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},
}
@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},
}
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},
}
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},
}
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},
}
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},
}
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},
}
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 Thin Harness for Exploring the Limits of VLM Agents as Robot Coding Agents}},
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},
}
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},
}
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},
}
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},
}
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。
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},
}
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},
}
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},
}
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},
}
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},
}
@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},
}
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},
}
@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},
}
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},
}
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},
}
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},
}
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。
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},
}
Jianyu Hou, Jiawei Fu, Weikang Wan, Minghui Jiang et al.
Abstract摘要
Supernumerary robotic fingers augment human dexterity by enabling a single hand to perform complex "hold-and-manipulate" tasks that typically require bimanual coordination. However, existing devices are often hindered by bulky hardware that limits interaction to large objects and rely on explicit hand motion triggers that lack environmental context. To address these challenges, we present EgoThumb, a fully open-source, vision-driven supernumerary robotic thumb. EgoThumb features a compact, dual tendon-driven finger whose actuation is relocated to the forearm, reducing distal weight while …
@misc{hou2026egothumb,
title = {{EgoThumb: A Vision-Driven Supernumerary Robotic Thumb for Autonomous Grasp and Holding Assistance}},
author = {Jianyu Hou and Jiawei Fu and Weikang Wan and Minghui Jiang and Jiajun Li and Hao Su},
year = {2026},
eprint = {2610.37582},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37582},
}
Failure diagnosers for robot manipulation are increasingly vision--language models (VLMs), trained and evaluated on single-arm failures produced by perturbing a successful demonstration. Bimanual tool use adds failures of other kinds: a tool that slips out of its socket while still held, two correct gestures made at different times, and attempts that fail for two reasons at once. On a simulated bimanual tool-use task whose failure causes are read from the recorded trace, we measure what each sensing channel lets a diagnoser tell. The tool's slip is read on the tool hand; which hand was late …
@misc{bonin2026which,
title = {{Which Hand Was Late? Diagnosis for Robot Failure Recovery in Bimanual Tool Use}},
author = {Baptiste Bonin and Caro Strickland and Audrey Durand},
year = {2026},
eprint = {2610.37533},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37533},
}
Lorenzo Ticozzi, Patricio A. Vela, Panagiotis Tsiotras
Abstract摘要
Spacecraft-mounted robotic manipulators can support long-term operability of orbiting assets by conducting tasks such as on-orbit assembly, repair, upgrade, and refueling. In this context, the integration of continuum/soft robotic mechanisms into space manipulator designs promises to reduce stowage volume and mass, as well as to improve operational safety through the inherent mechanical compliance of soft structures. This work presents a novel state reconstruction pipeline for a hybrid soft-rigid space manipulator, in which the distal link is a tendon-driven continuum robot. The estimation …
@misc{ticozzi2026state,
title = {{State estimation for a hybrid rigid-continuum space manipulator system}},
author = {Lorenzo Ticozzi and Patricio A. Vela and Panagiotis Tsiotras},
year = {2026},
eprint = {2610.37043},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37043},
}
Mohammad Bahari, Amir Hossein Barjini, Jouni Mattila
Abstract摘要
This paper presents a mixed discrete-continuous multiobjective bilevel framework for the structure-actuation co-design of all-electric heavy-duty robotic manipulators. The upper level optimizes manipulator geometry and actuation topology, while joint-specific lower-level searches construct feasible archives of discrete motor, gearbox, and screw configurations. A system-level coordinator selects actuator packages according to hardware mass, co-design cost, and loss-aware supply energy, and the resulting actuator masses are fed back to the robot model until a mass-consistent fixed point is …
Maintaining contact in non-prehensile manipulation requires more than nominal friction cone feasibility: a force barely inside the cone and one well inside it are both feasible, but they provide different margins against slip and contact loss. This paper uses signed friction distance to measure friction margin in force space and derives a task-adaptive margin from the force correction needed to balance pivot moment deviations in bimanual tilting. The resulting contact margins are embedded in a sequence of second-order cone programs (SOCPs), each giving a globally optimal contact force …
@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},
}
Zongyuan Zhang, Christopher Lehnert, Will N. Browne, Jonathan M. Roberts
Abstract摘要
Mobile robot manipulators extend contact-task capability beyond the workspace of fixed robot installations, but their bases must also sustain the forces generated by the task. On lightweight platforms, base motion and support compliance can change the force and motion realised at the workpiece. How these non-rigid base effects manifest during sustained contact remains insufficiently characterised. This paper investigates non-rigid base effects in mobile robot manipulators through a sanding case study combining exploratory simulation with experiments on two lightweight, unactuated wheeled …
@misc{zhang2026shaky,
title = {{On Shaky Ground: Characterising Non-Rigid Base Effects in Mobile Manipulation}},
author = {Zongyuan Zhang and Christopher Lehnert and Will N. Browne and Jonathan M. Roberts},
year = {2026},
eprint = {2610.36725},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36725},
}
Hongyu Wu, Chong Liu, Shaoxuan Xie, Shaqi Luo et al.
Abstract摘要
Bimanual robot manipulation plays an important role in real-world robotic applications, but the high cost of collecting bimanual demonstrations limits the scalable development of bimanual policy learning. Meanwhile, existing single-arm manipulation data are difficult to directly transfer to bimanual systems due to the Single-to-Dual gap in action spaces, coordination requirements, and arm-side spatial preference. To address this issue, we propose RoboSTD, a zero-shot Single-to-Dual data construction method for bimanual robot learning. RoboSTD first applies sagittal-plane mirroring to …
@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},
}
Philip Huang, Fabio Ramos, Jiaoyang Li, Caelan Garrett et al.
Abstract摘要
Multi-arm manipulation has the potential to dramatically improve the throughput, efficiency, and dexterity of many manipulation tasks. Recent work coordinates multiple robot arms in a shared workcell by allocating and scheduling tasks for greater parallelism, jointly planning multi-arm motions, or directly learning multi-arm policies. So far, these approaches are either too slow, rely on domain-specific assumptions, or scale poorly with the number of robots and task horizon. Thus, we introduce cuArms, a conflict-based planner co-designed with GPU parallelism that efficiently decomposes the …
@misc{huang2026cuarms,
title = {{cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation}},
author = {Philip Huang and Fabio Ramos and Jiaoyang Li and Caelan Garrett and Lars Johannsmeier},
year = {2026},
eprint = {2610.36250},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36250},
}
Annika Delucchi, Vincenzo Di Paola, Bruno Marco Quadrelli, Matteo Zoppi
Abstract摘要
Estimating forces acting on Deformable Linear Objects (DLOs) is challenging due to their underactuated nature. However, reliable contact estimation is crucial to precisely control them and enable safe manipulation tasks. Differently from the various approaches presented in the literature, this work adopts a probabilistic formulation where the DLO state and sensor data are related through conditional distributions. In particular, factor graphs are used to determine the magnitude, direction, and location of a 3D force acting on a DLO, while simultaneously reconstructing its shape. Since DLOs …
@misc{delucchi2026d,
title = {{3D-Force Estimation for Dual-arm Manipulation of Deformable Linear Objects via Factor Graphs}},
author = {Annika Delucchi and Vincenzo Di Paola and Bruno Marco Quadrelli and Matteo Zoppi},
year = {2026},
eprint = {2610.35058},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35058},
}
Vision-language-action (VLA) models are increasingly used for robot manipulation because they map camera images and a language instruction directly to actions. Robots that cannot be tethered to a workstation, such as mobile manipulators and humanoids, must run them on onboard embedded boards, where the prefill latency of the language backbone grows with the hundreds of visual tokens processed at every control step. To meet real-time limits, training-free accelerators recompute only a subset of the visual tokens and skip the rest, either by pruning them or by reusing their cached key-value …
This paper presents two controllers for a quadrotor equipped with a two-DOF robot manipulator that physically interacts with the environment. Cartesian impedance and adaptive sliding mode controllers are implemented and compared for a simple hovering task in the presence of some bounded disturbance. A full dynamic model is developed, and the two above controllers are implemented with the same initial conditions and desired position.
