Mobile robot

Mobile robot navigation

Crowd psychology

Reinforcement learning

Human–computer interaction

Machine learning

Recurrent neural network

Artificial intelligence

Crowds

Computer science

Crowd simulation

Computer security

Artificial neural network

Robot control

Robot

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.

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