Improving Reliable Navigation Under Uncertainty via Predictions Informed by Non-Local Information

Abstract

We improve reliable, long-horizon, goal-directed navigation in partially-mapped environments by using nonlocally available information to predict the goodness of temporally-extended actions that enter unseen space. Making predictions about where to navigate in general requires nonlocal 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 nonlocal 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 nonlocal 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.