Disentangling Coordiante Frames for Task Specific Motion Retargeting in Teleoperation using Shared Control and VR Controllers

TL;DR

This paper introduces a formal motion retargeting method that separates translation and rotation inputs, integrated into an optimal control trajectory planner, to improve task performance in teleoperation with VR controllers.

Contribution

It proposes a novel formal motion retargeting approach that separates translation and rotation, enhancing teleoperation task efficiency and integrating it into an optimal control trajectory planner.

Findings

Separated translation and rotation improves task performance.

Method successfully applied to UR5e manipulator.

Enhanced control in complex teleoperation tasks.

Abstract

Task performance in terms of task completion time in teleoperation is still far behind compared to humans conducting tasks directly. One large identified impact on this is the human capability to perform transformations and alignments, which is directly influenced by the point of view and the motion retargeting strategy. In modern teleoperation systems, motion retargeting is usually implemented through a one time calibration or switching modes. Complex tasks, like concatenated screwing, might be difficult, because the operator has to align (e.g. mirror) rotational and translational input commands. Recent research has shown, that the separation of translation and rotation leads to increased task performance. This work proposes a formal motion retargeting method, which separates translational and rotational input commands. This method is then included in a optimal control based trajectory planner and shown to work on a UR5e manipulator.