Communication is essential for successful interaction. In human-robot interaction, implicit communication enhances robots' understanding of human needs, emotions, and intentions. This paper introduces a method to foster implicit communication in HRI without explicitly modeling 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 or competition performance, while resisting influence diminishes performance. Our findings are validated through simulations and real-world experiments with human participants.

翻译：通信是成功交互的关键。在人机交互中，隐式通信能提升机器人对人类需求、情感及意图的理解能力。本文提出一种方法，旨在促进人机交互中的隐式通信，而无需显式建模人类意图或依赖先验知识。我们利用传递熵，在协作或竞争场景中调制社会交互中智能体间的影响。通过将影响整合至部分可观测马尔可夫决策过程中智能体的奖励函数，我们证明增强影响可提升协作或竞争表现，而抵抗影响则会降低表现。我们的研究结果通过仿真实验和真人参与的实际实验得到了验证。