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Enabling a robot to learn from human videos is hard; learning from a SINGLE human video is even harder, especially if you want to perform dexterous tasks. Hongyi Chen has a way -- learn more in our new episode on VideoManip ->

Chris Paxton

37,520 subscribers

21,810 次观看 • 5 个月前 •via X (Twitter)

Anya Rossi• Live Now

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Lizhi(Gary) Yang 的头像

Lizhi(Gary) Yang5 个月前

@chen_hongyi_ I remember this being one off the earliest works - the idea of learning inverse kinematic/dynamics has gone far

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Teaching robots to perform dexterous manipulation tasks currently requires teleoperation, which limits demonstration quality, speed, and scalability. Instead, why not use human videos? The problem is that a human hand isn’t a robot hand, so data must be retargeted using simulation to resolve issues like collisions and interpenetration when controlling the hand. In VideoManip, Hongyi Chen and co-authors built a system to solve this problem, taking in RGB videos of humans performing manipulation tasks and using them to create accurate simulations with which to learn robot policies. Watch episode #73 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton, now to learn more!

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