  In order to solve complex, long-horizon tasks, intelligent robots need to
carry out high-level, abstract planning and reasoning in conjunction with
motion planning. However, abstract models are typically lossy and plans or
policies computed using them can be inexecutable. These problems are
exacerbated in stochastic situations where the robot needs to reason about and
plan for multiple contingencies. We present a new approach for integrated task
and motion planning in stochastic settings. In contrast to prior work in this
direction, we show that our approach can effectively compute integrated task
and motion policies whose branching structures encode agent behaviors that
handle multiple execution-time contingencies. We prove that our algorithm is
probabilistically complete and can compute feasible solution policies in an
anytime fashion so that the probability of encountering an unresolved
contingency decreases over time. Empirical results on a set of challenging
problems show the utility and scope of our method.
