TY - GEN
T1 - Discovering State and Action Abstractions for Generalized Task and Motion Planning
AU - Curtis, Aidan
AU - Silver, Tom
AU - Tenenbaum, Joshua B.
AU - Lozano-Pérez, Tomás
AU - Kaelbling, Leslie
N1 - Publisher Copyright:
© 2022, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2022/6/30
Y1 - 2022/6/30
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85136118989
UR - https://www.scopus.com/inward/citedby.url?scp=85136118989&partnerID=8YFLogxK
U2 - 10.1609/aaai.v36i5.20475
DO - 10.1609/aaai.v36i5.20475
M3 - Conference contribution
AN - SCOPUS:85136118989
T3 - Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022
SP - 5377
EP - 5384
BT - AAAI-22 Technical Tracks 5
PB - Association for the Advancement of Artificial Intelligence
T2 - 36th AAAI Conference on Artificial Intelligence, AAAI 2022
Y2 - 22 February 2022 through 1 March 2022
ER -