Physical Testbed for Self-Driving Laboratories: Comparing Bayesian and Agentic
Overview
This work introduces a physical testbed combining commercial laboratory automation with AI-driven decision-making to enable continuous, closed-loop autonomous operation.
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Environment: controlled real
“comparatively less developed. In this work, we develop a physical testbed that assembles and integrates commercially available laboratory automation instruments and demonstrate continuous, closed-loop, autonomous operation over 24 cumulative hours of unattended experimentation. We validate this platform on a minimal closed-loop discovery task in which the system autonomously formulates aqueous dye mixtures to match a”
Exact source · revision 1Source owner not reported
Control: reported autonomous
“comparatively less developed. In this work, we develop a physical testbed that assembles and integrates commercially available laboratory automation instruments and demonstrate continuous, closed-loop, autonomous operation over 24 cumulative hours of unattended experimentation. We validate this platform on a minimal closed-loop discovery task in which the system autonomously formulates aqueous dye mixtures to match a”
Exact source · revision 1Source owner not reported
Reported quantity · iterations: 6 other · reported trials · Differing source assertions
“target absorbance spectrum. The system completed six independent campaigns, each consisting of 48 closed-loop iterations over approximately four hours. In every campaign, the system autonomously selected, dispensed, and measured each mixture, with an AI-driven decision-making algorithm determining subsequent formulations without human intervention. Using this testbed, we compared the performance of two AI-driven dec”
Exact source · revision 1Source owner not reported
Reported quantity · iterations: 48 other · reported trials · Differing source assertions
“target absorbance spectrum. The system completed six independent campaigns, each consisting of 48 closed-loop iterations over approximately four hours. In every campaign, the system autonomously selected, dispensed, and measured each mixture, with an AI-driven decision-making algorithm determining subsequent formulations without human intervention. Using this testbed, we compared the performance of two AI-driven dec”
Exact source · revision 1Source owner not reported
Reported quantity · elapsed_hours: 4 hours · Basis not reported
“target absorbance spectrum. The system completed six independent campaigns, each consisting of 48 closed-loop iterations over approximately four hours. In every campaign, the system autonomously selected, dispensed, and measured each mixture, with an AI-driven decision-making algorithm determining subsequent formulations without human intervention. Using this testbed, we compared the performance of two AI-driven dec”
Exact source · revision 1Source owner not reported
Reported quantity · iterations: 48 other · reported trials · Differing source assertions
“ision-making algorithms, traditional Bayesian optimization (BO) and an agentic approach, in selecting mixtures under a fixed total-volume constraint. The agentic optimizer matched target absorbance spectra using fewer iterations than BO, with reductions up to 52%, and converged on a solution for one target that BO failed to match within 48 iterations. These results demonstrate a reference physical platform for SDLs t”
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- arXiv Robotics — research abstractsSignalA Physical Testbed for Self-Driving Laboratories: Comparing Bayesian and Agentic Optimization in a Minimal Closed-Loop Color Formulation Task
This work introduces a physical testbed combining commercial laboratory automation with AI-driven decision-making to enable continuous, closed-loop autonomous operation.
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There is not enough continuous observation data to show a trend.
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