Identifying Influential Actions in Human-Robot Interactions
This work addresses the problem of understanding and improving human-robot interaction for designers of robotic systems, offering an incremental step in analysis methods.
This paper introduces a method using transfer entropy to identify influential robot actions. It was applied to analyze how robot proximity affects human behavior during conversations with a robot avatar, demonstrating its ability to pinpoint key actions.
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.