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

TL;DR

This paper explores how large language models can assist developers in editing robotic finite state machines efficiently and accurately through natural language instructions, reducing manual effort and errors.

Contribution

It introduces a novel approach using LLMs with few-shot prompting for FSM modification in robotics, demonstrating practical effectiveness.

Findings

LLMs can accurately interpret natural language instructions for FSM editing.

The approach reduces time and errors in FSM modification.

Effective on real-world robotics datasets.

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.