  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.
