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. In this paper, 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. The predictions are incorporated into a model-free RL framework to prevent the robot from intruding into the intended paths of other agents. We demonstrate that our method enables the robot to achieve good navigation performance and non-invasiveness in challenging crowd navigation scenarios. We successfully transfer the policy learned in simulation to a real-world TurtleBot 2i. Our code and videos are available at https://sites.google.com/view/intention-aware-crowdnav/home.

翻译：我们研究了密集且具有交互性的人群中安全且具备意图感知能力的机器人导航问题。以往大多数基于强化学习（RL）的方法未能考虑所有智能体间不同类型的交互，或忽略人群的意图，导致性能下降。本文提出一种新颖的循环图神经网络，结合注意力机制，以在空间和时间维度上捕捉智能体间的异质交互。为鼓励机器人具有长远眼光的决策行为，我们通过预测动态智能体未来多个时间步的轨迹来推断其意图，并将这些预测融入无模型RL框架中，避免机器人侵入其他智能体的意图路径。实验表明，在具有挑战性的人群导航场景下，该方法能使机器人实现良好的导航性能与非侵入性。我们成功将在仿真中学习到的策略迁移至真实世界的TurtleBot 2i机器人。代码与视频见 https://sites.google.com/view/intention-aware-crowdnav/home。