連続ロボットTAMPのための状態・行動抽象化の発見
Discovering State and Action Abstractions for Generalized Task and Motion Planning
少数の訓練例から連続ロボットのタスク・動作計画(TAMP)向けに特徴・抽象化・汎用計画を学習し、TAMPソルバの探索効率を改善する手法を提案した論文。
著者: Aidan Curtis, Tom Silver, Joshua B. Tenenbaum, Tomas Lozano-Perez, Leslie Pack Kaelbling
分類: cs.RO
原文アブストラクト
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.
