  Safe and efficient navigation through human crowds is an essential capability
for mobile robots. Previous work on robot crowd navigation assumes that the
dynamics of all agents are known and well-defined. In addition, the performance
of previous methods deteriorates in partially observable environments and
environments with dense crowds. To tackle these problems, we propose
decentralized structural-Recurrent Neural Network (DS-RNN), a novel network
that reasons about spatial and temporal relationships for robot decision making
in crowd navigation. We train our network with model-free deep reinforcement
learning without any expert supervision. We demonstrate that our model
outperforms previous methods in challenging crowd navigation scenarios. We
successfully transfer the policy learned in the simulator to a real-world
TurtleBot 2i.
