用眼动追踪帮机器人设计者理解操作员注意力分布。
Attune: A Self-Annotation Tool for Understanding Robot Operator Attention Profiles
- 通过自动识别眼动变化，分析操作员注意力转移原因。
- 用户研究验证了工具能有效捕捉不同操作员的注意力模式差异。
- 适合人机交互、多机器人系统设计者使用。
在复杂真实环境中部署机器人车队时，需要人类操作员同时监督多个机器人。管理操作员注意力是设计多机器人监控界面的核心挑战，涉及信息流布局与内容（即机器人行为设计）。然而，当前设计者缺乏关于如何调整机器人行为以吸引、维持或释放操作员注意力的实证指导。我们提出一种预部署获取工具Attune，将操作员眼动作为行为设计线索：自动识别有意义的眼动转移，利用AI辅助标注转移原因，并输出操作员眼动模式总结供审查。通过用户研究，参与者对视觉触发因素进行标注，结果揭示了眼动模式的个体差异，并验证了Attune在刻画操作员注意力方面的有效性。
原文摘要 · Abstract (English)
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.
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