  PDDLStream solvers have recently emerged as viable solutions for Task and
Motion Planning (TAMP) problems, extending PDDL to problems with continuous
action spaces. Prior work has shown how PDDLStream problems can be reduced to a
sequence of PDDL planning problems, which can then be solved using
off-the-shelf planners. However, this approach can suffer from long runtimes.
In this paper we propose LAZY, a solver for PDDLStream problems that maintains
a single integrated search over action skeletons, which gets progressively more
geometrically informed, as samples of possible motions are lazily drawn during
motion planning. We explore how learned models of goal-directed policies and
current motion sampling data can be incorporated in LAZY to adaptively guide
the task planner. We show that this leads to significant speed-ups in the
search for a feasible solution evaluated over unseen test environments of
varying numbers of objects, goals, and initial conditions. We evaluate our TAMP
approach by comparing to existing solvers for PDDLStream problems on a range of
simulated 7DoF rearrangement/manipulation problems.

Robotics;Artificial Intelligence