用信息传递度量找出影响人机互动的关键机器人动作
Identifying Influential Actions in Human-Robot Interactions
- 用转移熵分析机器人动作对人类行为的影响
- 发现距离变化是影响人类反应的关键动作
- 适合机器人交互设计与自适应系统优化者阅读
人机交互融合机器人学、认知科学与人因工程，研究协作系统。本文提出一种基于转移熵（transfer entropy）的方法，用于识别关键的机器人动作。转移熵能有效捕捉时间序列间的非线性、定向信息传递。研究以远程操控机器人化身对话场景为实验背景，聚焦于空间距离变化对人类行为的影响。结果表明，该方法可准确识别出显著影响人类反应的机器人动作，验证了其在提升机器人系统设计与自适应能力方面的潜力。
原文摘要 · Abstract (English)
Human-robot interaction combines robotics, cognitive science, and human factors to study collaborative systems. This paper introduces a method for identifying influential robot actions using transfer entropy, a statistic that measures directed information transfer between time series. TE is effective for capturing complex, nonlinear interactions. We apply this method to analyze how robot actions affect human behavior during a conversation with a remotely controlled robot avatar. By focusing on the impact of proximity, our approach demonstrates TE's capability to identify key actions influencing human responses, highlighting its potential to improve the design and adaptability of robotic systems.
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