HandMimicX: Learning Human-Like Dexterous Manipulation via an Object-Centric Policy from Egocentric Videos
HandMimicX: 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.
The authors propose an object-centric reinforcement learning framework that decouples skill from hand trajectory using egocentric RGB-D videos, with claims of generalizable transfer to novel spatial configurations supported by experiments in both simulation and on real robots.
Source: arXiv Robotics — research abstracts · Read original article ↗
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arXiv:2610.41336v1 Announce Type: new Abstract: Learning dexterous manipulation from human videos remains an open challenge, particularly in the context of transferring a single demonstration into a spatially generalizable robotic skill and maintaining physically plausible human-like interactions. Unlike prior works that emphasize hand-motion imitation, we introduce an object-centric framework for acquiring dexterous
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skills from egocentric human RGB-D videos. Within our framework, we formulate the reinforcement learning (RL) objective in terms of object motion to decouple the underlying skill from the specific hand trajectory and impose constraints on hand-object interaction states to promote more anatomically natural hand configurations. Extensive experiments in simulation and on real robots demonstrate that our method enables
Open source S3
Source：arXiv Robotics — research abstracts · arxiv.org