Autonomous navigation in unfamiliar environments often relies on geometric mapping and planning strategies that overlook rich semantic cues such as signs, room numbers, and textual labels. We propose a novel semantic navigation framework that leverages large language models (LLMs) to infer patterns from partial observations and predict regions where the goal is most likely located. Our method combines local perceptual inputs with frontier-based exploration and periodic LLM queries, which extract symbolic patterns (e.g., room numbering schemes and building layout structures) and update a confidence grid used to guide exploration. This enables robots to move efficiently toward goal locations labeled with textual identifiers (e.g., "room 8") even before direct observation. We demonstrate that this approach enables more efficient navigation in sparse, partially observable grid environments by exploiting symbolic patterns. Experiments across environments modeled after real floor plans show that our approach consistently achieves near-optimal paths and outperforms baselines by over 25% in Success weighted by Path Length.

翻译：在陌生环境中的自主导航通常依赖于几何建图与规划策略，这些方法往往忽略了丰富的语义线索，如标识牌、房间号及文本标签。我们提出了一种新颖的语义导航框架，该框架利用大型语言模型（LLMs）从局部观测中推断模式，并预测目标最可能出现的区域。我们的方法将局部感知输入与基于前沿的探索及周期性LLM查询相结合，通过查询提取符号模式（例如房间编号规则与建筑布局结构），并更新用于引导探索的置信度网格。这使得机器人即使在直接观测到目标之前，也能高效地向带有文本标识（如“8号房间”）的目标位置移动。我们证明，该方法通过利用符号模式，能够在稀疏、部分可观测的网格环境中实现更高效的导航。在基于真实平面图建模的多种环境中进行的实验表明，我们的方法始终能实现接近最优的路径，并以路径长度加权的成功率指标超越基线方法超过25%。