Relational Graph Learning for Crowd Navigation

-- We present a relational graph learning approach for robotic crowd navigation using model-based deep reinforcement learning that plans actions by looking into the future. Our approach reasons about the relations between all agents based on their latent features and uses a Graph Convolutional Network to encode higher-order interactions in each agent's state representation, which is subsequently leveraged for state prediction and value estimation. The ability to predict human motion allows us to perform multi-step lookahead planning, taking into account the temporal evolution of human crowds. We evaluate our approach against a state-of-the-art baseline for crowd navigation and ablations of our model to demonstrate that navigation with our approach is more efficient, results in fewer collisions, and avoids failure cases involving oscillatory and freezing behaviors. I. INTRODUCTION Inferring the underlying relations between components of complex dynamic systems can inform decision making for autonomous agents. One natural system with complex dynamics is crowd navigation (i.e., navigation in the presence of multiple humans). The crowd navigation task is challenging as the agent must predict and plan relative to likely human motions so as to avoid collisions and remain at safe and socially appropriate distances from people. Some prior work predicts human trajectories using handcrafted social interaction models [1] or by modeling the temporal behavior of humans [2]. Although these methods can estimate human trajectories, they do not use the prediction to inform the navigation policy.