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

TL;DR

This paper introduces a navigation method that uses non-local information and graph neural networks to improve long-horizon goal-directed navigation in partially-mapped environments, ensuring reliability and better performance.

Contribution

It presents a novel approach combining non-local information with learning-augmented planning that guarantees reaching the goal even with imperfect predictions.

Findings

9.3% reduction in cost-to-go in simulated environments

14.9% improvement over local-information-only planners

Effective in large-scale, real-world-like environments

Abstract

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.