用地图化思维让大模型导航更高效，减少重复探索。
From Reactive to Map-Based AI: Tuned Local LLMs for Semantic Zone Inference in Object-Goal Navigation
- 用微调的LLM分析观察到的物体，推断功能区域和目标概率。
- 在拓扑图上规划路径，通过旅行商问题优化探索路线，成功率更高。
- 适合做智能导航、机器人任务规划的研究者参考。
物体目标导航（ObjectNav）要求智能体在未知环境中找到并抵达目标物体类别。尽管基于大语言模型（LLM）的智能体具备零样本推理能力，但其常依赖“反应式”策略，缺乏显式空间记忆，导致重复探索和短视行为。为此，我们提出从反应式AI转向“地图化AI”，将基于LLM的语义推理与混合拓扑-网格地图系统结合。框架采用低秩适应（LoRA）微调Llama-2模型，从口头描述的物体观测中推断语义区域类别及目标存在概率。其中，“区域”定义为由观测物体构成的功能区，提供关键的语义共现线索以定位目标。该语义信息被整合进拓扑图，使智能体能优先探索高概率区域，并通过旅行商问题（TSP）优化实现系统性探索。在AI2-THOR模拟器中的评估表明，该方法显著优于传统前沿探索和反应式LLM基线，在成功率（SR）和路径长度加权成功率（SPL）上均表现更优。
原文摘要 · Abstract (English)
Object-Goal Navigation (ObjectNav) requires an agent to find and navigate to a target object category in unknown environments. While recent Large Language Model (LLM)-based agents exhibit zero-shot reasoning, they often rely on a "reactive" paradigm that lacks explicit spatial memory, leading to redundant exploration and myopic behaviors. To address these limitations, we propose a transition from reactive AI to "Map-Based AI" by integrating LLM-based semantic inference with a hybrid topological-grid mapping system. Our framework employs a fine-tuned Llama-2 model via Low-Rank Adaptation (LoRA) to infer semantic zone categories and target existence probabilities from verbalized object observations. In this study, a "zone" is defined as a functional area described by the set of observed objects, providing crucial semantic co-occurrence cues for finding the target. This semantic information is integrated into a topological graph, enabling the agent to prioritize high-probability areas and perform systematic exploration via Traveling Salesman Problem (TSP) optimization. Evaluations in the AI2-THOR simulator demonstrate that our approach significantly outperforms traditional frontier exploration and reactive LLM baselines, achieving a superior Success Rate (SR) and Success weighted by Path Length (SPL).
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