Proceedings of the Annual Meeting of the Cognitive Science Society

In human social interactions, decisions are naturally influenced by both individual needs and the needs of others. However, it remains unclear whether cognitive robots exhibit similar needs-guided decision-making characteristics. In this study, we design a collaborative tracking task to evaluate this phenomenon. Specifically, we develop a needs-guided reinforcement learning framework that enables robots to autonomously learn and shape behavior by considering both their intrinsic needs and those of others. Our experiments highlight that the robots' inherent needs play a more crucial role in decision-making than the needs of others. In essence, our model establishes an interpretable foundation for applications in cognitive robotics.