  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 unexecutable. 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 encoding agent behaviors handling 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 methods.
