大規模言語モデルによる物体レベル計画のブートストラップ
Bootstrapping Object-level Planning with Large Language Models
大規模言語モデルから物体状態の高レベル変化を表す計画スキーマを抽出し、タスク・動作計画（TAMP）のブートストラップに活用する手法を提案。シミュレーションでのピック・アンド・プレース作業で既存手法を大きく上回る性能を示した。
著者: David Paulius, Alejandro Agostini, Benedict Quartey, George Konidaris
分類: cs.RO
原文アブストラクト
We introduce a new method that extracts knowledge from a large language model (LLM) to produce object-level plans, which describe high-level changes to object state, and uses them to bootstrap task and motion planning (TAMP). Existing work uses LLMs to directly output task plans or generate goals in representations like PDDL. However, these methods fall short because they rely on the LLM to do the actual planning or output a hard-to-satisfy goal. Our approach instead extracts knowledge from an LLM in the form of plan schemas as an object-level representation called functional object-oriented networks (FOON), from which we automatically generate PDDL subgoals. Our method markedly outperforms alternative planning strategies in completing several pick-and-place tasks in simulation.
