HandMimicX: Learning Human-Like Dexterous Manipulation via an Object-Centric Policy from Egocentric Videos
Overview
This paper introduces HandMimicX, a method for learning dexterous manipulation from human videos by formulating reinforcement learning objectives around object motion rather than hand trajectories. The approach uses egocentric RGB-D videos to acquire skills that generalize across spatial configurations while maintaining physically plausible, anatomically natural hand-object interactions. Experiments in simulation and on real robots demonstrate efficient learning from human demonstrations and successful transfer to new environments.
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- arXiv Robotics — research abstractsSignalHandMimicX: Learning Human-Like Dexterous Manipulation via an Object-Centric Policy from Egocentric Videos
This paper introduces HandMimicX, a method for learning dexterous manipulation from human videos by formulating reinforcement learning objectives around object motion rather than hand trajectories. The approach uses egocentric RGB-D videos to acquire skills that generalize across spatial configurations while maintaining physically plausible, anatomically natural hand-object interactions. Experiments in simulation and on real robots demonstrate efficient learning from human demonstrations and successful transfer to new environments.
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