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.
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