Jingwen Li, Levin Kuhlmann, Jason Friedman, Gideon Kowadlo
Abstract摘要
Bimanual robotic control requires simultaneous management of distinct functional demands under motor noise, energetic costs, and communication delays. We investigate how learning responsibility should be organised across modular controllers and the two arms in a simulated musculoskeletal system with two communicating recurrent controllers. Using a 2 x 2 design, we compare differential versus shared controller objectives and fixed versus balanced hand-task assignments. Task-aligned functional specialisation was strongest when differential objectives were combined with a fixed assignment …
@misc{li2026learning,
title = {{Learning Functional Specialisation for Bimanual Robotic Control}},
author = {Jingwen Li and Levin Kuhlmann and Jason Friedman and Gideon Kowadlo},
year = {2026},
eprint = {2610.33947},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.33947},
}
Doanh Le Thien, Viet-Thanh Nguyen, Pham Tri Quang, Nhi Pham et al.
Abstract摘要
Video world models (VWMs) are rapidly emerging as scalable neural simulators for robotic manipulation. However, most benchmarks only evaluate them as open-world video generators, using Vision-Language Models (VLMs) that lack strict 3D spatial and kinematic reasoning. To bridge this gap, we introduce RoboWorM, a comprehensive, deployment-aware benchmark that leverages known hardware priors such as robot kinematic trees and fixed camera configurations, to systematically examine structural and spatiotemporal fidelity. RoboWorM curates 1,605 episodes across single-arm, bimanual, and humanoid …
@misc{thien2026roboworm,
title = {{RoboWorM: Evaluating Movement Realism, Consistency and Acceleration Affects in Embodied Video World Models}},
author = {Doanh Le Thien and Viet-Thanh Nguyen and Pham Tri Quang and Nhi Pham and Christopher Wewer and Minh-Loi Nguyen and Hoang H. Tran and Tho Le and others},
year = {2026},
eprint = {2610.33807},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.33807},
}
Andrea Morghen, Pierluigi Arpenti, Roberto Schiattarella, Giovanni Acampora et al.
Abstract摘要
The integration of quantum computing into robotics requires mathematical representations that preserve the geometric structure and configuration dependence of robot motion. This paper introduces a structure-preserving quantum representation of rigid-body transformations and serial-manipulator kinematics, establishing a correspondence between classical kinematic models and parameterized quantum circuits. Building on the Bloch-sphere representation, spatial directions are encoded into single-qubit states, while quantum rotations reproduce the ordered composition of reference-frame …
@misc{morghen2026quantum,
title = {{Quantum Closed Loop Inverse Kinematics Through Hilbert-Space Encoding of Robot Kinematics}},
author = {Andrea Morghen and Pierluigi Arpenti and Roberto Schiattarella and Giovanni Acampora and Bruno Siciliano},
year = {2026},
eprint = {2610.33715},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.33715},
}
Giovanni Minelli, Antonio Langella, Giovanni B. Dessy, Lorenzo Amatucci et al.
Abstract摘要
Vision-language-action (VLA) models are typically pretrained on broad, diverse datasets and subsequently fine-tuned on a comparatively small dataset tailored to a specific target robot. Such adaptation recipe is now widely established, achieving its most mature results on fixed-base arms due to their static base dynamics and consistent reference frame. Mounting the same arm on a quadruped extends the workspace to the entire floor, but fundamentally changes the embodiment: rather than controlling the arm in isolation, the policy must jointly coordinate base navigation, body posture, and …
@misc{minelli2026from,
title = {{From Tabletop to Walking Bot: Deploying a VLA Model on a Legged Manipulator Robot}},
author = {Giovanni Minelli and Antonio Langella and Giovanni B. Dessy and Lorenzo Amatucci and Giulio Turrisi and Victor Barasuol and Claudio Semini},
year = {2026},
eprint = {2610.32271},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.32271},
}
The growing population of orbital debris has intensified interest in active debris removal and on-orbit servicing, for which dual-arm space manipulators offer improved grasping stability and load distribution relative to single-arm systems. However, coordinating two manipulators under inter-arm collision avoidance, orientation alignment, and sensing uncertainty remains challenging. This paper develops a coordinated reinforcement learning framework for cooperative motion planning of a dual-arm space manipulator in a target-reaching task with inter-arm collision-avoidance constraints. A …
@misc{fallahiarezoodar2026ppo,
title = {{PPO vs. SAC: A Comparative Study for Cooperative Motion Control of Dual-Arm Space Robots}},
author = {Nasim Fallahiarezoodar and Zheng H. Zhu},
year = {2026},
eprint = {2610.32034},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2610.32034},
}
Hengyi Sim, Quentin Rouxel, Dennis Hong, Darwin Caldwell et al.
