研究人形机器人指物动作的可读性，发现人类能通过部分动作和眼神预测意图。
Examining the legibility of humanoid robot arm movements in a pointing task
- 通过截断机器人手臂轨迹，测试人类对意图的预测能力。
- 眼神与指物一致时预测准确率最高，达87%以上。
- 适合关注人机交互、机器人行为设计的研究者阅读。
人机交互需要机器人动作具备可读性，使人类能够理解、预测并感到安全。本研究以指物任务为场景，探讨人形机器人手臂动作的可读性，旨在理解人类如何通过不完整动作和身体线索预测机器人意图。实验采用NICO人形机器人，在触摸屏上观察其手臂向目标移动的过程。实验条件包括：视线、指物、指物与视线一致或不一致等组合。手臂轨迹在完成60%或80%时被截断，参与者需预测最终目标。结果支持多模态优势与眼动优先假设：当眼神与指物方向一致时，人类预测准确率最高，达到87%以上。
原文摘要 · Abstract (English)
Human--robot interaction requires robots whose actions are legible, allowing humans to interpret, predict, and feel safe around them. This study investigates the legibility of humanoid robot arm movements in a pointing task, aiming to understand how humans predict robot intentions from truncated movements and bodily cues. We designed an experiment using the NICO humanoid robot, where participants observed its arm movements towards targets on a touchscreen. Robot cues varied across conditions: gaze, pointing, and pointing with congruent or incongruent gaze. Arm trajectories were stopped at 60\% or 80\% of their full length, and participants predicted the final target. We tested the multimodal superiority and ocular primacy hypotheses, both of which were supported by the experiment.
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