  Safe and efficient crowd navigation for mobile robot is a crucial yet
challenging task. Previous work has shown the power of deep reinforcement
learning frameworks to train efficient policies. However, their performance
deteriorates when the crowd size grows. We suggest that this can be addressed
by enabling the network to identify and pay attention to the humans in the
crowd that are most critical to navigation. We propose a novel network
utilizing a graph representation to learn the policy. We first train a graph
convolutional network based on human gaze data that accurately predicts human
attention to different agents in the crowd. Then we incorporate the learned
attention into a graph-based reinforcement learning architecture. The proposed
attention mechanism enables the assignment of meaningful weightings to the
neighbors of the robot, and has the additional benefit of interpretability.
Experiments on real-world dense pedestrian datasets with various crowd sizes
demonstrate that our model outperforms state-of-art methods by 18.4% in task
accomplishment and by 16.4% in time efficiency.
