A Physical Testbed for Self-Driving Laboratories: Comparing Bayesian and Agentic Optimization in a Minimal Closed-Loop Color Formulation Task

Key points

A testbed integrating commercial robots and measurement instruments performed unattended closed-loop experimentation for over 24 cumulative hours across six campaigns.
Each campaign completed 48 closed-loop iterations in approximately four hours without human intervention.
On the dye-mixture matching task, the system achieved an average RMSE of 9.95 × 10−3 OD.
The agentic optimizer reached the target with 52% fewer iterations for target 1 and 32% fewer for target 2.
The agentic optimizer matched target 3, which BO failed to match within 48 iterations, in 11 iterations.
The agent switched between several models during the campaigns, including linear models, Hill curves, and Michaelis-Menten curves.

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