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In order for an autonomous robot to efficiently explore an unknownenvironment, it must account for uncertainty in sensor measurements, hazardassessment, localization, and motion execution. Making decisions for maximalreward in a stochastic setting requires value learning and policy constructionover a belief space, i.e., probability distribution over all possiblerobot-world states. However, belief space planning in a large spatialenvironment over long temporal horizons suffers from severe computationalchallenges. Moreover, constructed policies must safely adapt to unexpectedchanges in the belief at runtime. This work proposes a scalable value learningframework, PLGRIM (Probabilistic Local and Global Reasoning on InformationroadMaps), that bridges the gap between (i) local, risk-aware resiliency and(ii) global, reward-seeking mission objectives. Leveraging hierarchical beliefspace planners with information-rich graph structures, PLGRIM addresseslarge-scale exploration problems while providing locally near-optimal coverageplans. We validate our proposed framework with high-fidelity dynamicsimulations in diverse environments and on physical robots in Martian-analoglava tubes.

PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments