Bootstrapping Object-level Planning with Large Language Models

Autor:	Paulius, David, Agostini, Alejandro, Quartey, Benedict, Konidaris, George
Rok vydání:	2024
Předmět:	Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	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) in a hierarchical manner. Existing works use LLMs to either directly output task plans or to generate goals in representations like PDDL. However, these methods fall short because they either rely on the LLM to do the actual planning or output a hard-to-satisfy goal. Our approach instead extracts knowledge from a 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 experiments demonstrate how our method's performance markedly exceeds alternative planning strategies across several tasks in simulation. Comment: 11 pages (6 pages + 1 page references + 4 pages appendix)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.12262 Zobrazit plný text záznamu View this record from Arxiv