Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze
This addresses safe and efficient navigation for mobile robots in crowded environments, representing an incremental advance by integrating human attention into existing reinforcement learning frameworks.
The paper tackled the problem of robot navigation in dense crowds by using a graph convolutional network with attention learned from human gaze to identify critical humans, resulting in an 18.4% improvement in task accomplishment and 16.4% in time efficiency over state-of-the-art methods.
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.