# LOG-Nav: Efficient Layout-Aware Object-Goal Navigation with Hierarchical Planning

arXiv: https://arxiv.org/abs/2505.06131 · HTML: https://mlsanity.com/paper/2505.06131/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2025.

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

- [Hi-Dyna Graph: Hierarchical Dynamic Scene Graph for Robotic Autonomy in Human-Centric Environments](https://mlsanity.com/paper/2506.00083/) (2025)

## Abstract (arXiv)

We introduce LOG-Nav, an efficient layout-aware object-goal navigation approach designed for complex multi-room indoor environments. By planning hierarchically leveraging a global topologigal map with layout information and local imperative approach with detailed scene representation memory, LOG-Nav achieves both efficient and effective navigation. The process is managed by an LLM-powered agent, ensuring seamless effective planning and navigation, without the need for human interaction, complex rewards, or costly training. Our experimental results on the MP3D benchmark achieves 85% object navigation success rate (SR) and 79% success rate weighted by path length (SPL) (over 40% point improvement in SR and 60% improvement in SPL compared to exsisting methods). Furthermore, we validate the robustness of our approach through virtual agent and real-world robotic deployment, showcasing its capability in practical scenarios.