论文/arXiv 2609.20649·1 个月前

DexTouch-WM：从人类触摸学习用于灵巧机器人操作的动作条件触觉世界模型

DexTouch-WM用可迁移的人类触摸学习动作条件世界模型，并在人到机器人扩展和真机策略学习中验证。

AI 整理

• 数据与采集
• 具身模型
• 评测与安全

原文链接：DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation（arXiv 2609.20649 · 发布日期：2026-09-17）

DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation

DexTouch-WM用可迁移的人类触摸学习动作条件世界模型，并在人到机器人扩展和真机策略学习中验证。

核心要点

• 密集触觉交互难以在真机上规模化，且常受具身特定传感器绑定。
• 通过共享布局柔性压阻阵列与动作重定向，使人类触摸监督机器人预测模型。
• 模型结合预训练视频专家和轻量触觉专家，预测未来RGB与双侧触觉动态。
• 增加人类交互后，留出机器人域的视觉、几何与接触预测显著改进。

验证范围与局限：验证限于摘要所述的人到机器人扩展、策略评估替代环境和真机策略学习，机器人监督固定为5小时。

英文摘要 (Abstract)

Learning predictive models of contact-rich dexterous manipulation requires dense tactile interaction, but such data are costly to scale on real robots and remain tied to embodiment-specific sensors. We introduce DexTouch-WM, an action-conditioned world model that learns from scalable human touch to jointly predict future RGB observations and bilateral tactile dynamics. Our insight is that human and robot manipulation share transferable contact dynamics when their tactile observations and action spaces are made compatible. We deploy flexible piezoresistive arrays with a shared sensing layout on both human and dexterous robot hands, and retarget human motion into the robot action space so that human interaction can supervise the same dynamics model used for real-robot prediction. DexTouch-WM couples a pretrained video expert with a lightweight tactile expert using anatomy-aware tactile tokens and aligned action conditioning. In human-to-robot scaling experiments, we keep five hours of real-robot supervision fixed while increasing human interaction from 0 to 100 hours, and observe substantial improvements in held-out robot-domain visual, geometric, and contact prediction despite disjoint human and robot task sets. Beyond prediction, we evaluate the world models as surrogate environments for policy evaluation and as generators of synthetic trajectories for real-robot policy learning, showing that scalable human interaction provides a complementary data axis for learning dexterous robot world models.

作者：Yan Qin, Yue Chen, Wenwei Lin, Shujia Liu, Chuqiao Lyu, Kailun Su, Weiyang Jin, Chenze Yu, Ping Luo, Wenbo Ding

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2609.20649：DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation访问 2026-10-21