Safe and efficient navigation through human crowds is an essential capability for mobile robots. Previous work on robot crowd navigation assumes that the dynamics of all agents are known and well-defined. In addition, the performance of previous methods deteriorates in partially observable environments and environments with dense crowds. To tackle these problems, we propose decentralized structural-Recurrent Neural Network (DS-RNN), a novel network that reasons about spatial and temporal relationships for robot decision making in crowd navigation. We train our network with model-free deep reinforcement learning without any expert supervision. We demonstrate that our model outperforms previous methods in challenging crowd navigation scenarios. We successfully transfer the policy learned in the simulator to a real-world TurtleBot 2i.

翻译：通过人群进行安全和高效的导航是移动机器人的基本能力。以前关于机器人人群导航的工作假定所有物剂的动态是已知的和定义明确的。此外,在部分可见的环境下和人群稠密的环境中,以往方法的性能会恶化。为了解决这些问题,我们提议分散结构-实时神经网络(DS-RNNN),这是一个新颖的网络,可以解释在人群导航中机器人决策的空间和时间关系。我们培训我们的网络,在没有任何专家监督的情况下进行无型深层强化学习。我们证明我们的模型在挑战人群导航情景方面比以往的方法要好。我们成功地将模拟器所学的政策转移到现实世界的TurturtBot 2i 。