Abstract摘要
Robust manipulation of soft objects, such as garments, is a critical skill for service and household robots to adopt, but remains challenging due to its high-dimensional state space, and material-dependent deformations. Humans address this challenge by using mental world models that "imagine" the shape and material properties from partial observations, which enables them to predict how actions affect future states. As such, embedding such world models in robots allows for better generalization in manipulating a wide variety of garments in the real world. However, learned world models …
@misc{sim2026context,
title = {{In-Context Learning World Models for Robot Dual-Arm Manipulation of Soft Garment with Partial Observations}},
author = {Hengyi Sim and Quentin Rouxel and Dennis Hong and Darwin Caldwell and Fei Chen},
year = {2026},
eprint = {2610.30988},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.30988},
}
Emma Huang, Moira Bohley, Krishna Jamakhandi, Rebecca Fellbaum et al.
Abstract摘要
Currently, CT-guided lung biopsy is a highly manual procedure requiring patient breath-holding, iterative manual needle insertions, and repeated CT confirmation scans to accurately target a tumor. Automating this process within the CT bore--without relying on manual needle placement or patient breath-holding--has the potential to reduce procedure time, patient discomfort, and radiation exposure. In this work, we present an integrated sensing and control system capable of performing a gated needle insertion within the CT bore. The experiments demonstrate the system's ability to track and …
@misc{huang2026integrated,
title = {{Integrated Respiratory Sensing and Gated Control for Autonomous Needle Insertion Via CT Bore Robotic Arm}},
author = {Emma Huang and Moira Bohley and Krishna Jamakhandi and Rebecca Fellbaum and Sophia Tang and Sean Tutton and Albert Hsiao and Alexander Norbash and others},
year = {2026},
eprint = {2610.30452},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.30452},
}
Mingze Sun, Ziang Cao, Yuan Fei, Qiankuan Tao et al.
Abstract摘要
Despite recent progress in dexterous reference tracking, existing models focus primarily on single-hand manipulation and a limited range of interactions, leaving the bimanual dexterous behaviors common in real life unaddressed. The scarcity of contact-rich bimanual manipulation data and the increased complexity of coordinating contacts make developing a generalist bimanual tracker particularly challenging. Therefore, we present 1) DONIC, the first generalist bimanual tracker jointly trained across operation categories and human-motion sources, together with 2) BiHOI, a multi-view dataset of …
@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 others},
year = {2026},
eprint = {2610.29997},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29997},
}
Jingxing Qian, Xintong Du, Jun Yang, Siqi Zhou et al.
Abstract摘要
Mobile manipulators must navigate safely in cluttered, partially unknown environments across logistics, manufacturing, and service applications. Semi-static changes between robot visits can lead to outdated scene knowledge and increased collision risk. We propose an active perception framework that enables a high degree-of-freedom (DoF) mobile manipulator to inspect uncertain or safety-critical regions with its hand camera during navigation. A probabilistic, object- and task-aware next-best-view (NBV) formulation prioritizes observations near the desired path and around uncertain or …
@misc{qian2026object,
title = {{Object- and Task-Aware Active Perception for Safe Navigation of Mobile Manipulators in Semi-Static Environments}},
author = {Jingxing Qian and Xintong Du and Jun Yang and Siqi Zhou and Steven L. Waslander and Angela P. Schoellig},
year = {2026},
eprint = {2610.29964},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29964},
}
Xingyi He, Zhiwen Qiu, Adhitya Polavaram, Yunhao Cao et al.
Abstract摘要
We present SpatialDex, a framework that learns 3D-grounded bimanual dexterity from RGB-only human videos through reconstruction-augmented visuomotor pre-training. The central idea is to learn human behavior together with its spatial context. From each video, we reconstruct the scene geometry and estimate the hand motion, align them at a shared video-level scale using the hand-size prior of 3D hand estimates. We retain the reconstructed geometry as both a policy input and a prediction target. The policy grounds the token grid of a pre-trained vision-language model in this geometry through …
@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},
}
Yaohua Zhou, Mingdao Lin, Jiayu Song, Xixin Zhao et al.
