Autonomous exploration in structured and complex indoor environments remains a challenging task, as existing methods often struggle to appropriately model unobserved space and plan globally efficient paths. To address these limitations, we propose GUIDE, a novel exploration framework that synergistically combines global graph inference with diffusion-based decision-making. We introduce a region-evaluation global graph representation that integrates both observed environmental data and predictions of unexplored areas, enhanced by a region-level evaluation mechanism to prioritize reliable structural inferences while discounting uncertain predictions. Building upon this enriched representation, a diffusion policy network generates stable, foresighted action sequences with significantly reduced denoising steps. Extensive simulations and real-world deployments demonstrate that GUIDE consistently outperforms state-of-the-art methods, achieving up to 18.3% faster coverage completion and a 34.9% reduction in redundant movements.
@article{arxiv.2509.19916,
title = {GUIDE: A Diffusion-Based Autonomous Robot Exploration Framework Using Global Graph Inference},
author = {Zijun Che and Yinghong Zhang and Shengyi Liang and Boyu Zhou and Jun Ma and Jinni Zhou},
journal= {arXiv preprint arXiv:2509.19916},
year = {2026}
}