{"ID":2866469,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.19916","arxiv_id":"2509.19916","title":"GUIDE: A Diffusion-Based Autonomous Robot Exploration Framework Using Global Graph Inference","abstract":"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.","short_abstract":"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 globa...","url_abs":"https://arxiv.org/abs/2509.19916","url_pdf":"https://arxiv.org/pdf/2509.19916v3","authors":"[\"Zijun Che\",\"Yinghong Zhang\",\"Shengyi Liang\",\"Boyu Zhou\",\"Jun Ma\",\"Jinni Zhou\"]","published":"2025-09-24T09:15:24Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Diffusion Model\",\"LoRA\"]","has_code":false}