1. Cautious Bayesian Optimization: A Line Tracker Case Study.
- Author
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Girbés-Juan, Vicent, Moll, Joaquín, Sala, Antonio, and Armesto, Leopoldo
- Subjects
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GAUSSIAN processes - Abstract
In this paper, a procedure for experimental optimization under safety constraints, to be denoted as constraint-aware Bayesian Optimization, is presented. The basic ingredients are a performance objective function and a constraint function; both of them will be modeled as Gaussian processes. We incorporate a prior model (transfer learning) used for the mean of the Gaussian processes, a semi-parametric Kernel, and acquisition function optimization under chance-constrained requirements. In this way, experimental fine-tuning of a performance objective under experiment-model mismatch can be safely carried out. The methodology is illustrated in a case study on a line-follower application in a CoppeliaSim environment. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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