Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph

Understand

We study the problem of safe and intention-aware robot navigation in dense and interactive crowds.

• Most previous reinforcement learning (RL) based methods fail to consider different types of interactions among all agents or ignore the intentions of people, which results in performance degradation.
• To learn a safe and efficient robot policy, we propose a novel recurrent graph neural network with attention mechanisms to capture heterogeneous interactions among agents through space and time.
• To encourage longsighted robot behaviors, we infer the intentions of dynamic agents by predicting their future trajectories for several timesteps.