Reproduce “FoLD: Force-Informed Learning for Dexterous Articulated Object Manipulation” on OpenResearch
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About this paper
Haowei Shen, Tingai Li, Yumeng Liu, Wenyuan Guang, Xuanze Yang, Qing Fang, Kai Xu, Ligang Liu and 1 more
FoLD guides retargeted hand actions with a learned policy conditioned on a virtual force field derived from object-motion error. The field is guidance, not applied or measured force. Across four hands, mean normalized completion rises from 0.606 for DexMachina to 0.772, with lower aggregate motion errors, but gains are not uniform: XHand completion falls and Allegro position error rises.
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