Socially-aware Robot Navigation with Meta Reinforcement Learning: A Field-theory Perspective

Social robot navigation presents a significant and complex challenge within indoor environments characterized by numerous obstacles and dynamic changes. Reinforcement Learning (RL) serves as a potent approach for addressing this issue. However, the primary attributes of such environments—their diversity and complexity—pose substantial challenges to the generalization capabilities of RL-based social robot navigation systems. In this paper, we propose a meta-reinforcement learning framework for social robot navigation that empowers robots to adapt to a variety of environments. Furthermore, we introduce the concept of utilizing social norms to guide social navigation for the first time. We employ field modeling to represent interactions among the environment, pedestrians, and robots. Historical pedestrian motion data is utilized as training input for physical information neural networks, which generate pedestrian movement vector fields within the environment. We then incorporate the similarity between the robot’s predicted motion state and its actual motion state into the reward function, guiding the robot’s learning process in alignment with social norms. Simulation results demonstrate that our proposed method enhances the robot’s social navigation capabilities. The code for our approach is publicly accessible at https://github.com/SiyiLoo/MetaRlNav-FTI.