Abstract摘要
This article investigates the joint-angle invariance (JAI) of Jacobian-based performance indices for serial robotic manipulators. We identify kinematic conditions under which representative performance indices, including manipulability, stiffness, and other Jacobian-derived indices, remain independent of the first and last joint angles. This property allows a systematic reduction in the effective configuration-space dimensionality, thereby simplifying the performance evaluation. The utility of JAI is demonstrated through two application scenarios: workspace performance map construction and …
@misc{zhou2026joint,
title = {{Joint-Angle Invariance of Jacobian-Based Performance Indices for Serial Robotic Manipulators}},
author = {Yaohua Zhou and Mingdao Lin and Jiayu Song and Xixin Zhao and Haidong Huang and Jiahu Qin and Xiaocong Li},
year = {2026},
eprint = {2610.29570},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29570},
}
In this article, we propose ContactFlowFilter, a novel approach for contact estimation that determines the number of active contacts, their locations, and the associated force vectors. We consider a robot manipulator equipped with joint torque sensors and a base force/torque sensor; such a proprioceptive-only setup presents a challenging inverse problem characterized by (i) multi-modality from non-unique solutions, (ii) temporal dependencies in state evolution, and (iii) discrete contact events involving contact addition/removal. Existing approaches, such as particle filters (PFs) and …
@misc{han2026mixture,
title = {{Mixture of Flow Matching and Particle Filter for Proprioception-Based Online Contact Estimation}},
author = {Seo Wook Han and Min Jun Kim},
year = {2026},
eprint = {2610.29568},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29568},
}
Xuhan Miao, Zijin Han, Yutong Shen, Haidong Huang et al.
Abstract摘要
Teleoperation enables intuitive control of remote engineering robots for precision tasks. Existing direct joint-mapping methods have two limitations: independent motor saturation disrupts pitch-roll coordination in differential wrists, while fixed mappings struggle with abrupt tracking changes caused by gravitational loads and actuation constraints. We propose an actuator-constraint-aware leader-follower teleoperation method that jointly models leader gravity compensation, leader-follower joint mapping, and follower differential-wrist control. Specifically, we perform coordinated saturation …
@misc{miao2026preserving,
title = {{Preserving Operator Intent Under Actuation Constraints: Teleoperation of Manipulators with Coupled Transmissions}},
author = {Xuhan Miao and Zijin Han and Yutong Shen and Haidong Huang and Shanliushui Gao and Lei Zhang and Xiaocong Li and Chenguang Yang},
year = {2026},
eprint = {2610.29565},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29565},
}
Taiga Ikeshima, Sheila Sutjipto, Lan Wu, Francisco J. Naranjo-Campos et al.
Abstract摘要
Robust and reactive collision avoidance remains a central challenge for deploying Reinforcement Learning (RL) policies on real robotic manipulators, particularly when transferring behaviours learned in simulation to the physical world. This paper presents an RL framework that integrates the Euclidean Distance Field (EDF) directly into the observation space, providing the agent with continuous geometric cues that support stable and responsive avoidance behaviours. By leveraging the rich spatial information encoded in the EDF, the method enables a streamlined reward formulation that achieves …
@misc{ikeshima2026distance,
title = {{Distance Field Driven Reinforcement Learning for Reactive and Robust Collision Avoidance}},
author = {Taiga Ikeshima and Sheila Sutjipto and Lan Wu and Francisco J. Naranjo-Campos and Taiki Ishigaki and Taisuke Kobayashi and Teresa A. Vidal-Calleja and Eiichi Yoshida},
year = {2026},
eprint = {2610.29452},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29452},
}
Jiahui Yang, Jason Jingzhou Liu, Ray Muxin Liu, Andrew E. Wang et al.
Abstract摘要
Mobile manipulators must adapt not only to objects, but also to the spatial context in which they appear: where to position the base, how to manipulate, and where to look while navigating surrounding scene geometry. To address this, we present COWBOY, a scalable sim-to-real framework for learning such contextual whole-body manipulation. On whole-body dexterous grasping, COWBOY transfers zero-shot to hundreds of objects and diverse in-the-wild scenes, spanning cluttered tabletops, shelves, drawers, and other unstructured settings. Rather than learning scene adaptation directly with RL in …
@misc{yang2026cowboy,
title = {{COWBOY: A Scalable Sim-to-Real Framework for Learning Contextual Whole-Body Manipulation}},
author = {Jiahui Yang and Jason Jingzhou Liu and Ray Muxin Liu and Andrew E. Wang and Kenneth Shaw and Deepak Pathak and Ruslan Salakhutdinov},
year = {2026},
eprint = {2610.28948},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.28948},
}
Huanyu Tian, Adriana Namour, Lingyun Zeng, Zicong Wu et al.
