タスク・動作計画のための記号的オペレータの学習
Learning Symbolic Operators for Task and Motion Planning
TAMPのためのオペレータ学習を定式化し、ボトムアップ関係学習で記号的オペレータを獲得して計画に用いる手法を提案した論文。
著者: Tom Silver, Rohan Chitnis, Joshua Tenenbaum, Leslie Pack Kaelbling, Tomas Lozano-Perez
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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 decisions and task-level plan feasibility. TAMP approaches rely on domain-specific symbolic operators to guide the task-level search, making planning efficient. In this work, we formalize and study the problem of operator learning for TAMP. Central to this study is the view that operators define a lossy abstraction of the transition model of a domain. We then propose a bottom-up relational learning method for operator learning and show how the learned operators can be used for planning in a TAMP system. Experimentally, we provide results in three domains, including long-horizon robotic planning tasks. We find our approach to substantially outperform several baselines, including three graph neural network-based model-free approaches from the recent literature. Video: https://youtu.be/iVfpX9BpBRo Code: https://git.io/JCT0g
