# LTLCodeGen: Code Generation of Syntactically Correct Temporal Logic for Robot Task Planning

arXiv: https://arxiv.org/abs/2503.07902 · HTML: https://mlsanity.com/paper/2503.07902/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [ViTL: Temporal Logic-Guided Zero-Shot Natural Language Navigation via Vision-Language Models](https://mlsanity.com/paper/2606.30696/) (2026)

## Abstract (arXiv)

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.