不同演示设备影响人类看物方式，进而决定机器人学得快慢。
Where Do Humans Look When Demonstrating to Robots? Human Gaze Behavior in Pick-and-Place Tasks Across Demonstration Devices
- 用多种仿真机器人动作的设备对比人类注视行为。
- 设备不同，人更关注操作手柄而非物体，导致模型性能下降。
- 适合研究人机交互与模仿学习中数据质量的团队。
模仿学习实现通用化表现通常需要大量示范数据，成本高昂。一种有前景的策略是利用人类演示者具备强泛化能力的认知技能，特别是通过其注视行为揭示任务内在需求。然而，模仿学习通常需人类使用模拟机器人本体和视觉条件的演示设备采集数据，这引发一个问题：这些设备如何影响注视行为？我们提出一个实验框架，系统分析人类演示者在一系列机器人仿真设备下的注视行为。实验结果表明，某些设备特性会引导注视从任务目标线索（如物体）转向控制监控线索（如末端执行器）。这种注视转移直接影响典型基于注视的模仿学习模型性能，有时使其低于不依赖注视的基线模型。
原文摘要 · Abstract (English)
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.
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