# Touch2Robot：人类示教循环中的机器人触觉

> 原文链接：https://robotics.hellobike.com/papers/2026-09-21-2609-24660
> 频道：papers | 更新日期：2026-09-21 | 标签：arxiv, cs.RO
> 核心摘要：Touch2Robot将目标机器人手接触可视化纳入人类示教，并在四个真实世界任务上验证提升灵巧数据采集质量与效率。

# Touch2Robot：人类示教循环中的机器人触觉

::: v-pre
> 原文链接：[Touch2Robot: Robot Touch in the Human Demonstration Loop](https://arxiv.org/abs/2609.24660)（arXiv 2609.24660 · 发布日期：2026-09-21）

**Touch2Robot: Robot Touch in the Human Demonstration Loop**

Touch2Robot将目标机器人手接触可视化纳入人类示教，并在四个真实世界任务上验证提升灵巧数据采集质量与效率。

## 核心要点

- 人类示教易规模化采集操作数据，但其接触迁移到机器人手时可能不稳定或不可行。
- 框架记录人手运动、触觉手套测量和物体运动，用RL复现物体运动并偏好人类触觉一致接触。
- 学习行为被蒸馏为实时重定向器，将人类观测和物体几何映射到机器人手配置。
- 四个真实世界任务中，真实机器人回放完成率由37.9%升至72.1%，并提供项目网页。

**验证范围与局限**：验证范围为摘要所述四个真实世界任务、真实机器人回放、真实机器人触觉测量和下游Diffusion Policy训练。

## 英文摘要 (Abstract)

Human demonstrations offer a scalable way to collect manipulation data, but their contacts may be unstable or infeasible when transferred to a robot hand. Collecting demonstrations directly on the target robot avoids this mismatch but substantially increases the cost of data collection. To address this trade-off, we present Touch2Robot, a framework that lets humans collect demonstrations while seeing how the target robot hand would contact the object. We capture human hand motion, tactile-glove measurements, and object motion during human manipulation. These recordings guide object-specific RL policies to reproduce the demonstrated object motion while favoring contacts consistent with the recorded human touch. We distill the learned behaviors into a unified real-time retargeter that maps incoming human observations and object geometry to robot hand configurations. During collection, the predicted robot configuration is synchronized with the tracked object pose in simulation to reconstruct robot-object contacts, which are visualized to help the demonstrator adapt subsequent interactions to the target hand. Across four real-world tasks, Touch2Robot improves average real-robot replay completion from 37.9% to 72.1% over visual-only feedback, while reducing the collection time per replay-successful demonstration from 58.6s to 18.2s. Reconstructed target-hand contacts achieve 44.2% F1 against real-robot tactile measurements, and policies trained on Touch2Robot demonstrations improve downstream Diffusion Policy performance by 29.1 percentage points over visual-only feedback. These results show that bringing robot touch into the human demonstration loop improves both the quality and efficiency of scalable dexterous data collection. Project webpage: https://Touch2Robot.github.io/.

作者：Shengcheng Luo, Xiaoyang Cheng, Hong Ying, Xiaoying Zhou, Jiaming Jiang, Haoran Guo, Wanlin Li, Ziyuan Jiao, Chenxi Xiao
:::

## 原始参考来源 (Sources)

- [arXiv 2609.24660：Touch2Robot: Robot Touch in the Human Demonstration Loop](https://arxiv.org/abs/2609.24660)