用大模型让开发者用自然语言改机器人状态机代码
Can Large Language Models Help Developers with Robotic Finite State Machine Modification?
- 用少样本提示+语言引导生成代码，降低修改门槛
- 在真实机器人数据集上验证，提升修改效率与正确率
- 适合需要快速迭代控制逻辑的机器人开发人员
有限状态机（FSM）广泛用于管理机器人行为逻辑，尤其在对可靠性与结构要求高的实际应用中。然而，传统手动设计和修改FSM过程耗时且易出错。本文提出利用大语言模型（LLMs）辅助开发者编辑适用于真实机器人场景的FSM代码。得益于其上下文理解和自然语言处理能力，LLMs可通过自然语言指令实现高正确率的复杂控制逻辑更新。本方法采用少样本提示与语言引导代码生成技术，显著减少修改时间。我们在一个真实机器人数据集上验证了该方法的有效性，证明其在实际场景中的可行性与优势。
原文摘要 · Abstract (English)
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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