A Learning-Based Shared Control Approach for Contact Tasks

Youssef Michel, Zhendong Li, Dongheui Lee

Research output: Contribution to journalArticlepeer-review

Abstract

This work presents a novel shared control architecture dedicated to teleoperated contact tasks. We use Learning from demonstration as a framework to learn a task model that encodes the desired motions, forces and stiffness profiles. Then, the learnt information is used by a Virtual Fixture (VF) to guide the human operator along a nominal task trajectory that captures the task dynamics, while simultaneously adapting the remote robot impedance. Furthermore, we provide haptic guidance in a human-aware manner. To that end, we propose a control law that eliminates time dependency and depends only on the current human state, inspired by the path and flow control formulations used in the exoskeleton literature (Duschau-Wicke et al. (2010), Martínez et al. (2019)). The proposed approach is validated in a user study where we test the guidance effect for the bilateral teleoperation of a drawing and a wiping task. The experimental results reveal a statistically significant improvement in several metrics, compared to teleoperation without guidance.

Original languageEnglish
Pages (from-to)8002-8009
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume8
Issue number12
DOIs
StatePublished - 1 Dec 2023
Externally publishedYes

Keywords

  • Learning from demonstration
  • shared control
  • teleoperation
  • variable impedance control

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