CHORD：利用人体示范的接触扳矩引导学习机器人灵巧操作
Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration
研究者提出 CHORD 框架，以物体中心的接触扳矩空间为引导，将人体示范与强化学习结合，用于刚体和铰接物体的长时程灵巧操作。该框架配套的大规模仿真基准包含 4,739 个双手灵巧操作任务，在 1,831 个评估任务上平均成功率达 82.12%；从手部第一人称和第三人称示范泛化到全身操作时成功率达 90.77%，且策略可迁移到真实世界的开环和闭环设置。
Authors:Xinghao Zhu, Zixi Liu, Shalin Jain, Chenran Li, Milad Noori, Michael Andres Lin, Huihua Zhao, John Welsh, Mrinal Verghese, Wei Liu, Tingwu Wang, Xingye Da, Zhengyi Luo, Vishal Kulkarni, Naema Bhatti, Yuke Zhu, Linxi Fan, Bowen Wen, Danfei Xu, Soha Pouya, Yan Chang
Abstract:Dexterous robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to robot policies remains challenging. We present Contact Wrench Guidance from Human Demonstration in Robotic Dexterous Manipulation (CHORD), a framework for long-horizon manipulation of rigid and articulated objects with reinforcement learning. The key idea is object-centric contact wrench space guidance: we represent human and robot motions by the forces and torques they can induce on the object, enabling similarity to be measured by the induced instantaneous motions. This guidance makes reinforcement learning more scalable for contact-rich dexterous manipulation. We further introduce a large-scale simulation benchmark with 4,739 bimanual dexterous manipulation tasks, constructed from motion-capture datasets and reconstructed in-house videos. Evaluated on 1,831 benchmark tasks, CHORD achieves an average success rate of 82.12%, demonstrating strong scalability. CHORD also generalizes to whole-body manipulation from hand-only and third-person demonstrations, achieving a 90.77% success rate, and the learned policies transfer to the real world in both open-loop and closed-loop settings.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2607.00033 [cs.RO] |
| (or arXiv:2607.00033v3 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2607.00033 arXiv-issued DOI via DataCite |
Submission history
From: Xinghao Zhu [view email]
[v1]
Mon, 22 Jun 2026 22:54:33 UTC (34,048 KB)
[v2]
Fri, 14 Aug 2026 05:48:56 UTC (34,050 KB)
[v3]
Tue, 10 Nov 2026 07:47:20 UTC (33,937 KB)
来源：arXiv cs.AI · arxiv.org