Identifying Influential Actions in Human-Robot Interactions

TL;DR

This paper presents a transfer entropy-based method to identify influential robot actions in human-robot interactions, aiming to enhance robotic system design and adaptability by analyzing complex, nonlinear effects on human behavior.

Contribution

It introduces a novel application of transfer entropy to determine key robot actions affecting human responses in conversational settings.

Findings

Transfer entropy effectively captures complex interactions.

Proximity significantly influences human responses.

Method identifies key actions impacting human behavior.

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.