# Hand2Robot：面向机器人的基于人类示范的物体中心4D手部重建

> 原文链接：https://robotics.hellobike.com/papers/2026-10-11-2610-15769
> 频道：papers | 更新日期：2026-10-11 | 标签：arxiv, cs.RO
> 核心摘要：Hand2Robot从人类任务视频中以物体为中心重建4D手部，并经交互优化和重定向生成监督，在基准与真机任务上验证。

# Hand2Robot：面向机器人的基于人类示范的物体中心4D手部重建

::: v-pre
> 原文链接：[Hand2Robot: Object-Centric 4D Hand Reconstruction from Human Demonstrations for Robotics](https://arxiv.org/abs/2610.15769)（arXiv 2610.15769 · 发布日期：2026-10-11）

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

Hand2Robot从人类任务视频中以物体为中心重建4D手部，并经交互优化和重定向生成监督，在基准与真机任务上验证。

## 核心要点

- 人类示范能捕获操作，但手部运动不能直接作为机器人动作监督。
- 该框架提取交互片段、重建物体几何，并生成机器人动作监督。
- 物体跟踪结合观测置信度与时间运动约束，选择全局一致姿态轨迹。
- 在DexYCB和HO3D-v3上提升物体姿态准确性与手-物交互质量。

**验证范围与局限**：验证范围包括DexYCB、HO3D-v3和三个真机灵巧操作任务。

## 英文摘要 (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.

作者：Zetao Du, Weiqing Wang, Hanbing Zhang, Yimiao Zhou, Ye Shi, Liang Wang, Jingya Wang
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## 原始参考来源 (Sources)

- [arXiv 2610.15769：Hand2Robot: Object-Centric 4D Hand Reconstruction from Human Demonstrations for Robotics](https://arxiv.org/abs/2610.15769)