[论文解读] LLMs can generate robotic scripts from goal-oriented instructions in biological laboratory automation

Takashi Inagaki, Akari Kato+3 more|arXiv (Cornell University)|Apr 18, 2023

Machine Learning in Materials Science被引用 17

一句话总结

研究表明 GPT-4 能从面向目标的自然语言指令为生物实验生成 OT-2 机器人脚本，从而减少编写底层机器人代码的需求。

ABSTRACT

The use of laboratory automation by all researchers may substantially accelerate scientific activities by humans, including those in the life sciences. However, computer programs to operate robots should be written to implement laboratory automation, which requires technical knowledge and skills that may not be part of a researcher's training or expertise. In the last few years, there has been remarkable development in large language models (LLMs) such as GPT-4, which can generate computer codes based on natural language instructions. In this study, we used LLMs, including GPT-4, to generate scripts for robot operations in biological experiments based on ambiguous instructions. GPT-4 successfully generates scripts for OT-2, an automated liquid-handling robot, from simple instructions in natural language without specifying the robotic actions. Conventionally, translating the nuances of biological experiments into low-level robot actions requires researchers to understand both biology and robotics, imagine robot actions, and write robotic scripts. Our results showed that GPT-4 can connect the context of biological experiments with robot operation through simple prompts with expert-level contextual understanding and inherent knowledge. Replacing robot script programming, which is a tedious task for biological researchers, with natural-language LLM instructions that do not consider robot behavior significantly increases the number of researchers who can benefit from automating biological experiments.

研究动机与目标

• 激发实验室自动化在加速生命科学研究中的潜力。
• 证明大语言模型能够将模糊的自然语言实验目标转化为可执行的机器人脚本。
• 展示高层提示使大语言模型能够对生物学工作流程具备类似专家的情境理解。

提出的方法

• 使用 GPT-4 及相关大语言模型从自然语言指令为 OT-2 液体处理机器人生成脚本。
• 评估在没有明确底层命令的情况下，LLM 是否能够推断所需的机器人动作。
• 利用简单提示将生物实验上下文与机器人操作连接起来。
• 评估用自然语言提示替代传统机器人编程的可行性。

实验结果

研究问题

• RQ1 GPT-4 是否能从模糊的生物实验提示中生成功能性 OT-2 脚本？
• RQ2 在没有详细脚本知识的情况下，LLMs 在多大程度上能够将生物学上下文转化为合适的机器人动作？
• RQ3 简单提示是否足以使 LLMs 拥有对实验室工作流程的专家级情境理解？

主要发现

• GPT-4 能够从自然语言指令成功生成 OT-2 脚本，而无需指定机器人动作。
• LLMs 通过简洁的提示将生物实验上下文与机器人操作连接起来。
• 使用自然语言提示可以显著降低自动化生物实验的门槛。