Lowering the Learning Curve in Laboratory Automation with IvoryOS

Laboratory automation has made major progress in recent years, driven by advances in robotics, instrumentation, and data analysis. In practice, however, building and running automated workflows is still difficult for many laboratories. Software stacks, runtime and orchestration are often fragmented, integration is highly customized, and effective use typically requires strong programming expertise. This talk presents IvoryOS, an open-source orchestration platform designed to make lab automation easier to build and adopt while also supporting hands-on learning. Instead of relying on a new domain-specific language or framework, IvoryOS inspects existing Python protocols to extract metadata and build interoperable interfaces. This provides an instantly operable runtime and separates hardware control from experimental logic and optimization, improving reuse, reproducibility, and maintainability. IvoryOS allows scientists to design and run autonomous workflows through a visual interface, enabling researchers to get started in a single day. As workflows grow more complex, researchers can gradually engage with the underlying Python code, learning programming skills in context rather than upfront. It has been deployed on more than 20 robotic systems, including educational platforms across academic and industrial labs.

Learning Objectives:

1. Explore orchestration and adoption challenges in lab automation

2. Discover how IvoryOS lowers the barrier of building and operating self-driving laboratories

3. Demonstrate how visual orchestration supports hands-on automation and progressive skill development