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cs.RO2021
Discovering State and Action Abstractions for Generalized Task and Motion Planning
Aidan Curtis, Tom Silver, Joshua B. Tenenbaum +2
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 train…
cs.RO2021
Learning Symbolic Operators for Task and Motion Planning
Tom Silver, Rohan Chitnis, Joshua Tenenbaum +2
Robotic planning problems in hybrid state and action spaces can be solved by integrated task and motion planners (TAMP) that handle the complex interaction between motion-level dec…