# Towards An LLM-Driven Unified Conversion Framework for BT and FSM in Autonomous Intelligent Systems

arXiv: https://arxiv.org/abs/2609.29228 · HTML: https://mlsanity.com/paper/2609.29228/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 19 of 19)

- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [A Survey of Behavior Trees in Robotics and AI](https://mlsanity.com/paper/2005.05842/) (2020)
- [LLM Inference Serving: Survey of Recent Advances and Opportunities](https://mlsanity.com/paper/2407.12391/) (2024)
- [LLM-BRAIn: AI-driven Fast Generation of Robot Behaviour Tree based on Large Language Model](https://mlsanity.com/paper/2305.19352/) (2023)
- [Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics](https://mlsanity.com/paper/2011.09756/) (2020)
- [LLM-BT: Performing Robotic Adaptive Tasks based on Large Language Models and Behavior Trees](https://mlsanity.com/paper/2404.05134/) (2024)
- [Behavior Trees in Robot Control Systems](https://mlsanity.com/paper/2203.13083/) (2022)
- [Behavior Trees and State Machines in Robotics Applications](https://mlsanity.com/paper/2208.04211/) (2022)
- [mc-mujoco: Simulating Articulated Robots with FSM Controllers in MuJoCo](https://mlsanity.com/paper/2209.00274/) (2022)
- [On the programming effort required to generate Behavior Trees and Finite State Machines for robotic applications](https://mlsanity.com/paper/2209.07392/) (2022)
- [Comparison between Behavior Trees and Finite State Machines](https://mlsanity.com/paper/2405.16137/) (2024)
- [Can Large Language Models Help Developers with Robotic Finite State Machine Modification?](https://mlsanity.com/paper/2412.05625/) (2024)
- [Behavior Trees with Dataflow: Coordinating Reactive Tasks in Lingua Franca](https://mlsanity.com/paper/2401.09185/) (2024)
- [Flexible Behavior Trees: In search of the mythical HFSMBTH for Collaborative Autonomy in Robotics](https://mlsanity.com/paper/2203.05389/) (2022)
- [Fully Abstract Normal Form Bisimulation for Call-by-Value PCF](https://mlsanity.com/paper/2310.01069/) (2023)
- [Behavior Trees in Industrial Applications: A Case Study in Underground Explosive Charging](https://mlsanity.com/paper/2403.19602/) (2024)
- [Enhancing Finite State Machine Design Automation with Large Language Models and Prompt Engineering Techniques](https://mlsanity.com/paper/2506.00001/) (2025)

## Abstract (arXiv)

Finite state machine (FSM) and behavior trees (BT) are widely adopted behavioral modeling paradigms for autonomous intelligent systems. While functionally equivalent and inter-convertible in principle, existing transformation methods between FSM and BT face major challenges in preserving behavioral completeness and avoiding model complexity explosion. To overcome these issues, we propose an LLM-driven unified conversion framework that enables automatic, efficient, and semantically consistent transformation between FSM and BT. Specifically, a novel loop execution BT structure is designed for LLM to accurately capture the loop structure in FSM, thereby preserving behavioral completeness. To mitigate the state explosion problem in BT-to-FSM conversion, a depth compression strategy is introduced with LLM prompt to eliminate redundant control nodes, complemented by differentiated hierarchical conversion rules that collectively reduce the number of required sub-FSM. Simulation experiments in multiple autonomous decision-making scenarios demonstrate that the proposed framework enables an accurate and automated bidirectional conversion between FSM and BT. Furthermore, it significantly enhances the scalability and maintainability of generated models compared to traditional approaches, providing a practical solution for behavior model conversion in consumer-grade autonomous intelligent systems such as service robots, game agents, and smart home devices