  Navigating fluently around pedestrians is a necessary capability for mobile
robots deployed in human environments, such as buildings and homes. While
research on social navigation has focused mainly on the scalability with the
number of pedestrians in open spaces, typical indoor environments present the
additional challenge of constrained spaces such as corridors and doorways that
limit maneuverability and influence patterns of pedestrian interaction. We
present an approach based on reinforcement learning (RL) to learn policies
capable of dynamic adaptation to the presence of moving pedestrians while
navigating between desired locations in constrained environments. The policy
network receives guidance from a motion planner that provides waypoints to
follow a globally planned trajectory, whereas RL handles the local
interactions. We explore a compositional principle for multi-layout training
and find that policies trained in a small set of geometrically simple layouts
successfully generalize to more complex unseen layouts that exhibit composition
of the structural elements available during training. Going beyond walls-world
like domains, we show transfer of the learned policy to unseen 3D
reconstructions of two real environments. These results support the
applicability of the compositional principle to navigation in real-world
buildings and indicate promising usage of multi-agent simulation within
reconstructed environments for tasks that involve interaction.
