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HandMimicX: Learning Object-Centric Dexterous Manipulation Policies from Egocentric Videos

HandMimicX proposes an object-centric framework for learning dexterous manipulation skills from egocentric human RGB-D videos. The method defines the reinforcement learning objective in terms of object motion, decoupling skills from specific hand trajectories, and imposes constraints on hand-object interaction states during grasping and manipulation to obtain more anatomically plausible hand configurations. Simulation and real-robot experiments show that the method learns efficiently from human videos and transfers skills to novel spatial configurations.

Part of the event: HandMimicX: Learning Dexterous Manipulation from First-Person Videos

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