  We consider an autonomous exploration problem in which a range-sensing mobile
robot is tasked with accurately mapping the landmarks in an a priori unknown
environment efficiently in real-time; it must choose sensing actions that both
curb localization uncertainty and achieve information gain. For this problem,
belief space planning methods that forward-simulate robot sensing and
estimation may often fail in real-time implementation, scaling poorly with
increasing size of the state, belief and action spaces. We propose a novel
approach that uses graph neural networks (GNNs) in conjunction with deep
reinforcement learning (DRL), enabling decision-making over graphs containing
exploration information to predict a robot's optimal sensing action in belief
space. The policy, which is trained in different random environments without
human intervention, offers a real-time, scalable decision-making process whose
high-performance exploratory sensing actions yield accurate maps and high rates
of information gain.
