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arXiv · 2412.05625

Can Large Language Models Help Developers with Robotic Finite State Machine Modification?

Xiangyu Robin Gan·Yuxin Ray Song·Nick Walker·Maya Cakmak
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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.

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Xiangyu Robin Gan, Yuxin Ray Song, Nick Walker, Maya Cakmak. 2024-12-07. Can Large Language Models Help Developers with Robotic Finite State Machine Modification?. https://arxiv.org/abs/2412.05625

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