  Planning in robotics is often split into task and motion planning. The
high-level, symbolic task planner decides what needs to be done, while the
motion planner checks feasibility and fills up geometric detail. It is known
however that such a decomposition is not effective in general as the symbolic
and geometrical components are not independent. In this work, we show that it
is possible to compile task and motion planning problems into classical AI
planning problems; i.e., planning problems over finite and discrete state
spaces with a known initial state, deterministic actions, and goal states to be
reached. The compilation is sound, meaning that classical plans are valid robot
plans, and probabilistically complete, meaning that valid robot plans are
classical plans when a sufficient number of configurations is sampled. In this
approach, motion planners and collision checkers are used for the compilation,
but not at planning time. The key elements that make the approach effective are
1) expressive classical AI planning languages for representing the compiled
problems in compact form, that unlike PDDL make use of functions and state
constraints, and 2) general width-based search algorithms capable of finding
plans over huge combinatorial spaces using weak heuristics only. Empirical
results are presented for a PR2 robot manipulating tens of objects, for which
long plans are required.
