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HandMimicX: Learning Dexterous Manipulation from First-Person Videos

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Zihan Gao and other authors released a preprint paper proposing the HandMimicX framework, which learns dexterous manipulation skills from first-person human RGB-D videos. The method defines the reinforcement learning objective as object motion, decoupling skills from specific hand trajectories, and imposes constraints on hand-object interaction states during grasping and manipulation to obtain hand configurations that better conform to anatomical structure. The authors state that simulation and real-robot experiments show the method can efficiently learn from human videos and transfer skills to new spatial configurations.

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  1. 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.

    arXiv cs.RO ·

Related topics

Grasping and manipulationRobot models and learningData and simulationResearch papers

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