Identifying Influential Actions in Human-Robot Interactions
Abstract
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.
Cite
@article{arxiv.2603.07885,
title = {Identifying Influential Actions in Human-Robot Interactions},
author = {Haoyang Jiang and Chenfei Xu and Yuya Okadome and Yukata Nakamura},
journal= {arXiv preprint arXiv:2603.07885},
year = {2026}
}
Comments
Presented at the 30th International Symposium on Artificial Life and Robotics (AROB 30th). Beppu, Japan, January 2025