  Integrated task and motion planning (TAMP) has proven to be a valuable
approach to generalizable long-horizon robotic manipulation and navigation
problems. However, the typical TAMP problem formulation assumes full
observability and deterministic action effects. These assumptions limit the
ability of the planner to gather information and make decisions that are
risk-aware. We propose a strategy for TAMP with Uncertainty and Risk Awareness
(TAMPURA) that is capable of efficiently solving long-horizon planning problems
with initial-state and action outcome uncertainty, including problems that
require information gathering and avoiding undesirable and irreversible
outcomes. Our planner reasons under uncertainty at both the abstract task level
and continuous controller level. Given a set of closed-loop goal-conditioned
controllers operating in the primitive action space and a description of their
preconditions and potential capabilities, we learn a high-level abstraction
that can be solved efficiently and then refined to continuous actions for
execution. We demonstrate our approach on several robotics problems where
uncertainty is a crucial factor and show that reasoning under uncertainty in
these problems outperforms previously proposed determinized planning, direct
search, and reinforcement learning strategies. Lastly, we demonstrate our
planner on two real-world robotics problems using recent advancements in
probabilistic perception.
