发表机构
George Mason University（乔治梅森大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对多机器人监管界面设计中缺乏机器人行为调整的实证指导问题，开发了自标注工具Attune，通过眼动分析助力表征操作员注意力，为机器人行为校准提供依据。
AI 中文摘要
在复杂的真实环境中部署机器人集群需要人类操作员同时监管多台机器人。管理操作员的注意力是多机器人监管界面设计的核心挑战，涉及 feed 布局和 feed 内容（即机器人行为设计）。迄今为止，设计师在后者方面缺乏实证指导——即如何改变机器人行为以在多机器人监管过程中捕获、维持或转移操作员注意力。在我们对未来的设想中，设计师应能利用此类指导将机器人行为校准为不同操作员的注意力概况。我们将操作员的眼动视为机器人行为设计的线索，创建了名为 Attune 的预部署启发工具。Attune 自动识别有意义的眼动转移发生的时刻，提供 AI 辅助来标注转移发生的原因，并输出操作员眼动模式的摘要供操作员查看。我们通过一项用户研究评估了 Attune，参与者标注了吸引其注意力的视觉触发因素。我们的研究结果揭示了观察到的眼动模式存在差异，并表明 Attune 有助于表征操作员的注意力。
英文摘要
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.
Comments13 pages, 9 figures. To appear in the Proceedings of the 39th Annual ACM Symposium on User Interface Software and Technology (UIST '26)
