A Non-parametric Approach to Exploring and Quantifying the Information Flow in Human–Robot Collaboration

Abstract

Human–Robot Interaction (HRI) has emerged as a pivotal domain in robotics, centering on the interplay and collaboration between humans and robots to achieve complex tasks. Effective communication is a cornerstone of successful HRI, facilitating the exchange of critical information essential for joint decision-making and task execution. This article explores the intricate dynamics of collaborative communication in physical HRI (pHRI), specifically focusing on non-verbal cues. Within HRI, we assert that collaboration fundamentally hinges on communication, wherein agents share information to achieve common objectives. Information theory provides a rigorous mathematical framework for quantifying the flow of information within communicating agents. It serves as a unifying framework for evaluating the dynamic interplay of various communication channels in pHRI. This study introduces a non-parametric approach based on information entropy to assess communication between agents in pHRI scenarios and detect important behaviors, such as information flow, leadership, and coupling. Through a comprehensive experimental setup involving collaborative catching tasks, we demonstrate the versatility and applicability of the proposed methodology.