Influence of Operator Expertise on Robot Supervision and Intervention

TL;DR

This study explores how operators with different levels of robotics expertise perceive, decide, and intervene during remote robot supervision, revealing distinct patterns in intervention timing and decision-making strategies.

Contribution

It provides new insights into how user expertise influences supervision behavior and decision-making in human-robot teams during autonomous exploration tasks.

Findings

Different intervention timing patterns across expertise levels

Varying decision-making strategies among novice, intermediate, and expert users

Insights into designing better training and interfaces for diverse users

Abstract

With increasing levels of robot autonomy, robots are increasingly being supervised by users with varying levels of robotics expertise. As the diversity of the user population increases, it is important to understand how users with different expertise levels approach the supervision task and how this impacts performance of the human-robot team. This exploratory study investigates how operators with varying expertise levels perceive information and make intervention decisions when supervising a remote robot. We conducted a user study (N=27) where participants supervised a robot autonomously exploring four unknown tunnel environments in a simulator, and provided waypoints to intervene when they believed the robot had encountered difficulties. By analyzing the interaction data and questionnaire responses, we identify differing patterns in intervention timing and decision-making strategies across novice, intermediate, and expert users.