# Bootstrapping Object-level Planning with Large Language Models

arXiv: https://arxiv.org/abs/2409.12262 · HTML: https://mlsanity.com/paper/2409.12262/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2024.

## Built on by (most cited first, 3 of 3)

- [Jointly Learning Predicates and Actions Enables Zero-Shot Skill Composition](https://mlsanity.com/paper/2605.20648/) (2026)
- [QueryGraph: Reliable Multi-Tool Query Execution Planning via LLM-Based Graph Generation](https://mlsanity.com/paper/2606.08300/) (2026)
- [Leveraging Inter-object Affordances for Efficient Planning in Contact-rich Tasks](https://mlsanity.com/paper/2608.25641/) (2026)

## Abstract (arXiv)

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.