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arXiv · 2610.15769

Hand2Robot: Object-Centric 4D Hand Reconstruction from Human Demonstrations for Robotics

Zetao Du·Weiqing Wang·Hanbing Zhang·Yimiao Zhou·Ye Shi·Liang Wang·Jingya Wang
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Abstract

Human demonstrations can capture task-relevant manipulation without using the target robot, but the recorded hand motions cannot directly serve as robot action supervision. Hand reconstruction and motion retargeting provide a direct route to such supervision, yet recovering hand motion alone does not ensure physically consistent hand-object interactions. To address this issue, we introduce Hand2Robot, an object-centric 4D hand reconstruction framework for robot policy post-training. Given videos of humans performing the target task, Hand2Robot extracts interaction clips, reconstructs object geometry, and generates robot action supervision through hand-object interaction optimization and motion retargeting. For object tracking, we combine observation confidence with temporal motion constraints to select a globally consistent object pose trajectory from per-frame pose candidates. For hand refinement, we use object geometry and motion as a reference and combine staged 2D alignment with object-local contact constraints to refine hand trajectories and finger joint configurations. Experiments on DexYCB and HO3D-v3 show improved object pose accuracy and hand-object interaction quality. Experiments on three real-robot dexterous manipulation tasks further show that reconstructed and retargeted human demonstrations can effectively supplement limited robot data for target-task policy post-training.

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Zetao Du, Weiqing Wang, Hanbing Zhang, Yimiao Zhou, Ye Shi, Liang Wang, Jingya Wang. 2026-10-11. Hand2Robot: Object-Centric 4D Hand Reconstruction from Human Demonstrations for Robotics. https://arxiv.org/abs/2610.15769

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