# Human2Dex：从腕部中心人类示范中进行灵巧机器人学习

> 原文链接：https://robotics.hellobike.com/papers/2026-10-09-2610-13119
> 频道：papers | 更新日期：2026-10-09 | 标签：arxiv, cs.RO
> 核心摘要：Human2Dex用腕部中心人类示范接口，通过手表示、重定向和GPIR，在两种灵巧手六项真机任务验证跨手复用学习。

# Human2Dex：从腕部中心人类示范中进行灵巧机器人学习

::: v-pre
> 原文链接：[Human2Dex: Dexterous Robot Learning from Wrist-Centric Human Demonstrations](https://arxiv.org/abs/2610.13119)（arXiv 2610.13119 · 发布日期：2026-10-09）

**Human2Dex: Dexterous Robot Learning from Wrist-Centric Human Demonstrations**

Human2Dex用腕部中心人类示范接口，通过手表示、重定向和GPIR，在两种灵巧手六项真机任务验证跨手复用学习。

## 核心要点

- 动机：为每种灵巧手反复采集任务示范成本高，希望自然人类示范语料可跨手复用。
- 方法：对齐手部观测，采用共享21点手表示，将人手运动重定向到目标手，并引入GPIR。
- 实验：六项真实世界任务中，Linker O6成功109/120，Wuji成功97/120。
- 结果：完整Human2Dex达206/240，优于Human-Raw的63/240；项目网站已公开。

**验证范围与局限**：验证范围为Linker O6和Wuji两种已校准灵巧手的六项真实世界任务。

## 英文摘要 (Abstract)

Collecting dexterous manipulation demonstrations for each robot hand requires repeated task-specific data acquisition. We ask whether one shared corpus of natural human demonstrations can instead serve as a reusable data interface for different dexterous hands, without collecting task demonstrations on each target robot. We present Human2Dex, a wrist-centric human demonstration interface that separates shared human observation and motion semantics from embodiment-specific execution. It aligns observations through local hand-appearance adaptation and a shared 21-point hand representation with consistent structural semantics across human and robot observations. It constructs executable supervision by retargeting fused human hand motion to each target hand, and introduces the Grasp-Pocket Interaction Representation (GPIR) to express tracked objects relative to a hand-centered Functional Grasp Pocket. The corpus is converted into separate training sets for Linker O6 and Wuji, while action labels, normalization, training, and diffusion-policy checkpoints remain hand-specific. Across six real-world tasks, Human2Dex achieves 109/120 (90.8%) successes on Linker O6 and 97/120 (80.8%) on Wuji. Across both hands, Human-Raw achieves 63/240 (26.3%) successes, whereas the complete Human2Dex configuration achieves 206/240 (85.8%). These results support the use of wrist-centric human demonstrations as a reusable interface across calibrated dexterous hands while retaining embodiment-specific policy learning. The project website is publicly available at https://human2dex.pages.dev/.

作者：Jiachen Zhang, Yuanyuan Li, Shaqi Luo
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## 原始参考来源 (Sources)

- [arXiv 2610.13119：Human2Dex: Dexterous Robot Learning from Wrist-Centric Human Demonstrations](https://arxiv.org/abs/2610.13119)