外观
论文arXiv 2609.338821 个月前
DexTaG：灵巧操作强化学习中的触觉作为引导
原文链接：DexTaG: Tactile-as-Guidance in Reinforcement Learning for Dexterous Manipulation（arXiv 2609.33882 · 发布日期：2026-09-27）
DexTaG: Tactile-as-Guidance in Reinforcement Learning for Dexterous Manipulation
DexTaG用手套触觉信号引导强化学习重定向人手示教，并在马克笔、锤子操作和OakInk2上验证。
核心要点
- 针对人手与机器人手运动学差异，手套采集的人手动作难以直接用于接触丰富的工具操作。
- 训练时用手套捕获的触觉信号引导策略搜索，保持人类接触模式并降低对精确几何监督的依赖。
- 同一物体的全部训练轨迹联合训练单个可泛化重定向器，并蒸馏为部署用的无触觉学生控制器。
- 在马克笔和锤子操作中学到自然接触丰富行为，泛化到同物体同任务留出轨迹，并在OakInk2上优于单轨迹基线。
验证范围与局限：验证范围限于摘要所述马克笔和锤子操作、同一物体同任务留出轨迹及OakInk2对比。
英文摘要 (Abstract)
Glove-based motion capture is emerging as a scalable approach to collecting dexterous-hand demonstration data. However, due to the kinematic gap between the human and robot hand, the recorded human motions cannot be executed directly on the robot, especially for contact-rich tool-use tasks involving in-hand reorientation. Prior work bridges this gap in simulation through reinforcement learning (RL) or trajectory optimization, but the human contact pattern is hard to preserve under such formulations, often producing unnatural manipulation and unstable functional grasps. These methods also train a separate policy or solve a separate optimization for each reference trajectory, which is inefficient. To solve these problems, we propose DexTaG, a tactile-guided RL framework for dexterous manipulation. During training, tactile signals captured by the glove guide policy search toward the measured human contact pattern, reducing reliance on precise reference geometry for contact supervision. To improve efficiency, we train a single generalizable retargeter jointly on all training trajectories of the same object. The retargeter is further distilled into a tactile-free student controller conditioned on the target object trajectory for real-world deployment. On marker-pen and hammer manipulation tasks, DexTaG learns natural, contact-rich behaviors that baselines with distance-based contact heuristics fail to learn, generalizes to held-out trajectories of the same object and task, and outperforms single-trajectory baselines on OakInk2.
作者：Han Yang, Yian Wang, Yunlong Song, Zhenjia Xu, Chuang Gan
来源
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