# Empowering Robot Path Planning with Large Language Models: osmAG Map Topology & Hierarchy Comprehension with LLMs

arXiv: https://arxiv.org/abs/2403.08228 · HTML: https://mlsanity.com/paper/2403.08228/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [HaltNav: Reactive Visual Halting over Lightweight Topological Priors for Robust Vision-Language Navigation](https://mlsanity.com/paper/2603.12696/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated great potential in robotic applications by providing essential general knowledge. Mobile robots rely on map comprehension for tasks like localization and navigation. In this paper, we explore enabling LLMs to comprehend the topology and hierarchy of Area Graph, a text-based hierarchical, topometric semantic map representation utilizing polygons to demark areas such as rooms or buildings. Our experiments demonstrate that with the right map representation, LLMs can effectively comprehend Area Graph's topology and hierarchy. After straightforward fine-tuning, the LLaMA2 models exceeded ChatGPT-3.5 in mastering these aspects. Our dataset, dataset generation code, fine-tuned LoRA adapters can be accessed at https://github.com/xiefujing/LLM-osmAG-Comprehension.