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Generalized planning accelerates classical planning by finding analgorithm-like policy that solves multiple instances of a task. A generalizedplan can be learned from a few training examples and applied to an entiredomain of problems. Generalized planning approaches perform well in discrete AIplanning problems that involve large numbers of objects and extended actionsequences to achieve the goal. In this paper, we propose an algorithm forlearning features, abstractions, and generalized plans for continuous robotictask and motion planning (TAMP) and examine the unique difficulties that arisewhen forced to consider geometric and physical constraints as a part of thegeneralized plan. Additionally, we show that these simple generalized planslearned from only a handful of examples can be used to improve the searchefficiency of TAMP solvers.

Discovering State and Action Abstractions for Generalized Task and Motion Planning