  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.
