Successful application of large language models (LLMs) to robotic planning and execution may pave the way to automate numerous real-world tasks. Promising recent research has been conducted showing that the knowledge contained in LLMs can be utilized in making goal-driven decisions that are enactable in interactive, embodied environments. Nonetheless, there is a considerable drop in correctness of programs generated by LLMs. We apply goal modeling techniques from software engineering to large language models generating robotic plans. Specifically, the LLM is prompted to generate a step refinement graph for a task. The executability and correctness of the program converted from this refinement graph is then evaluated. The approach results in programs that are more correct as judged by humans in comparison to previous work.

翻译：成功将大型语言模型应用于机器人规划与执行，可能为自动化众多现实世界任务铺平道路。近期研究已展现出利用LLM中所蕴含的知识来制定可在交互式具身环境中执行的目标驱动决策的潜力。然而，LLM生成的程序在正确性方面存在显著下降。我们将软件工程中的目标建模技术应用于生成机器人规划的大型语言模型。具体而言，我们提示LLM为任务生成步骤细化图，随后评估由该细化图转换所得程序的可执行性与正确性。与先前工作相比，该方法生成的程序经人工评估具有更高的正确性。