Communication is essential for successful interaction. In human-robot interaction, implicit communication holds the potential to enhance robots’ understanding of human needs, emotions, and intentions. This paper introduces a method to foster implicit communication in HRI without explicitly modelling human intentions or relying on pre-existing knowledge. Leveraging Transfer Entropy, we modulate influence between agents in social interactions in scenarios involving either collaboration or competition. By integrating influence into agents’ rewards within a partially observable Markov decision process, we demonstrate that boosting influence enhances collaboration and interaction, while resisting influence promotes social independence and diminishes performance in certain scenarios. Our findings are validated through simulations and real-world experiments with human participants in social navigation and autonomous driving settings.

Communication, the flow of information between agents, is vital socially acceptably robot behaviours. Understanding and utilising social information is essential for achieving such behaviours. This research investigates the detection, analysis, and application of social information flows through the lens of information theory. This research comprises three stages: detecting and analysing social cues, applying transfer entropy to enhance human-robot interaction (HRI), and exploring real-world applications. We have proposed a framework for social cue detection and analysis, demonstrated across three human interaction settings: person-following, object-handover, and group-joining. This framework provides a systematic workflow that yields reliable results. In the second stage, our simulations and human studies have shown transfer entropy's effectiveness in improving social communication within a reinforcement learning context. In the third stage, we aim to validate our framework through practical user studies, enhancing its adaptability and exploring influence modulation across diverse HRI scenarios.

Graph Neural Networks (GNNs) often struggle in preserving high-frequency components of nodal signals when dealing with directed graphs. Such components are crucial for modeling flow dynamics, without which a traditional GNN tends to treat a graph with forward and reverse topologies equal. To make GNNs sensitive to those high-frequency components thereby being capable to capture detailed topological differences, this paper proposes a novel framework that combines 1) explicit difference matrices that model directional gradients and 2) implicit physical constraints that enforce messages passing within GNNs to be consistent with natural laws. Evaluations on two real-world directed graph data, namely, water flux network and urban traffic flow network, demonstrate the effectiveness of our proposal.

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.