Co-Adaptation in Human-Robot Training Scenarios

In human-robot collaboration scenarios, mutual adaptation between the human and robot must occur to ensure high task performance. This requires robotic systems to be capable of reasoning based on a long-term history of interactions. In this paper, we present and evaluate a robot simulation system that facilitates adaptive robot behavior using ontology-based reasoning and behavior trees in an interactive robotic scanning task. A study with 38 participants compares our adaptive system with a static system in team performance and perceived system usability. Our results suggest that use of the adaptive system significantly reduced session time, leading users to perform the task 19.5% faster. Furthermore, participants reported significantly lower fatigue levels, while maintaining the same task performance as those using the static system.