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 social cues and information transfer directionality using an information-theoretic measure, namely, transfer entropy. We demonstrate the framework in three settings involving social interactions between humans: object-handover, group-joining and person-following. Results show that transfer entropy can identify information flows between agents, when and where they occur, and their relative strength. For instance, in a person-following scenario, we find that head orientation of a predictor is particularly informative, and the different times and locations that this is used to convey information to a leader influences their behaviour. Potential applications of the framework include information flow or social cue analysis for interactive robot design, or socially-aware robot planning.

翻译：与人类协同工作的机器人需要理解并运用社交线索，以符合社会规范的方式行动。社交线索是人际间的一种沟通形式（即信息流动）。本文提出一个框架，利用信息论度量——转移熵——来检测和分析社交线索及信息传递方向。我们在包含人际互动的三种场景中验证了该框架：物体交接、群体加入与人员跟随。结果表明，转移熵能识别主体间的信息流动、其发生时间与位置及相对强度。例如，在人员跟随场景中，我们发现预测者的头部朝向携带有显著信息，而该信息在不同时间与位置传递给领导者时会影响其行为。该框架的潜在应用包括为交互机器人设计进行信息流或社交线索分析，以及实现具有社会意识的机器人规划。