint = {2610.41643},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.41643},
}
Prabhu Nithin Gollapudi, Andreas Maier, Franziska Mathis-Ullrich, Water Zhao et al.
Abstract摘要
Percutaneous needle insertion is a common practice in minimally invasive procedures whose clinical success depends on accurate needle placement. However, achieving the necessary precision is a challenge for manual operators. Robotic assistance can improve targeting accuracy and reduce operator dependence, but residual millimeter-scale errors persist because nominal kinematic models do not fully represent the physical manipulator. This paper presents a robust bound-constrained kinematic calibration method designed to improve positional accuracy for a 6-Degree-of-Freedom (6-DoF) robotic …
@misc{gollapudi2026physically,
title = {{Physically Constrained Kinematic Calibration for High-Precision Robotic Needle Guidance}},
author = {Prabhu Nithin Gollapudi and Andreas Maier and Franziska Mathis-Ullrich and Water Zhao and Jia Wang and Siming Bayer},
year = {2026},
eprint = {2610.41429},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.41429},
}
Eadom Dessalene, Michael Maynord, Amir-Hossein Shahidzadeh, Botao He et al.
Abstract摘要
Computer vision models learn hand action only from its visual consequences, without access to the underlying embodied motor signals. We address this absence with MotorSense, the first large-scale paired egocentric video and surface electromyography (EMG) dataset. MotorSense pairs bimanual EMG bands with egocentric video and yields synchronized video, bimanual EMG and IMU, and audio across 100 hours of unscripted manipulation. EMG offers a powerful scalable alternative to manual annotation: it captures finger movement, hand configuration, and the forces exerted between hand and environment as …
@misc{dessalene2026motorsense,
title = {{MotorSense: A Video-EMG Dataset of Motoric Representations for Action Understanding}},
author = {Eadom Dessalene and Michael Maynord and Amir-Hossein Shahidzadeh and Botao He and Yasamin Tabatabaee and Ruoshi Liu and Ruohan Gao and Paul Pu Liang and others},
year = {2026},
eprint = {2610.41127},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.41127},
}