Abstract摘要
General-purpose manipulators can position ophthalmic instruments under a software Remote Centre of Motion (RCM). However, at some reachable configurations that are nonsingular for the tip task alone, the RCM and tip-tracking objectives conflict and the servo oscillates. We formalise this failure as a constraint-induced task singularity: the RCM-projected tip Jacobian loses rank while the unprojected tip Jacobian stays well conditioned. Thus, the effective tip authority collapses although the target remains reachable. We present a high-precision RCM servo architecture with built-in singularity …
@misc{tian2026high,
title = {{High-Precision RCM Motion for Ophthalmic Robotics via Hierarchical QP Control and Authority-Guided Recovery Planning}},
author = {Huanyu Tian and Adriana Namour and Lingyun Zeng and Zicong Wu and Sebastien Ourselin and Lyndon da Cruz and Christos Bergeles},
year = {2026},
eprint = {2610.27905},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.27905},
}
Karl-Johan Sorensen, Nico vom Hofe, Frank H. P. Fitzek
Abstract摘要
Mobile legged robots offer unique advantages for on-site construction, including the ability to traverse unstructured terrain, climb stairs, and operate independently between spatially separated work zones. This paper presents a proof-of-concept system for autonomous brick stacking using a Boston Dynamics Spot quadrupedal robot equipped with a manipulator arm. The system operates between a supply area and an assembly area, using AprilTag fiducials for localization and autowalk missions for navigation. Target brick positions and orientations are specified in CAD software and exported directly …
@misc{sorensen2026towards,
title = {{Towards Autonomous Bricklaying Using Boston Dynamics Spot}},
author = {Karl-Johan Sorensen and Nico vom Hofe and Frank H. P. Fitzek},
year = {2026},
eprint = {2610.27472},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.27472},
}
Tianwei Luo, Yiming He, Hongjin Wu, Ting Pan et al.
Abstract摘要
General-purpose manipulation requires reliable behavior across embodiments, objects, and task demands. We introduce HandsOn, a composable simulation platform and benchmark for cross-embodiment dexterous manipulation. It supports five robotic arms, six dexterous hands, and approximately 200 rigid, articulated, and deformable assets in randomized scenes. The benchmark pairs 18,000 curated human demonstrations with 66 human-verified tasks: Core-50 supports demonstration-based learning and execution tests, while Transfer-16 evaluates asset and relation transfer, skill composition, and deformable …
@misc{luo2026handson,
title = {{HandsOn: A Large-Scale Simulation Benchmark for Generalizable Dexterous Manipulation}},
author = {Tianwei Luo and Yiming He and Hongjin Wu and Ting Pan and Debin Ren and Haorui Guo and Junlin Yang and Yuxin Zuo and others},
year = {2026},
eprint = {2610.26765},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.26765},
}
Langzhe Gu, Chengkai Hou, Meng Li, Xinhua Wang et al.
Abstract摘要
Humanoid robots hold great promise as general-purpose agents in human-centered environments, yet adapting generalist vision-language-action (VLA) policies to real-world humanoid loco-manipulation remains challenging. Unlike fixed-base or wheeled manipulators, humanoids must coordinate locomotion, posture, gaze, and dual-arm manipulation over a high-dimensional whole-body action space, where inaccurate base or waist motions can easily induce unstable and aggressive upper-body compensation. We propose HAF-VLA, a hierarchical action-flow framework for humanoid VLA control. Built upon a …
@misc{gu2026haf,
title = {{HAF-VLA: Hierarchical Action Flow Framework for Humanoid Vision-Language-Action Control}},
author = {Langzhe Gu and Chengkai Hou and Meng Li and Xinhua Wang and Xinyuan Lv and Shuanghao Bai and Guangrun Li and Jingyang He and others},
year = {2026},
eprint = {2610.26584},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.26584},
}
Shuyuan Hu, Deng-Ke Wu, Yifan Jin, Qianhang Luo et al.
