Adapting Robot's Explanation for Failures Based on Observed Human Behavior in Human-Robot Collaboration
This work addresses the specific problem of inefficient human-robot collaboration due to poorly timed or detailed explanations, though it is incremental as it builds on existing explanation adaptation methods with a new behavioral focus.
This research tackled the problem of robots providing ineffective explanations for failures during human-robot collaboration by developing a predictor of human confusion based on observed behavior (facial emotion, eye gaze, gestures) from 55 participants, and using it to adapt explanation levels, with promising results showing potential for enhanced collaboration.
This work aims to interpret human behavior to anticipate potential user confusion when a robot provides explanations for failure, allowing the robot to adapt its explanations for more natural and efficient collaboration. Using a dataset that included facial emotion detection, eye gaze estimation, and gestures from 55 participants in a user study, we analyzed how human behavior changed in response to different types of failures and varying explanation levels. Our goal is to assess whether human collaborators are ready to accept less detailed explanations without inducing confusion. We formulate a data-driven predictor to predict human confusion during robot failure explanations. We also propose and evaluate a mechanism, based on the predictor, to adapt the explanation level according to observed human behavior. The promising results from this evaluation indicate the potential of this research in adapting a robot's explanations for failures to enhance the collaborative experience.