  Generalized planning accelerates classical planning by finding an
algorithm-like policy that solves multiple instances of a task. A generalized
plan can be learned from a few training examples and applied to an entire
domain of problems. Generalized planning approaches perform well in discrete AI
planning problems that involve large numbers of objects and extended action
sequences to achieve the goal. In this paper, we propose an algorithm for
learning features, abstractions, and generalized plans for continuous robotic
task and motion planning (TAMP) and examine the unique difficulties that arise
when forced to consider geometric and physical constraints as a part of the
generalized plan. Additionally, we show that these simple generalized plans
learned from only a handful of examples can be used to improve the search
efficiency of TAMP solvers.
