  Partially observable Markov decision processes (POMDPs) have been widely used
in many robotic applications for sequential decision-making under uncertainty.
POMDP online planning algorithms such as Partially Observable Monte-Carlo
Planning (POMCP) can solve very large POMDPs with the goal of maximizing the
expected return. But the resulting policies cannot provide safety guarantees
which are imperative for real-world safety-critical tasks (e.g., autonomous
driving). In this work, we consider safety requirements represented as
almost-sure reach-avoid specifications (i.e., the probability to reach a set of
goal states is one and the probability to reach a set of unsafe states is
zero). We compute shields that restrict unsafe actions which would violate the
almost-sure reach-avoid specifications. We then integrate these shields into
the POMCP algorithm for safe POMDP online planning. We propose four distinct
shielding methods, differing in how the shields are computed and integrated,
including factored variants designed to improve scalability. Experimental
results on a set of benchmark domains demonstrate that the proposed shielding
methods successfully guarantee safety (unlike the baseline POMCP without
shielding) on large POMDPs, with negligible impact on the runtime for online
planning.
