Reproduce “HandMimicX: Learning Human-Like Dexterous Manipulation via an Object-Centric Policy from Egocentric Videos” on OpenResearch
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About this paper
Zihan Gao, Yufei Liu, Yuhong Zhang, Zhen Yu, Xinhao Huang, Zhinan You, Kai W. Ho, Zipeng Ye and 2 more
HandMimicX learns dexterous manipulation from an egocentric human video by targeting object motion rather than copying a fixed hand trajectory, while using demonstrated hand–object pose and contact regions to guide interaction. With object pretraining and task-specific finetuning, it reaches 85.4% mean simulation success and 71.3% real-world success across eight tested tasks, not broader guaranteed transfer.
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