Abstract摘要
A bimanual demonstration specifies both what the robot should do and one particular schedule for when each arm should act. Standard imitation learning does not distinguish task-required cross-arm constraints from incidental scheduling choices, and may therefore exploit opposite-arm progress as a shortcut cue, creating spurious cross-arm coupling even where relative timing is irrelevant for the current task phase. We introduce Constraint-preserving Temporal Recombination (CTR), a training-time data intervention that preserves recorded arm-local sensorimotor content while retiming the …
@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},
}
Fukang Liu, Feiyang Wu, Paul Barsa, Jaehwi Jang et al.
Abstract摘要
Humanoid robots are envisioned to perform diverse human-level tasks through their anthropomorphic morphology. Their dual-arm configuration enables a broad range of everyday actions. Yet, as in humans, this morphology can constrain loco-manipulation when both arms are occupied or additional contacts are required beyond their reach. Supernumerary robotic limbs (SRLs) offer a way to extend these capabilities, but their integration requires the added limb to be coordinated with whole-body motion and balance. In this study, we develop and integrate a supernumerary arm into a humanoid platform to …
@misc{liu2026humanoid,
title = {{Humanoid SRL: Augmenting Humanoids with Supernumerary Limbs for Coordinated Loco-Manipulation}},
author = {Fukang Liu and Feiyang Wu and Paul Barsa and Jaehwi Jang and Akshat Jain and Zhigen Zhao and Yipu Chen and Zhaoyuan Gu and others},
year = {2026},
eprint = {2610.25628},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25628},
}
Yong Wang, Kecheng Zheng, Xing Zhu, Chao Xu et al.
Abstract摘要
Bimanual manipulation with dexterous hands has attracted growing attention in research on robotic action models. However, few studies have developed benchmarks specifically tailored for dexterous manipulation, leaving the dexterous manipulation capabilities of existing models insufficiently characterized. In this paper, we present BiDex-100, a simulation benchmark for bimanual dexterous manipulation that combines broad task coverage, multi-dimensional evaluation, and high-DoF hand embodiments. Concretely, the benchmark comprises 106 tasks organized along five capability axes: generalization, …
@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},
}
Christopher Leet, Achyutha Menon, Sravanthi Machcha, Sven Koenig et al.
Abstract摘要
General purpose multimodal language models (LMs) are increasingly capable of directly controlling robotic arms (e.g., by commanding end effector poses) to manipulate their environment. Tracking the development of these capabilities is important for understanding their potential benefits, such as improving industrial automation, and risks, such as enabling malicious users to produce dangerous chemical or biological agents. To this end, we investigate the ability of frontier multimodal LMs to directly control physical robotic arms. We evaluate seven models at multiple reasoning-effort settings …
@misc{leet2026bitter,
title = {{The Bitter Lesson Comes to Robotics}},
author = {Christopher Leet and Achyutha Menon and Sravanthi Machcha and Sven Koenig and Tzu Kit Chan and Jay Chooi},
year = {2026},
eprint = {2610.25287},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25287},
}
Shifei Duan, Zhe Wang, Francesco De Pace, Minas Liarokapis
Abstract摘要
Nowadays, robotic telemanipulation enables humans to control robots so as to execute tasks in a manner similar to how they perform them, and serves as an essential approach for collecting on-robot data for robot learning by human demonstrations. The quality of collected demonstrations directly impacts the performance of the learned policy. However, existing telemanipulation systems rely heavily on human operation, particularly for large-scale data collection. Collecting dozens or hundreds of demonstrations is a demanding process, as operators tend to fatigue quickly, especially during the …
@misc{duan2026scalable,
title = {{Scalable, Intuitive, Mixed Reality-Based Semi-Autonomous Robotic Telemanipulation for the Execution of a Fine-Grained Bimanual Task}},
author = {Shifei Duan and Zhe Wang and Francesco De Pace and Minas Liarokapis},
year = {2026},
eprint = {2610.25006},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25006},
}
Jingxiang Guo, Haoyu Zhao, Xidong Zhang, Yuhang Zheng et al.
