无需标定，少量人类引导即可实现灵巧手高效远程操控
AnyDexRT: Calibration-Free Dexterous Hand Retargeting with Few-Shot Human Guidance
- 自监督指尖对应+少样本人类引导，自动建立映射关系
- 在多种灵巧手上实测，操控更直观、误差更低
- 适合快速部署到不同机器人手，减少人工调参
遥操作是控制灵巧机器人手的关键接口，也是模仿学习示范数据的重要来源。其效果主要依赖于运动学重定向，即把操作者手部动作映射为可行且自然的机器人手动作。现有方法通常需要手工设计目标函数、精确标定或全局手形匹配，对个体差异敏感，跨手适应性差。我们提出 AnyDexRT，一种无需标定的灵巧手重定向方法，可在不同类人灵巧手上实现直观的遥操作。该方法结合自监督指尖对应学习与少样本人类引导，将映射锚定在任务相关区域，并利用接触分类器进一步优化捏合类姿态。在多种灵巧手和真实遥操作任务上的实验表明，AnyDexRT显著提升重定向质量，减少人工调参，提供更直观高效的控制体验。
原文摘要 · Abstract (English)
Teleoperation is a key interface for controlling dexterous robotic hands and collecting demonstrations for imitation learning. Its effectiveness largely depends on kinematic retargeting, which maps operator hand motions to feasible and intuitive robot hand motions. Existing methods often require hand-crafted objectives, precise calibration, or global shape matching between human and robot hand spaces, making them sensitive to hand-specific tuning and less reliable across different dexterous hands. We propose AnyDexRT, a calibration-free retargeting method for intuitive dexterous teleoperation across human-like dexterous hands. AnyDexRT combines self-supervised fingertip correspondence learning with few-shot human guidance to anchor the mapping in task-relevant regions, and further refines pinch-related poses using a contact classifier. Experiments on diverse dexterous hands and real-world teleoperation tasks show that AnyDexRT improves retargeting quality, reduces manual tuning, and provides more intuitive and efficient control than prior retargeting methods. Project website: https://chenxi-wang.github.io/projects/anydexrt
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