  Optimal plans in Constrained Partially Observable Markov Decision Processes
(CPOMDPs) maximize reward objectives while satisfying hard cost constraints,
generalizing safe planning under state and transition uncertainty.
Unfortunately, online CPOMDP planning is extremely difficult in large or
continuous problem domains. In many large robotic domains, hierarchical
decomposition can simplify planning by using tools for low-level control given
high-level action primitives (options). We introduce Constrained Options Belief
Tree Search (COBeTS) to leverage this hierarchy and scale online search-based
CPOMDP planning to large robotic problems. We show that if primitive option
controllers are defined to satisfy assigned constraint budgets, then COBeTS
will satisfy constraints anytime. Otherwise, COBeTS will guide the search
towards a safe sequence of option primitives, and hierarchical monitoring can
be used to achieve runtime safety. We demonstrate COBeTS in several
safety-critical, constrained partially observable robotic domains, showing that
it can plan successfully in continuous CPOMDPs while non-hierarchical baselines
cannot.
