通过高斯过程残差学习，扩展机械手操作空间，实现更灵活的指尖操控。
ResPilot: Teleoperated Finger Gaiting via Gaussian Process Residual Learning
- 利用高斯过程残差学习，动态扩展机械手可达操作范围。
- 实验显示操作空间显著扩大，可完成全新灵巧指部移动任务。
- 适合需要精细手部操作的远程机器人控制场景。
灵巧机械手的遥操作能够远距离传递人类操作技能，同时为人类向机器人传授技能提供途径。然而，现有方法难以复现人手的功能工作空间，通常仅限于简单抓取任务。本文提出一种多指机械手的指部移动操纵新方法。通过残差高斯过程学习，扩展机械手的可达工作空间，增强操作者接触物体的灵活性；同时允许操作者约束手指协同运动，保持与物体的稳定接触。大量定量评估表明，该方法显著扩大了机械手的操作空间，并成功完成新型灵巧指部移动任务。
原文摘要 · Abstract (English)
Dexterous robot hand teleoperation allows for long-range transfer of human manipulation expertise, and could simultaneously provide a way for humans to teach these skills to robots. However, current methods struggle to reproduce the functional workspace of the human hand, often limiting them to simple grasping tasks. We present a novel method for finger-gaited manipulation with multi-fingered robot hands. Our method provides the operator enhanced flexibility in making contacts by expanding the reachable workspace of the robot hand through residual Gaussian Process learning. We also assist the operator in maintaining stable contacts with the object by allowing them to constrain fingertips of the hand to move in concert. Extensive quantitative evaluations show that our method significantly increases the reachable workspace of the robot hand and enables the completion of novel dexterous finger gaiting tasks. Project website: http://respilot-hri.github.io
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