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This paper introduces Attune, a self-annotation tool designed to enhance the understanding of robot operator attention profiles by analyzing eye gaze patterns during multi-robot supervision. The tool identifies significant gaze shifts, assists operators in annotating the reasons behind these shifts, and summarizes gaze patterns for review, providing empirical insights into operator attention management. User studies demonstrate that Attune effectively reveals variations in gaze patterns, offering valuable guidance for designing robot behaviors that align with operator attention needs.
Operators' gaze patterns reveal critical insights into attention management, and Attune helps decode these patterns to optimize robot behavior design.
Deploying robot fleets in complex, real-world environments requires human operators to supervise multiple robots simultaneously. Managing operator attention is a fundamental challenge of designing multi-robot supervision interfaces, encompassing both feed layout and feed content (i.e., robot behavior design). Thus far, designers lack empirical guidance on the latter-how to change a robot's behavior to capture, sustain, or relinquish operator attention during multi-robot supervision. In our vision of the future, designers should be able to use this guidance to calibrate robot behavior to different operator attention profiles. Treating operator eye gaze as a robot behavior design clue, we created a pre-deployment elicitation tool called Attune. Attune automatically identifies when meaningful gaze shifts occur, provides AI assistance for annotating why shifts occurred, and outputs a summary of operator gaze patterns for operator review. We evaluated Attune through a user study in which participants annotated the visual triggers that drew their attention. Our findings unveil variation in observed gaze patterns and reveal how Attune helps characterize operator attention.
