Follow the Signs: Using Textual Cues and LLMs to Guide Efficient Robot Navigation

TL;DR

This paper introduces a semantic navigation framework that uses large language models to interpret textual cues and guide robots efficiently to goals in unfamiliar environments, outperforming traditional methods.

Contribution

The novel integration of LLMs with exploration strategies enables robots to leverage textual and semantic cues for more efficient navigation in complex environments.

Findings

Achieves over 25% improvement in success weighted by path length.

Enables goal-directed navigation before direct observation.

Outperforms baseline methods in real-world-like environments.

Abstract

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.