Visualization of Intended Assistance for Acceptance of Shared Control

TL;DR

This paper explores visualization techniques to enhance user acceptance of shared control in robotics by improving the legibility and predictability of assistive controllers through user studies.

Contribution

It introduces multiple visualization methods aimed at increasing user acceptance of shared control systems and evaluates their effectiveness through empirical user testing.

Findings

Visualizations improve perceived predictability of assistive controllers.

Effective visualizations increase user willingness to relinquish control.

Designing visualizations that enhance both legibility and predictability is crucial.

Abstract

In shared control, advances in autonomous robotics are applied to help empower a human user in operating a robotic system. While these systems have been shown to improve efficiency and operation success, users are not always accepting of the new control paradigm produced by working with an assistive controller. This mismatch between performance and acceptance can prevent users from taking advantage of the benefits of shared control systems for robotic operation. To address this mismatch, we develop multiple types of visualizations for improving both the legibility and perceived predictability of assistive controllers, then conduct a user study to evaluate the impact that these visualizations have on user acceptance of shared control systems. Our results demonstrate that shared control visualizations must be designed carefully to be effective, with users requiring visualizations that improve both legibility and predictability of the assistive controller in order to voluntarily relinquish control.