  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.
