Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration
Overview
This paper presents CHORD, a framework for long-horizon dexterous manipulation of rigid and articulated objects using reinforcement learning with object-centric contact wrench guidance. The method represents human and robot motions via forces and torques on the object to measure similarity by induced instantaneous motions, improving scalability for contact-rich tasks. A large-scale simulation benchmark comprising 4,739 bimanual dexterous manipulation tasks was constructed from motion-capture datasets and in-house videos; evaluation on 1,831 tasks shows an average success rate of 82.12%. CHORD generalizes to whole-body manipulation from hand-only and third-person demonstrations with a 90.77% success rate and transfers to real-world open-loop and closed-loop settings.
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Reported quantity · Reported success rate: 82.12 percent · reported trials · Differing source assertions
“ation 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”
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Reported quantity · Reported success rate: 90.77 percent · reported trials · Differing source assertions
“ation 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”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalLearning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration
This paper presents CHORD, a framework for long-horizon dexterous manipulation of rigid and articulated objects using reinforcement learning with object-centric contact wrench guidance. The method represents human and robot motions via forces and torques on the object to measure similarity by induced instantaneous motions, improving scalability for contact-rich tasks. A large-scale simulation benchmark comprising 4,739 bimanual dexterous manipulation tasks was constructed from motion-capture datasets and in-house videos; evaluation on 1,831 tasks shows an average success rate of 82.12%. CHORD generalizes to whole-body manipulation from hand-only and third-person demonstrations with a 90.77% success rate and transfers to real-world open-loop and closed-loop settings.
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