Generating low-level robot task plans from high-level natural language instructions remains a challenging problem. Although large language models have shown promising results in generating plans, the accuracy of the output remains unverified. Furthermore, the lack of domain-specific language data poses a limitation on the applicability of these models. In this paper, we propose CLAIRIFY, a novel approach that combines automatic iterative prompting with program verification to ensure programs written in data-scarce domain-specific language are syntactically valid and incorporate environment constraints. Our approach provides effective guidance to the language model on generating structured-like task plans by incorporating any errors as feedback, while the verifier ensures the syntactic accuracy of the generated plans. We demonstrate the effectiveness of CLAIRIFY in planning chemistry experiments by achieving state-of-the-art results. We also show that the generated plans can be executed on a real robot by integrating them with a task and motion planner.

翻译：从高层自然语言指令生成低层机器人任务计划仍是一个具有挑战性的问题。尽管大语言模型在生成计划方面展现出有前景的结果，但输出结果的准确性尚未得到验证。此外，特定领域语言的训练数据匮乏限制了这些模型的适用性。本文提出CLAIRIFY方法——一种结合自动迭代提示与程序验证的创新方案，确保用数据稀缺的领域特定语言编写的程序在语法上有效，并融入环境约束。该方法通过将任何错误作为反馈纳入，为语言模型生成结构化任务计划提供有效引导，同时通过验证器确保生成计划的语法准确性。我们通过规划化学实验展示了CLAIRIFY的有效性，取得了当前最优结果。此外，结合任务与运动规划器，我们证明了所生成的计划可在真实机器人上执行。