# As You Wish: Mission Planning with Formal Verification using LLMs in Precision Agriculture

arXiv: https://arxiv.org/abs/2606.18519 · HTML: https://mlsanity.com/paper/2606.18519/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Builds on (most cited first, 15 of 15)

- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models](https://mlsanity.com/paper/2309.10062/) (2023)
- [Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification](https://mlsanity.com/paper/2303.08006/) (2023)
- [ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning](https://mlsanity.com/paper/2406.19741/) (2024)
- [From Spot 2.0 to Spot 2.10: What's New?](https://mlsanity.com/paper/2206.11366/) (2022)
- [Empowering Large Language Models on Robotic Manipulation with Affordance Prompting](https://mlsanity.com/paper/2404.11027/) (2024)
- [Can ChatGPT support software verification?](https://mlsanity.com/paper/2311.02433/) (2023)
- [Multi-robot Mission Planning in Dynamic Semantic Environments](https://mlsanity.com/paper/2209.06323/) (2022)
- [Guiding LLM Temporal Logic Generation with Explicit Separation of Data and Control](https://mlsanity.com/paper/2406.07400/) (2024)
- [Neurosymbolic Motion and Task Planning for Linear Temporal Logic Tasks](https://mlsanity.com/paper/2210.05180/) (2022)
- [Solving Stochastic Orienteering Problems with Chance Constraints Using Monte Carlo Tree Search](https://mlsanity.com/paper/2409.03170/) (2024)
- [Resilient Temporal Logic Planning in the Presence of Robot Failures](https://mlsanity.com/paper/2305.05485/) (2023)
- [Automatic Generation of Safety-compliant Linear Temporal Logic via Large Language Model: A Self-supervised Framework](https://mlsanity.com/paper/2503.15840/) (2025)
- [Leveraging LLMs for Mission Planning in Precision Agriculture](https://mlsanity.com/paper/2506.10093/) (2025)
- [One For All: LLM-based Heterogeneous Mission Planning in Precision Agriculture](https://mlsanity.com/paper/2506.10106/) (2025)

## Abstract (arXiv)

Though robotic systems are now being commercialized and deployed in various industries, many of these systems are highly specialized and often require an advanced skill set to operate and ensure they perform as instructed. To mitigate this problem, we recently introduced a mission planner leveraging LLMs to synthesize mission plans in precision agriculture based on mission descriptions provided in natural language. While the system demonstrates impressive performance, it also suffers from the inherent ambiguities of natural language. In this paper, we extend our system to address this issue by introducing multiple feedback loops in the planning architecture that leverage linear temporal logic (LTL) to ensure the mission planning system meets the specifications formulated by the user while still using natural language. To mitigate potential bias, this is achieved by using two different commercial LLMs in charge of the specification and verification subtasks. Through extensive experiments, we highlight the strengths and limitations of integrating mission verification into a fully autonomous pipeline, particularly regarding an LLM's ability to generate valuable LTL formulas, and show how our proposed implementation addresses and solves these challenges.