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Safe and efficient navigation through human crowds is an essential capabilityfor mobile robots. Previous work on robot crowd navigation assumes that thedynamics of all agents are known and well-defined. In addition, the performanceof previous methods deteriorates in partially observable environments andenvironments with dense crowds. To tackle these problems, we proposedecentralized structural-Recurrent Neural Network (DS-RNN), a novel networkthat reasons about spatial and temporal relationships for robot decision makingin crowd navigation. We train our network with model-free deep reinforcementlearning without any expert supervision. We demonstrate that our modeloutperforms previous methods in challenging crowd navigation scenarios. Wesuccessfully transfer the policy learned in the simulator to a real-worldTurtleBot 2i.

Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning