Haptic guidance is a powerful technique to combine the strengths of humans and autonomous systems for teleoperation. The autonomous system can provide haptic cues to enable the operator to perform precise movements; the operator can interfere with the plan of the autonomous system leveraging his/her superior cognitive capabilities. However, providing haptic cues such that the individual strengths are not impaired is challenging because low forces provide little guidance, whereas strong forces can hinder the operator in realizing his/her plan. Based on variational inference, we learn a Gaussian mixture model (GMM) over trajectories to accomplish a given task. The learned GMM is used to construct a potential field which determines the haptic cues. The potential field smoothly changes during teleoperation based on our updated belief over the plans and their respective phases. Furthermore, new plans are learned online when the operator does not follow any of the proposed plans or after changes in the environment. User studies confirm that our framework helps users perform teleoperation tasks more accurately than without haptic cues and, in some cases, faster. Moreover, we demonstrate the use of our framework to help a subject teleoperate a 7 DoF manipulator in a pick-and-place task. GRAPHICAL ABSTRACT

中文翻译：

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通过混合虚拟指南在不断变化的环境中辅助遥操作

触觉引导是一种强大的技术，可以结合人类和自主系统的优势进行远程操作。自主系统可以提供触觉提示，使操作员能够执行精确的动作；操作员可以利用他/她卓越的认知能力干扰自主系统的计划。然而，提供触觉提示以使个人力量不受损害是具有挑战性的，因为低力提供很少的指导，而强大的力会阻碍操作员实现他/她的计划。基于变分推理，我们在轨迹上学习高斯混合模型 (GMM) 以完成给定的任务。学习到的 GMM 用于构建确定触觉线索的势场。根据我们对计划及其各自阶段的更新信念，势场在遥操作期间会平滑地变化。此外，当操作员不遵循任何提议的计划或在环境发生变化后，在线学习新计划。用户研究证实，我们的框架可以帮助用户比没有触觉提示更准确地执行远程操作任务，并且在某些情况下更快。此外，我们还演示了如何使用我们的框架来帮助受试者在拾取和放置任务中远程操作 7 DoF 机械手。图形概要 在某些情况下，更快。此外，我们还演示了如何使用我们的框架来帮助受试者在拾取和放置任务中远程操作 7 DoF 机械手。图形概要 在某些情况下，更快。此外，我们还演示了如何使用我们的框架来帮助受试者在拾取和放置任务中远程操作 7 DoF 机械手。图形概要