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.
翻译:我们研究了在密集和互动人群中安全和有意向意识的机器人导航问题,以前多数基于强化学习的方法没有考虑到所有代理人之间的不同类型互动或忽视人们的意图,从而导致性能退化。在本文件中,我们提议建立一个新的重复式图形神经网络,其关注机制通过时空捕捉各种代理人之间的不同互动。为了鼓励远视机器人行为,我们通过预测其未来轨迹的几步来推断动态代理人的意图。这些预测已纳入一个无模型的RL框架,以防止机器人侵入其他代理人的预定路径。我们证明,我们的方法使机器人能够取得良好的导航性能和在挑战人群导航情景时无入侵性。我们成功地将模拟中学到的政策转移到现实世界的TurturtBot 2i。