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arXiv 提交日期: 2026-07-07
📄 Abstract - WristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation

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.

顶级标签: robotics reinforcement learning
详细标签: humanoid control retargeting contact-rich manipulation wrist-guided 或 搜索:

WristMimic:基于手腕引导的全身仿人操控方法 / WristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation


1️⃣ 一句话总结

本文提出一种名为WristMimic的全身控制框架,核心思路是通过仅追踪人体非接触部位(如躯干和手腕)的运动姿态,而让手指通过强化学习自主学会抓握和操作物体,从而在物理仿真中更鲁棒地复现人机交互演示。

源自 arXiv: 2607.06438