Influence of Operator Expertise on Robot Supervision and Intervention
🤖 AI Summary
This study investigates how users with varying levels of expertise—novice, intermediate, and expert—differ in their intervention behaviors when supervising autonomous robots during tunnel exploration tasks, and how these differences impact human-robot team performance. Through a user study conducted in a simulated environment, complemented by interaction log analysis and post-task questionnaires, the research systematically reveals—for the first time—how operator proficiency significantly influences intervention frequency, timing, and decision-making logic. The findings provide empirical evidence to inform the design of personalized human-robot interaction strategies, ultimately enhancing collaboration efficiency and overall system performance.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
robot supervision
operator expertise
human-robot interaction
intervention decision-making
autonomous robots
Innovation
Methods, ideas, or system contributions that make the work stand out.
robot supervision
operator expertise
human-robot interaction
intervention decision-making
autonomous robots
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