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.

翻译：与人类密切协作的机器人需理解并运用社会线索，以符合社会规范的方式行动。社会线索是人际间的一种沟通形式（即信息流）。本文提出一个框架，用于检测和分析一类可感知的非语言性、偶发性社会线索，并利用信息论度量——转移熵来量化相关的信息传递。我们通过群体融入场景验证了转移熵在分析人类间沟通中的实用性，随后在物体交接与人跟随两种人际社交互动场景中展示了该框架的应用效果。实验结果表明，转移熵能够识别智能体间的信息流及其发生时空位置。该框架的潜在应用包括面向交互式机器人设计的信息流分析、社会线索分析以及具有社会感知能力的机器人规划。