Researchers at the Department of Energy’s Pacific Northwest National Laboratory use a slew of autonomous robots to design and implement experiments. However, setting up an experiment on an autonomous lab robot is surprisingly slow. The effort requires a lengthy back-and-forth between a scientist and an engineer to design the experimental steps—a process that can take weeks.

To help researchers work more efficiently, a PNNL team developed a generative agentic AI that can quickly translate experimental goals into instructions for a laboratory robot. The translation agent, called AutoLabs, is currently designed to operate with Big Kahuna, an automated robot built by Unchained Labs that researchers use to study new and existing battery materials. The system can carry out multistep experimental workflows, including mixing, heating, stirring and filtering samples with minimal human intervention. By automating these processes, researchers can perform five to 10 times more experiments than would be practical by hand.

The team published a paper in Scientific Reports about AutoLabs, and the software is also available for other researchers to download on GitHub.