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

翻译：在陌生环境中导航对机器人而言是一项重大挑战：虽然制图与规划技术可用于构建环境表征，但使用此类方法在陌生场景中快速发现通往目标路径时，通常需要耗费大量时间进行制图与探索。人类能够通过利用语义知识在新型环境中快速导航，尤其是布局合理的室内环境——例如厨房常与客厅相连，出口标志指示撤离方向等。语言模型能为机器人提供此类知识，但直接使用语言模型指导机器人抵达目的地同样不切实际：尽管语言模型可能生成关于如何到达目标的叙事描述，但由于其缺乏真实世界观测支撑，这种描述可能存在任意性错误。因此，本文研究如何将语言模型生成的"语义推测"作为规划算法的引导启发式。我们的方法——语言前沿引导算法（Language Frontier Guide，LFG）——通过将语言模型存储的语义知识作为搜索启发式，用于拓扑地图或度量地图的规划，从而利用语言模型引导对新型真实环境的探索。我们在具有挑战性的真实环境与仿真基准测试中评估了LFG，其表现优于无信息探索及其他语言模型使用方法。