# GUIDE: A Diffusion-Based Autonomous Robot Exploration Framework Using Global Graph Inference

arXiv: https://arxiv.org/abs/2509.19916 · HTML: https://mlsanity.com/paper/2509.19916/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2025.

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

- [CAVEAT: Recurrent Multimodal Diffusion Planning for Mapless Aerial Exploration](https://mlsanity.com/paper/2609.06623/) (2026)

## Abstract (arXiv)

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.