@article{Wang2026, 
author = {Tao Wang and Chong Chen and Chao Liu and Zhuowei Wang and Lianglun Cheng and Hai Wan},
title = {Large Language Model Enhanced Intelligent Robotic Control Software Development},
year = {2026},
journal = {Tsinghua Science and Technology},
keywords = {Control Software Development, Large Language Model, Robotic Systems, Intelligent Manufacturing},
url = {https://www.sciopen.com/article/10.26599/TST.2026.9010029},
doi = {10.26599/TST.2026.9010029},
abstract = {The recent advance of robotic has led to an increasing demand for control software that governs their complex operations and adaptability across diverse tasks. Componentization is an effective way for the development of control software, while it faces two main challenges. Firstly, it is not clear how to develop unified components for control software, which impedes the assembly of components from different environments and programming languages. Meanwhile, the assembly of software components significantly relies on the domain knowledge, which is inextricably tied to task-specific demands. In order to address the aforementioned challenges, this study introduces a large language model (LLM) enhanced componentized software development framework for the agile development of the control software for robotic system. Firstly, a unified component development approach is proposed to develop the components that can be assembled seamlessly. Subsequently, a LLM is fine-tuned to generate the workflow of the task, which allows the efficient assembly the unified components. Thirdly, the software components are assembled as control software. A robotic sorting task and grinding task are implemented to validate the effectiveness of the proposed framework. The results substantiate that the proposed framework can flexibly and accurately generate control software for various production tasks.}
}