Social Cue Detection and Analysis Using Transfer Entropy

TL;DR

This paper presents a framework using transfer entropy to detect and analyze nonverbal social cues in human interactions, aiding socially-aware robot design and interaction understanding.

Contribution

It introduces a novel application of transfer entropy for analyzing social cues in human-robot interaction scenarios.

Findings

Transfer entropy successfully identifies information flows between humans.

The framework applies to object-handover and person-following interactions.

Results demonstrate the method's effectiveness in real social settings.

Abstract

Robots that work close to humans need to understand and use social cues to act in a socially acceptable manner. Social cues are a form of communication (i.e., information flow) between people. In this paper, a framework is introduced to detect and analyse a class of perceptible social cues that are nonverbal and episodic, and the related information transfer using an information-theoretic measure, namely, transfer entropy. We use a group-joining setting to demonstrate the practicality of transfer entropy for analysing communications between humans. Then we demonstrate the framework in two settings involving social interactions between humans: object-handover and person-following. Our results show that transfer entropy can identify information flows between agents and when and where they occur. Potential applications of the framework include information flow or social cue analysis for interactive robot design and socially-aware robot planning.