Safe and efficient navigation in dynamic environments shared with humans remains an open and challenging task for mobile robots. Previous works have shown the efficacy of using reinforcement learning frameworks to train policies for efficient navigation. However, their performance deteriorates when crowd configurations change, i.e. become larger or more complex. Thus, it is crucial to fully understand the complex, dynamic, and sophisticated interactions of the crowd resulting in proactive and foresighted behaviors for robot navigation. In this paper, a novel deep graph learning architecture based on attention mechanisms is proposed, which leverages the spatial-temporal graph to enhance robot navigation. We employ spatial graphs to capture the current spatial interactions, and through the integration with RNN, the temporal graphs utilize past trajectory information to infer the future intentions of each agent. The spatial-temporal graph reasoning ability allows the robot to better understand and interpret the relationships between agents over time and space, thereby making more informed decisions. Compared to previous state-of-the-art methods, our method demonstrates superior robustness in terms of safety, efficiency, and generalization in various challenging scenarios.

翻译：在与人共享的动态环境中实现安全高效的导航仍然是移动机器人面临的一个开放且具有挑战性的任务。以往研究已证明利用强化学习框架训练策略以实现高效导航的有效性。然而，当人群配置发生变化（即规模更大或更复杂）时，这些方法的性能会显著下降。因此，充分理解人群复杂、动态且精细的交互行为，从而为机器人导航赋予主动且具有前瞻性的行为模式至关重要。本文提出了一种基于注意力机制的新型深度图学习架构，通过空间-时间图增强机器人导航能力。我们利用空间图捕获当前空间交互，并通过与循环神经网络（RNN）的集成，时间图利用历史轨迹信息推断每个智能体的未来意图。空间-时间图推理能力使机器人能够更好地理解与诠释智能体之间随时间与空间变化的关联关系，从而做出更明智的决策。与现有最先进方法相比，本方法在多种具有挑战性的场景中展现出显著优越的鲁棒性，包括安全性、效率及泛化能力。