Group-Aware Robot Navigation in Crowds Using Spatio-Temporal Graph Attention Network With Deep Reinforcement Learning

Abstract

Robots are becoming essential in human environments, requiring them to behave in a socially compliant manner. Although previous learning-based methods have shown potential in social navigation, most have treated pedestrians as individuals, failing to account for group level interactions. Additionally, these methods have modeled pairwise interactions only in the spatial domain, overlooking the temporal evolution of relations among agents. In this letter, the above limitations are addressed by proposing a novel spatio-temporal graph attention network that explicitly models group level interactions in both spatial and temporal domains. Specifically, a novel group-awareness mechanism is designed to model group-aware behaviors, and a new network is proposed to capture spatio-temporal features of relations among agents while leveraging the model-free deep reinforcement learning to optimize the group-aware navigation policy. The test results show that our approach outperforms the baselines in all metrics in both simulation and real-world experiments. Furthermore, quantitative analysis of questionnaire responses further verifies the benefits of our method in group awareness and social compliance.