Grounding navigational commands to linear temporal logic (LTL) leverages its unambiguous semantics for reasoning about long-horizon tasks and verifying the satisfaction of temporal constraints. Existing approaches require training data from the specific environment and landmarks that will be used in natural language to understand commands in those environments. We propose Lang2LTL, a modular system and a software package that leverages large language models (LLMs) to ground temporal navigational commands to LTL specifications in environments without prior language data. We comprehensively evaluate Lang2LTL for five well-defined generalization behaviors. Lang2LTL demonstrates the state-of-the-art ability of a single model to ground navigational commands to diverse temporal specifications in 21 city-scaled environments. Finally, we demonstrate a physical robot using Lang2LTL can follow 52 semantically diverse navigational commands in two indoor environments.

翻译：将导航指令地面化为线性时序逻辑（LTL）利用其明确的语义，从而能够推理长周期任务并验证时间约束的满足性。现有方法需要从特定环境和将用于自然语言理解指令的标志物中获取训练数据。我们提出Lang2LTL，一个模块化系统及软件包，它利用大型语言模型（LLM）在无先验语言数据的环境中将时间导航指令地面化为LTL规范。我们针对五种定义明确的泛化行为对Lang2LTL进行了全面评估。Lang2LTL展示了单个模型在21个城市规模环境中将导航指令地面化为多样化时间规范的最先进能力。最后，我们通过物理机器人演示，证明使用Lang2LTL的机器人可以在两个室内环境中执行52条语义多样的导航指令。