Ergodicity reveals assistance and learning from physical human-robot interaction
Ergodicity reveals assistance and learning from physical human-robot interaction
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DOI：
10.1126/scirobotics.aav6079
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发表时间：
2019-04-24
期刊：
影响因子：
25
通讯作者：
Murphey, Todd D.
中科院分区：
文献类型：
--
作者：
Fitzsimons, Kathleen;Acosta, Ana Maria;Murphey, Todd D.
本文将信息论原理应用于物理人机交互的研究。信息论方法借鉴人类感知和神经编码的研究，提供了一种视角，能够定量地将身体解释为信息通道，将身体运动解释为携带信息的信号。我们证明，遍历性（可以解释为轨迹对任务信息进行编码的程度）可以正确预测由于人现有缺陷的减少或算法辅助的增加而导致的变化。该措施还捕捉了机器人辅助训练带来的变化。其他常见的评估措施未能捕捉到这些影响中的至少一种。这种基于信息的运动解释可以广泛应用于人机交互的评估和设计、通过演示范式进行学习或人体运动分析。
This paper applies information theoretic principles to the investigation of physical human-robot interaction. Drawing from the study of human perception and neural encoding, information theoretic approaches offer a perspective that enables quantitatively interpreting the body as an information channel and bodily motion as an information-carrying signal. We show that ergodicity, which can be interpreted as the degree to which a trajectory encodes information about a task, correctly predicts changes due to reduction of a person's existing deficit or the addition of algorithmic assistance. The measure also captures changes from training with robotic assistance. Other common measures for assessment failed to capture at least one of these effects. This information-based interpretation of motion can be applied broadly, in the evaluation and design of human-machine interactions, in learning by demonstration paradigms, or in human motion analysis.