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.

触觉引导是一种强大的技术，可结合人类和自治系统的优势进行远程操作。自主系统可以提供触觉提示，以使操作员能够执行精确的运动；操作员可以利用其出色的认知能力来干扰自主系统的计划。然而，提供触觉提示以使个人力量不被削弱是具有挑战性的，因为低力提供很少的指导，而强力会阻碍操作者实现他/她的计划。基于变分推理，我们学习了轨迹上的高斯混合模型（GMM）以完成给定的任务。学习到的GMM用于构建确定触觉提示的势场。根据我们对计划及其各个阶段的最新信念，在远程操作期间潜在领域会平稳变化。此外，当操作员不遵循任何建议的计划或环境变化后，可以在线学习新计划。用户研究证实，与没有触觉提示的情况相比，在某些情况下，我们的框架可帮助用户更准确地执行远程操作任务。此外，我们演示了如何使用我们的框架来帮助主体在拾取和放置任务中遥控7自由度操纵器。