エラーは有用なプロンプト：検証支援型反復プロンプティングによる指示誘導タスクプログラミング
Errors are Useful Prompts: Instruction Guided Task Programming with Verifier-Assisted Iterative Prompting
大規模言語モデルとプログラム検証を組み合わせ、エラーをフィードバックとして反復的にプロンプトすることで、データが少ない分野固有言語でも構文的に正しく環境制約を満たすロボットタスク計画を生成する手法CLAIRIFYを提案し、化学実験計画で最高性能を達成した。
著者: Marta Skreta, Naruki Yoshikawa, Sebastian Arellano-Rubach, Zhi Ji, Lasse Bjørn Kristensen, Kourosh Darvish, Alán Aspuru-Guzik, Florian Shkurti, Animesh Garg
分類: cs.RO
原文アブストラクト
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.