Abstract摘要
Universal Manipulation Interface (UMI) pairs wrist-camera videos with camera trajectories and gripper openings, providing scalable, robot-free data for learning how bimanual actions change a shared scene. Existing interactive world models, however, typically rely on text or single-camera control and struggle to follow each arm's actions while maintaining consistency between the two wrist views. We introduce UMI-WM, an action-conditioned bimanual world model that jointly generates both wrist views from observation history, per-arm relative camera poses, and gripper widths, with cross-view …
@misc{guo2026umi,
title = {{UMI-WM: Interactive World Modeling for Bimanual Manipulation from Wrist Cameras}},
author = {Jingxiang Guo and Haoyu Zhao and Xidong Zhang and Yuhang Zheng and Hao Wu and Chen Gao and Jianshu Zhou and Shuicheng Yan and others},
year = {2026},
eprint = {2610.23913},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.23913},
}
Mariana da Silva Barros, Matthias Kerzel, Cornelius Weber, Stefan Wermter
Abstract摘要
A robotic agent operating in an unknown environment must gather sufficient task-relevant information before acting. However, determining when the available information is sufficient remains a challenge. We propose a conceptual hybrid neuro-symbolic framework for task-sufficient active perception in unknown environments. After interpreting a requested task, the agent generates an initial plan and iteratively reevaluates it as new information becomes available. At each iteration, it can acquire a more informative viewpoint, inspect or manipulate candidate objects, ask the user for …
@misc{barros2026when,
title = {{When to Make a Decision: Task-Sufficient Active Perception for Mobile Manipulator Robots}},
author = {Mariana da Silva Barros and Matthias Kerzel and Cornelius Weber and Stefan Wermter},
year = {2026},
eprint = {2610.23891},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.23891},
}
Qinglian Wu, Yidian Hu, Weiqi Fan, Yutian Zhang et al.
Abstract摘要
Long-range throwing on unstructured terrain is a key capability for mobile robots in delivery and retrieval tasks, but it requires high release velocity, coordinated whole-body motion, and robustness to terrain-induced disturbances. We present a whole-body kinetic-coordination framework for a wheeled-quadruped manipulator that combines kinetic-chain arm modeling with SQP-based trajectory optimization and a reference-guided reinforcement learning policy for coordinated base motion. The manipulator is first reduced to a kinetic-chain model and optimized under kinematic and dynamic constraints …
@misc{wu2026learning,
title = {{Learning Whole-Body Kinetic Coordination for Wheeled-Quadruped Long-Range Throwing}},
author = {Qinglian Wu and Yidian Hu and Weiqi Fan and Yutian Zhang and Siyuan Ma and Jiaqi Zhai and Wei Dong and Li Xu},
year = {2026},
eprint = {2610.23883},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.23883},
}
Many manipulation tasks that are effortless for dexterous human hands remain challenging for robotic systems equipped with simple, task-specialized grippers. Industrial platforms commonly rely on end-effectors such as suction cups or parallel-jaw grippers, yet no single mode can reliably satisfy the diverse constraints encountered across different phases of complex manipulation tasks, including pickup, reorientation, insertion, and constrained placement. In this work, we investigate whether complementary simple grippers within a heterogeneous bimanual setup can collectively realize tasks that …
Yanzhou Jin, Fei Suo, Chang Liu, Yasuhisa Hasegawa et al.
Abstract摘要
Legged robots equipped with manipulators offer promising capabilities for loco-manipulation tasks in unstructured environments. However, achieving task-consistent end-effector behavior during locomotion remains challenging due to terrain-induced disturbances and the strong coupling between locomotion and manipulation, particularly for small-scale quadruped platforms. A reinforcement-learning based whole-body control framework is proposed for quadruped loco-manipulation on complex terrain. The proposed framework combines a hybrid task representation with task-prioritized whole-body learning to …
@misc{jin2026reinforcement,
title = {{Reinforcement Learning Based Multi-Terrain Adaptive Whole-Body Control for a Quadruped Manipulator}},
author = {Yanzhou Jin and Fei Suo and Chang Liu and Yasuhisa Hasegawa and Liming Chen and Yusuke Iwasawa and Yutaka Matsuo and Yaonan Zhu},
year = {2026},
eprint = {2610.23548},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.23548},
}