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Navigation in unfamiliar environments presents a major challenge for robots:while mapping and planning techniques can be used to build up a representationof the world, quickly discovering a path to a desired goal in unfamiliarsettings with such methods often requires lengthy mapping and exploration.Humans can rapidly navigate new environments, particularly indoor environmentsthat are laid out logically, by leveraging semantics -- e.g., a kitchen oftenadjoins a living room, an exit sign indicates the way out, and so forth.Language models can provide robots with such knowledge, but directly usinglanguage models to instruct a robot how to reach some destination can also beimpractical: while language models might produce a narrative about how to reachsome goal, because they are not grounded in real-world observations, thisnarrative might be arbitrarily wrong. Therefore, in this paper we study how the``semantic guesswork'' produced by language models can be utilized as a guidingheuristic for planning algorithms. Our method, Language Frontier Guide (LFG),uses the language model to bias exploration of novel real-world environments byincorporating the semantic knowledge stored in language models as a searchheuristic for planning with either topological or metric maps. We evaluate LFGin challenging real-world environments and simulated benchmarks, outperforminguninformed exploration and other ways of using language models.

Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning