A Shared Control Approach Based on First-Order Dynamical Systems and Closed-Loop Variable Stiffness Control

TL;DR

This paper introduces a novel shared control framework combining first-order dynamical systems and variable stiffness control, enabling adaptive, synchronized, and efficient teleoperation with improved success rates and user preference.

Contribution

The work presents a new learning-based shared control approach utilizing dynamical systems and variable stiffness control for enhanced authority allocation and task adaptation.

Findings

Highest success rate in target reaching tasks

Reduces execution time and task load

Preferred by users over other controllers

Abstract

In this paper, we present a novel learning-based shared control framework. This framework deploys first-order Dynamical Systems (DS) as motion generators providing the desired reference motion, and a Variable Stiffness Dynamical Systems (VSDS) \cite{chen2021closed} for haptic guidance. We show how to shape several features of our controller in order to achieve authority allocation, local motion refinement, in addition to the inherent ability of the controller to automatically synchronize with the human state during joint task execution. We validate our approach in a teleoperated task scenario, where we also showcase the ability of our framework to deal with situations that require updating task knowledge due to possible changes in the task scenario, or changes in the environment. Finally, we conduct a user study to compare the performance of our VSDS controller for guidance generation to two state-of-the-art controllers in a target reaching task. The result shows that our VSDS controller has the highest successful rate of task execution among all conditions. Besides, our VSDS controller helps reduce the execution time and task load significantly, and was selected as the most favorable controller by participants.