Empowering Robot Path Planning with Large Language Models: osmAG Map Topology & Hierarchy Comprehension with LLMs
<|reference_start|>Empowering Robot Path Planning with Large Language Models: osmAG Map Topology & Hierarchy Comprehension with LLMs: 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.<|reference_end|>
@article{xie2024empowering,
    title={Empowering Robot Path Planning with Large Language Models: osmAG Map
  Topology & Hierarchy Comprehension with LLMs},
    author={Fujing Xie, S"oren Schwertfeger},
    journal={arXiv preprint arXiv:2403.08228},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08228},
    primaryClass={cs.RO}
}