LTLCodeGen: ロボットタスク計画のための構文的に正しい時相論理のコード生成
LTLCodeGen: Code Generation of Syntactically Correct Temporal Logic for Robot Task Planning
自然言語の指示をコード生成によって構文的に正しい線形時相論理(LTL)式に変換し、セマンティック占有マップと組み合わせて衝突のないロボット経路を計画する手法を提案。
著者: Behrad Rabiei, Mahesh Kumar A. R., Zhirui Dai, Surya L. S. R. Pilla, Qiyue Dong, Nikolay Atanasov
分類: cs.RO
原文アブストラクト
This paper focuses on planning robot navigation tasks from natural language specifications. We develop a modular approach, where a large language model (LLM) translates the natural language instructions into a linear temporal logic (LTL) formula with propositions defined by object classes in a semantic occupancy map. The LTL formula and the semantic occupancy map are provided to a motion planning algorithm to generate a collision-free robot path that satisfies the natural language instructions. Our main contribution is LTLCodeGen, a method to translate natural language to syntactically correct LTL using code generation. We demonstrate the complete task planning method in real-world experiments involving human speech to provide navigation instructions to a mobile robot. We also thoroughly evaluate our approach in simulated and real-world experiments in comparison to end-to-end LLM task planning and state-of-the-art LLM-to-LTL translation methods.
