We improve reliable, long-horizon, goal-directed navigation in partially-mapped environments by using non-locally available information to predict the goodness of temporally-extended actions that enter unseen space. Making predictions about where to navigate in general requires non-local information: any observations the robot has seen so far may provide information about the goodness of a particular direction of travel. Building on recent work in learning-augmented model-based planning under uncertainty, we present an approach that can both rely on non-local information to make predictions (via a graph neural network) and is reliable by design: it will always reach its goal, even when learning does not provide accurate predictions. We conduct experiments in three simulated environments in which non-local information is needed to perform well. In our large scale university building environment, generated from real-world floorplans to the scale, we demonstrate a 9.3\% reduction in cost-to-go compared to a non-learned baseline and a 14.9\% reduction compared to a learning-informed planner that can only use local information to inform its predictions.

翻译：我们通过利用非局部可用信息来预测进入未知空间的时延动作的优劣性，从而改进了部分映射环境中可靠、长时域、目标导向的导航能力。在导航中做出关于行进方向的预测通常需要非局部信息：机器人迄今观察到的任何观测都可能为特定行进方向的优劣性提供信息。基于近期在不确定性下学习增强型模型规划方面的研究成果，我们提出了一种方法，该方法既能依赖非局部信息进行预测（通过图神经网络），又具有设计上的可靠性：即使学习未能提供准确预测，它也能始终到达目标。我们在三个需要非局部信息才能取得良好性能的模拟环境中进行了实验。在我们基于真实世界楼层平面图按比例生成的大型大学建筑环境中，相比非学习基线方法，我们的方法将成本降低了9.3%；相比仅能利用局部信息进行预测的学习驱动型规划器，成本降低了14.9%。