发表机构
Northwestern University; Center for Robotics and Biosystems（西北大学; 机器人与生物系统中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对远程操作机器人性能不及人类灵巧性的问题，通过实验证明空间分布的触觉反馈可提升操作表现、降低轨迹偏差，还能压缩动作状态空间分布，助力自主机器人训练。
AI 中文摘要
机器人远程操作的一项根本挑战是让操作员能像控制自己的双手一样轻松、直观地控制远程机器人。尽管远程操作越来越多地用于收集演示数据以训练自主机器人策略，但即使是基础任务，远程操作机器人的性能仍远不及人类灵巧性。本文提供证据表明，导致这种性能差距的关键因素是缺乏空间分布的触觉反馈。我们使用一个两自由度（DoF）双边力反馈远程操纵器，搭配一个32自由度（DoF）触觉指尖显示器，结果显示当远程操纵器上的局部变形被忠实地复现在操作员指尖时，操作员的性能显著提升。在一系列远程操作任务中，复现分布式接触信息不仅加快了任务执行速度，还通过减少修正动作和任务完成步骤，让远程操作动作更接近人类自然行为，从而将远程操作轨迹与自然轨迹之间的偏差降低了29%至79%。此外，我们发现提高触觉反馈的分辨率——即细化测量位移用于复现的量化精细度——会压缩远程操作动作的状态空间分布，而这与自主机器人策略训练结果的提升相关。综上，这些结果表明，空间分布的触觉反馈对于缩小人类与远程操作灵巧性之间的差距、训练下一代自主机器人至关重要。
英文摘要
A fundamental challenge in robotic teleoperation is enabling an operator to control a remote robot as effortlessly and intuitively as their own hands. Despite the growing use of teleoperation to collect demonstration data for training autonomous robot policies, teleoperated robot performance still falls significantly short of human dexterity, even for basic tasks. Here, we present evidence that a key factor contributing to this performance gap is the absence of spatially distributed tactile feedback. Using a two-degree-of-freedom (DoF) bilateral force-feedback telemanipulator paired with a 32-DoF tactile fingertip display, we show that operator performance improves significantly when localized deformations on the remote manipulator are faithfully reproduced on the operator's fingertip. In a series of teleoperation tasks, reproducing distributed contact information not only accelerated task performance but also brought teleoperated movements closer to natural human behavior by minimizing corrective actions and task completion steps, thereby reducing the deviation between teleoperated and natural trajectories by 29$\unicode{x2013}$79%. Furthermore, we found that increasing the resolution of the tactile feedback$\unicode{x2014}$by refining how finely the measured displacements were quantized for reproduction$\unicode{x2014}$compressed the state-space distribution of teleoperated motions, which has been associated with improved training outcomes for autonomous robot policies. Together, these results suggest that spatially distributed tactile feedback is essential for closing the gap between human and teleoperated dexterity and training the next generation of autonomous robots.
