用分层规划实现高效精准的室内导航，无需复杂训练。
LOG-Nav: Efficient Layout-Aware Object-Goal Navigation with Hierarchical Planning
- 分层规划：全局拓扑图+局部细节记忆协同决策
- 在MP3D上达85%成功率，路径加权成功率提升60%
- 适用于真实机器人部署，适合做智能导航系统
我们提出LOG-Nav，一种针对复杂多房间室内环境的高效布局感知物体目标导航方法。通过结合带有布局信息的全局拓扑地图与包含详细场景表征的记忆化局部指令式规划，实现了高效且有效的导航。整个过程由基于大语言模型的智能体管理，无需人工干预、复杂奖励设计或昂贵训练。在MP3D基准上的实验结果表明，该方法达到85%的物体导航成功率（SR）和79%的路径长度加权成功率（SPL），较现有方法分别提升超过40个百分点和60个百分点。此外，通过虚拟代理与真实机器人部署验证了方法的鲁棒性，展示了其在实际场景中的应用能力。
原文摘要 · Abstract (English)
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.
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