Planning methods struggle with computational intractability in solving task-level problems in large-scale environments. This work explores leveraging the commonsense knowledge encoded in LLMs to empower planning techniques to deal with these complex scenarios. We achieve this by efficiently using LLMs to prune irrelevant components from the planning problem's state space, substantially simplifying its complexity. We demonstrate the efficacy of this system through extensive experiments within a household simulation environment, alongside real-world validation using a 7-DoF manipulator (video https://youtu.be/6ro2UOtOQS4).
@article{arxiv.2409.04775,
title = {Scalable Task Planning via Large Language Models and Structured World Representations},
author = {Rodrigo Pérez-Dattari and Zhaoting Li and Robert Babuška and Jens Kober and Cosimo Della Santina},
journal= {arXiv preprint arXiv:2409.04775},
year = {2025}
}