人类在演示给机器人时的 gaze 行为：在不同演示设备下的拾取和放置任务中的人类 gaze 行为
Where Do Humans Look When Demonstrating to Robots? Human Gaze Behavior in Pick-and-Place Tasks Across Demonstration Devices
AI总结：
研究探讨了人类在不同机器人演示设备中 gaze 行为的变化，发现设备特性会影响 gaze 从任务目标线索转向控制监控线索，影响基于 gaze 的模仿学习性能。
摘要：
可泛化性能的模仿学习通常需要大量演示数据，使过程显著昂贵。一种有前景的策略是利用具有强泛化能力的人类演示者认知技能，特别是通过揭示其 gaze 行为中反映的任务需求。然而，模仿学习通常涉及人类使用模拟机器人本体和视觉条件的演示设备收集数据。这引发了此类设备如何影响 gaze 行为的问题。我们提出了一种实验框架，系统分析人类演示者在一系列机器人模拟演示设备上的 gaze 行为。我们的实验结果表明，某些设备特性会将 gaze 从任务目标线索（如物体）转向控制监控线索（如末端执行器）。此外，这些转变直接影响典型基于 gaze 的模仿学习模型的性能，有时使其性能低于非 gaze 基线。
英文摘要：
Imitation learning for generalizable performance often requires a large volume of demonstration data, making the process significantly costly. One promising strategy to address this challenge is to leverage the cognitive skills of human demonstrators with strong generalization capability, particularly by revealing the underlying task demands reflected in their gaze behavior. However, imitation learning typically involves humans collecting data using demonstration devices that emulate a robot's embodiment and visual condition. This raises the question of how such devices influence gaze behavior. We propose an experimental framework that systematically analyzes human demonstrators' gaze behavior across a spectrum of robot-emulating demonstration devices. Our experimental results show that certain device properties shift gaze from task-goal cues (e.g., objects) toward control-monitoring cues (e.g., the end-effector). Furthermore, these shifts directly affect the performance of typical gaze-based imitation learning models, sometimes degrading it below non-gaze baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
