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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
Author/Presenters
Event Type
Workshop
TimeSunday, 12 November 202310:40am - 11am MST
Location708
Large Scale Systems
Performance Measurement, Modeling, and Tools
Software Engineering
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