Presentation
Exploring Benchmarks for Self-Driving Labs Using Color Matching
DescriptionSelf Driving Labs (SDLs) that combine automation of experimental procedures with autonomous decision making are gaining popularity as a means of increasing the throughput of scientific workflows. The task of identifying a mix of supplied colored pigments that matches a target color, the color matching problem, has emerged as a simple and flexible test case for these labs, as it requires experiment proposal, sample creation, and sample analysis, three common components in automated discovery applications. We present a modular, easily retargetable robotic solution to the color matching problem that allows for fully autonomous execution of a color matching protocol, with feedback from pluggable optimization approaches allowing for continuous refinement and automated publication of results facilitating experiment tracking and post-hoc analysis