  Rather than augmenting rewards with penalties for undesired behavior,
Constrained Partially Observable Markov Decision Processes (CPOMDPs) plan
safely by imposing inviolable hard constraint value budgets. Previous work
performing online planning for CPOMDPs has only been applied to discrete action
and observation spaces. In this work, we propose algorithms for online CPOMDP
planning for continuous state, action, and observation spaces by combining dual
ascent with progressive widening. We empirically compare the effectiveness of
our proposed algorithms on continuous CPOMDPs that model both toy and
real-world safety-critical problems. Additionally, we compare against the use
of online solvers for continuous unconstrained POMDPs that scalarize cost
constraints into rewards, and investigate the effect of optimistic cost
propagation.
