Diffusion as reasoning: Enhancing object navigation via diffusion model conditioned on LLM-based object-room knowledge

The Object Navigation (ObjectNav) task aims to guide an agent to locate target objects in unseen environments using partial observations. Although prior works have utilized location prediction paradigms to address long-term goal reasoning, a recurring challenge remains the efficient integration of contextual relationships between objects to enhance reasoning precision. Alternatively, map completion-based paradigms predict long-term goals by generating semantic maps of unexplored areas. However, existing methods in this category fail to fully leverage known environmental information, resulting in suboptimal map quality that requires further improvement. In this work, we propose a novel approach to enhancing the ObjectNav task, by training a diffusion model to learn the statistical distribution patterns of objects in semantic maps, and using the map of the explored regions during navigation as the condition to generate the map of the unknown regions, thereby realizing the long-term goal reasoning of the target object, i.e., diffusion as reasoning (DAR). Meanwhile, we propose the Room Guidance method, which leverages commonsense knowledge derived from large language models (LLMs) to guide the diffusion model in generating room-aware object distributions. Based on the generated map in the unknown region, the agent sets the predicted location of the target as the goal and moves towards it. Experimental results from the Habitat simulator demonstrate that our framework outperforms baseline methods by 60% in map generation accuracy (IoU). This improvement translates to higher navigation efficiency, surpassing baselines by approximately 10% on the Success-weighted by Path Length (SPL) metric. Furthermore, real-world demonstrations confirm the effectiveness of our method in navigating multi-room environments for object-goal navigation tasks.