Can Large Language Models Help Developers with Robotic Finite State Machine Modification?
Abstract
Finite state machines (FSMs) are widely used to manage robot behavior logic, particularly in real-world applications that require a high degree of reliability and structure. However, traditional manual FSM design and modification processes can be time-consuming and error-prone. We propose that large language models (LLMs) can assist developers in editing FSM code for real-world robotic use cases. LLMs, with their ability to use context and process natural language, offer a solution for FSM modification with high correctness, allowing developers to update complex control logic through natural language instructions. Our approach leverages few-shot prompting and language-guided code generation to reduce the amount of time it takes to edit an FSM. To validate this approach, we evaluate it on a real-world robotics dataset, demonstrating its effectiveness in practical scenarios.
Cite
@article{arxiv.2412.05625,
title = {Can Large Language Models Help Developers with Robotic Finite State Machine Modification?},
author = {Xiangyu Robin Gan and Yuxin Ray Song and Nick Walker and Maya Cakmak},
journal= {arXiv preprint arXiv:2412.05625},
year = {2024}
}