  In order for an autonomous robot to efficiently explore an unknown
environment, it must account for uncertainty in sensor measurements, hazard
assessment, localization, and motion execution. Making decisions for maximal
reward in a stochastic setting requires value learning and policy construction
over a belief space, i.e., probability distribution over all possible
robot-world states. However, belief space planning in a large spatial
environment over long temporal horizons suffers from severe computational
challenges. Moreover, constructed policies must safely adapt to unexpected
changes in the belief at runtime. This work proposes a scalable value learning
framework, PLGRIM (Probabilistic Local and Global Reasoning on Information
roadMaps), that bridges the gap between (i) local, risk-aware resiliency and
(ii) global, reward-seeking mission objectives. Leveraging hierarchical belief
space planners with information-rich graph structures, PLGRIM addresses
large-scale exploration problems while providing locally near-optimal coverage
plans. We validate our proposed framework with high-fidelity dynamic
simulations in diverse environments and on physical robots in Martian-analog
lava tubes.
