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WristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation

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Retargeting human object interaction demonstrations to physics based simulation requires reproducing not only body motion but also the object motion and contacts that make manipulation succeed. However, position only hand trajectories do not specify the contact forces needed to manipulate objects, and directly tracking them can overconstrain contact rich finger behavior. We introduce WristMimic, a wrist guided whole body control framework that explicitly separates contact free body motion from contact rich hand manipulation. The contact free body and wrist are guided by kinematic pose targets, whereas the fingers are not directly supervised by human hand pose. Instead, they learn grasping and manipulation behaviors from object tracking and contact outcomes. Our key insight is that the wrist is the natural gate between these two regimes. It is largely free from contact and can be tracked kinematically, yet it determines the global hand configuration and places the fingers within reachable grasp affordances. To ensure reliable wrist placement during interaction, we introduce wrist specific reset constraints and reward prioritization. Experiments show that WristMimic matches or surpasses methods using full finger pose supervision while enabling finger agnostic retargeting across diverse hand embodiments.

Wongyun Yu, Youngwoon Kim, Minsu Cho• 2026

Related benchmarks

TaskDatasetResultRank
Human-object interactionParaHome 20 interaction sequences
Success Rate83.3
3
Human-object interactionOMOMO & ParaHome (40 interaction sequences)
Success Rate91.1
3
Human-object interactionOMOMO 20 interaction sequences
Success Rate98.9
2
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