  In order for a robot to explore an unknown environment autonomously, it must
account for uncertainty in sensor measurements, hazard assessment,
localization, and motion execution. Making decisions for maximal reward in a
stochastic setting requires learning values and constructing policies over a
belief space, i.e., probability distribution of the robot-world state. Value
learning over belief spaces suffer from computational challenges in
high-dimensional spaces, such as large spatial environments and long temporal
horizons for exploration. At the same time, it should be adaptive and resilient
to disturbances at run time in order to ensure the robot's safety, as required
in many real-world applications. 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. By leveraging hierarchical
belief space planners with information-rich graph structures, PLGRIM can
address large-scale exploration problems while providing locally near-optimal
coverage plans. PLGRIM is a step toward enabling belief space planners on
physical robots operating in unknown and complex environments. We validate our
proposed framework with a high-fidelity dynamic simulation in diverse
environments and with physical hardware, Boston Dynamics' Spot robot, in a lava
tube.
