  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.
