# LLM-BRAIn: AI-driven Fast Generation of Robot Behaviour Tree based on Large Language Model

arXiv: https://arxiv.org/abs/2305.19352 · HTML: https://mlsanity.com/paper/2305.19352/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2023.

## Built on by (most cited first, 24 of 24)

- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test](https://mlsanity.com/paper/2503.02911/) (2025)
- [Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human Instructions](https://mlsanity.com/paper/2405.07474/) (2024)
- [Nadine: An LLM-driven Intelligent Social Robot with Affective Capabilities and Human-like Memory](https://mlsanity.com/paper/2405.20189/) (2024)
- [Industry 6.0: New Generation of Industry driven by Generative AI and Swarm of Heterogeneous Robots](https://mlsanity.com/paper/2409.10106/) (2024)
- [LLM-SAP: Large Language Models Situational Awareness Based Planning](https://mlsanity.com/paper/2312.16127/) (2023)
- [A Study on Training and Developing Large Language Models for Behavior Tree Generation](https://mlsanity.com/paper/2401.08089/) (2024)
- [HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning](https://mlsanity.com/paper/2406.00965/) (2024)
- [Designing Robust Cyber-Defense Agents with Evolving Behavior Trees](https://mlsanity.com/paper/2410.16383/) (2024)
- [LLMSat: A Large Language Model-Based Goal-Oriented Agent for Autonomous Space Exploration](https://mlsanity.com/paper/2405.01392/) (2024)
- [Integrating Large Language Models for UAV Control in Simulated Environments: A Modular Interaction Approach](https://mlsanity.com/paper/2410.17602/) (2024)
- [The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare](https://mlsanity.com/paper/2411.03287/) (2024)
- [Multimodal Behavior Tree Generation: A Small Vision-Language Model for Robot Task Planning](https://mlsanity.com/paper/2603.06084/) (2026)
- [AUTONODE: A Neuro-Graphic Self-Learnable Engine for Cognitive GUI Automation](https://mlsanity.com/paper/2403.10171/) (2024)
- [Exploring GPT-4 for Robotic Agent Strategy with Real-Time State Feedback and a Reactive Behaviour Framework](https://mlsanity.com/paper/2503.23601/) (2025)
- [Behavior Trees Enable Structured Programming of Language Model Agents](https://mlsanity.com/paper/2404.07439/) (2024)
- [Interpretable Robot Control via Structured Behavior Trees and Large Language Models](https://mlsanity.com/paper/2508.09621/) (2025)
- [Robot guide with multi-agent control and automatic scenario generation with LLM](https://mlsanity.com/paper/2509.10317/) (2025)
- [VersualRL: Closed-Loop Verbal Reinforcement Learning with Visual Execution Feedback for Task-Level Robot Planning](https://mlsanity.com/paper/2603.22169/) (2026)
- [Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision](https://mlsanity.com/paper/2604.02812/) (2026)
- [Contract-Grounded Behavior Tree Synthesis via Coding Agents](https://mlsanity.com/paper/2607.12220/) (2026)
- [Mamba-based Selective State Space Modeling Improves the Accuracy-Complexity Tradeoff of SmolVLA Vision-Language-Action Experts](https://mlsanity.com/paper/2608.21407/) (2026)
- [ORDER: A Fictitious-World Benchmark for Domain-Adaptive Embodied AI](https://mlsanity.com/paper/2609.22285/) (2026)
- [Towards An LLM-Driven Unified Conversion Framework for BT and FSM in Autonomous Intelligent Systems](https://mlsanity.com/paper/2609.29228/) (2026)

## Abstract (arXiv)

This paper presents a novel approach in autonomous robot control, named LLM-BRAIn, that makes possible robot behavior generation, based on operator's commands. LLM-BRAIn is a transformer-based Large Language Model (LLM) fine-tuned from Stanford Alpaca 7B model to generate robot behavior tree (BT) from the text description. We train the LLM-BRAIn on 8,5k instruction-following demonstrations, generated in the style of self-instruct using text-davinchi-003. The developed model accurately builds complex robot behavior while remaining small enough to be run on the robot's onboard microcomputer. The model gives structural and logical correct BTs and can successfully manage instructions that were not presented in training set. The experiment did not reveal any significant subjective differences between BTs generated by LLM-BRAIn and those created by humans (on average, participants were able to correctly distinguish between LLM-BRAIn generated BTs and human-created BTs in only 4.53 out of 10 cases, indicating that their performance was close to random chance). The proposed approach potentially can be applied to mobile robotics, drone operation, robot manipulator systems and Industry 4.0.