Research Article
Empowering Universal Robot Programming with Fine-Tuned Large Language Models
@ARTICLE{10.4108/airo.8983, author={Tien Dat Le and Minhhuy Le}, title={Empowering Universal Robot Programming with Fine-Tuned Large Language Models}, journal={EAI Endorsed Transactions on AI and Robotics}, volume={4}, number={1}, publisher={EAI}, journal_a={AIRO}, year={2025}, month={7}, keywords={Large Language Models, LLMs, Fine-tuning, Synthesis dataset, URScript}, doi={10.4108/airo.8983} }- Tien Dat Le
Minhhuy Le
Year: 2025
Empowering Universal Robot Programming with Fine-Tuned Large Language Models
AIRO
EAI
DOI: 10.4108/airo.8983
Abstract
LLMs are transforming AI but face challenges in robotics due to domain-specific requirements. This paper explores LLM-generated URScript code for Universal Robots (UR), improving automation accessibility. A fine-tuning dataset of 20,000 synthetic samples, based on 514 validated human-created examples, enhances performance. Using the Unsloth framework, we fine-tune and evaluate the model in real-world scenarios. Results demonstrate LLMs’potential to simplify UR robot programming, highlighting their value in industrial automation. The video demo is available at the following link, and the codebase will be added soon: https://github.com/t1end4t/llm-robotics
Copyright © 2025 Author Name et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
