Relational Navigation Learning in Continuous Action Space among Crowds

Abstract

In this paper, a novel navigation learning method in continuous action space among crowds based on relational graph is proposed which can be directly deployed on differential-drive mobile robots without any change. More specifically, in order to increase generalization ability in crowd sizes, Graph Convolutional Network (GCN) is at first adopted to extract the relationships between robot and pedestrians. Then the relation features are further utilized as the inputs of the pedestrian state prediction network, the actor network, and the critic network. To efficiently and safely learn the navigation policy, all networks are pretrained through imitating ORCA which is a state-of-the-art algorithm in crowd navigation, and then a model-based reinforcement learning (RL) method which combines the model prediction and the clipped advantage-weighted regression is proposed to finetune the networks. Finally, simulation experiments are performed and it’s verified that the proposed learning method performs significantly better than ORCA and the other state-of-the-art RL methods.