人間の視線から学習した注意機構付きグラフ畳み込みネットワークによる群衆内ロボットナビゲーション
Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze
人間の視線データから群衆内の重要人物への注意を学習し、それをグラフ強化学習に組み込むことで、混雑環境でも安全かつ効率的に移動できるロボットナビゲーション手法を提案した。
著者: Yuying Chen, Congcong Liu, Ming Liu, Bertram E. Shi
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
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.
