Discovering State and Action Abstractions for Generalized Task and Motion Planning

By Aidan Curtis, Tom Silver, Josh Tenenbaum, Tomas Lozano-Perez, Leslie Pack Kaelbling. 2021

37 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Inventing Relational State and Action Abstractions for Effective and Efficient Bilevel Planning (2022, methodology): “In the course of our research, we considered many options inspired by prior work, including per-operator prediction error [12, 34], bisimulation [17, 29], and inverse planning-based objectives [35, 36], but found them all to be divergent from J real , leading to poor performance (see the baselines…”
Text2Motion: from natural language instructions to feasible plans (2023, background): “While learning symbolic representations has been proposed for TAMP [34, 3, 33, 49, 11, 10, 50], these approaches often require task-specific symbolic transition experience.”