Enhancing PyLabRobot for Scalable and Accessible Open-Source Laboratory Automation
openNIBIB - National Institute of Biomedical Imaging and Bioengineering
Abstract
Laboratory automation is reshaping scientific discovery by enabling high-throughput, precise, and scalable
experimentation. Yet, unlike fields such as manufacturing, transportation, and agriculture—where robotics has
driven rapid advances in innovation—laboratory automation has lagged behind. Most commercial lab robots
are still built to mimic human pipetting rather than enabling experiments that were previously impossible to
execute manually. Closed, proprietary platforms prevent researchers from fully leveraging their computational
skills and severely limit training opportunities for students and early-stage investigators. This project addresses
those gaps by expanding PyLabRobot (PLR), a free, open-source, Python-based framework for designing,
simulating, and executing complex liquid handling protocols across a wide range of robotic systems. PLR is
software- and hardware-agnostic, supports complete virtual simulation, and can be run in the cloud, making it
accessible to anyone with basic Python skills—no expensive equipment required. By enabling users to develop
and debug protocols entirely in simulation, PLR allows students, educators, and researchers to build
automation expertise before ever stepping into a physical lab. This significantly lowers the entry barrier and
ensures that trainees enter the workforce ready to contribute immediately to automation-driven projects.
Python’s ubiquity across STEM and engineering makes PLR a natural training tool for modern science. In a
recent graduate-level class piloting PLR in full simulation mode, students with no prior robotics background
programmed highly sophisticated workflows running from CRISPRi screening, AlphaLISA and TR-FRET
assays, small molecule synthesis, FISH staining, and cell line manufacturing—demonstrating both the
accessibility and real-world relevance of the platform. These examples highlight PLR’s potential not just to
replicate existing workflows, but to unlock new experimental designs that would be infeasible with manual
techniques. This R03 proposal supports three focused aims: (1) Create a web-based repository of validated,
sharable protocols and documentation to enable reproducibility, benchmarking, and AI-driven method
development; (2) Integrate PLR into cloud platforms like Google Colab and develop a dedicated IDE for
real-time visualization, debugging, and seamless protocol execution; (3) Build a digital twin simulation engine
that models labware, deck layouts, and liquid handling to support predictive optimization before execution. This
project is supported by letters of support from the entire PyLabRobot development team and multiple industry
stakeholders, including leading automation companies. By removing the barriers that have long constrained
laboratory robotics programming, this work will accelerate discovery, lower costs, and build a highly skilled U.S.
workforce ready to lead in bioengineering, automation, and advanced manufacturing.
Up to $78K
health research