Noise Conditioned Bayesian Optimization Strategy Selection for Self-Driving Labs
- 1Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), 76344Eggenstein-Leopoldshafen, Germany
Abstract
Self-driving labs have become a widespread paradigm for automating research and development processes across disciplines such as biology, chemistry, and pharmaceutical research. Such systems combine robotic hardware and software to iteratively optimize experimental outcomes in a closed loop. Noise inherent in sensors and robotic actuation not only affects the speed at which the optimal result is found, but also which optimization strategies perform best. We present a simulation-based methodology to analyze selfdriving lab loops and to select the optimal acquisition function and hyperparameters for Bayesian optimization by incorporating known uncertainty in realistic simulated system behavior. Our results indicate that optimizer selection informed by noise characteristics can significantly reduce the number of required iterations.
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BibTeX
@article{martin2026noise,
title = {{Noise Conditioned Bayesian Optimization Strategy Selection for Self-Driving Labs}},
author = {Krüger Martin and Höfer Hawo H. and Exler David and Gattung Rolf and Reischl Markus},
journal = {Current Directions in Biomedical Engineering},
year = {2026},
month = oct,
doi = {10.1515/cdbme-2026-0172},
url = {https://doi.org/10.1515/cdbme-2026-0172},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Noise Conditioned Bayesian Optimization Strategy Selection for Self-Driving Labs
AU - Krüger Martin
AU - Höfer Hawo H.
AU - Exler David
AU - Gattung Rolf
AU - Reischl Markus
PY - 2026
DA - 2026/10/26
JO - Current Directions in Biomedical Engineering
DO - 10.1515/cdbme-2026-0172
UR - https://doi.org/10.1515/cdbme-2026-0172
AB - Self-driving labs have become a widespread paradigm for automating research and development processes across disciplines such as biology, chemistry, and pharmaceutical research. Such systems combine robotic hardware and software to iteratively optimize experimental outcomes in a closed loop. Noise inherent in sensors and robotic actuation not only affects the speed at which the optimal result is found, but also which optimization strategies perform best. We present a simulation-based methodology to analyze selfdriving lab loops and to select the optimal acquisition function and hyperparameters for Bayesian optimization by incorporating known uncertainty in realistic simulated system behavior. Our results indicate that optimizer selection informed by noise characteristics can significantly reduce the number of required iterations.
SN - 2364-5504
ER -