2021
DOI: 10.1007/s10994-021-06019-1
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Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics

Abstract: Selecting the right tuning parameters for algorithms is a pravelent problem in machine learning that can significantly affect the performance of algorithms. Data-efficient optimization algorithms, such as Bayesian optimization, have been used to automate this process. During experiments on real-world systems such as robotic platforms these methods can evaluate unsafe parameters that lead to safety-critical system failures and can destroy the system. Recently, a safe Bayesian optimization algorithm, called Safe… Show more

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Cited by 130 publications
(121 citation statements)
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“…1) Robot skill parameter inference: Several approaches for the automatic optimization of robot skill parameters have been proposed, which rely on gradient-free optimization techniques such as evolutionary algorithms [7], [8], [9] or Bayesian optimization [10], [11], [12] due to the non-differentiability of most skill libraries and frameworks. Gradient-free approaches require frequent execution of the skills during optimization, which is a time-consuming process if done on real robot systems, has to be repeated whenever the task objectives change and often require good initial parameterizations.…”
Section: Related Workmentioning
confidence: 99%
“…1) Robot skill parameter inference: Several approaches for the automatic optimization of robot skill parameters have been proposed, which rely on gradient-free optimization techniques such as evolutionary algorithms [7], [8], [9] or Bayesian optimization [10], [11], [12] due to the non-differentiability of most skill libraries and frameworks. Gradient-free approaches require frequent execution of the skills during optimization, which is a time-consuming process if done on real robot systems, has to be repeated whenever the task objectives change and often require good initial parameterizations.…”
Section: Related Workmentioning
confidence: 99%
“…Due to these properties, GPs gained increasing attention in the field of reinforcement learning and system identification. Especially, when safety guarantees are necessary, GPs are favored in reinforcement learning (Berkenkamp et al, 2016a(Berkenkamp et al, ,c, 2017Koller et al, 2018) as well as control (Berkenkamp and Schoellig, 2015;Umlauft et al, 2017;Beckers and Hirche, 2018;Lederer et al, 2020;Umlauft et al, 2018;Helwa et al, 2019). These approaches heavily rely on error bounds of GP regression and are therefore limited by the strict assumptions made in previous works on GP uniform error bounds (Srinivas et al, 2012;Chowdhury and Gopalan, 2017).…”
Section: Introductionmentioning
confidence: 99%
“…In this work, we restrict the range of the optimization variables to a limited set where the system is stable and focus on overshoot and set point tracking errors. Bayesian optimization in controller tuning where stability is guaranteed through safe exploration has been proposed in (Berkenkamp et al, 2016b), and applied for robotic applications (Berkenkamp et al, 2016a), and in process systems (Khosravi et al, 2019a,b). The proposed Bayesian optimization tuning ensures a compromise between the need of extensive number of trials for finding the optimal gains (according to a specified performance criterion), and a single trial, as resulting from standard methods, where a sub-optimal gain with respect to the performance of the system is found, but stability is ensured for a wide range of operation.…”
Section: Introductionmentioning
confidence: 99%