Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics

DOI10.1007/s10994-021-06019-1arXiv1602.04450OpenAlexW3175352502MaRDI QIDQ6053801

Andreas Krause, Felix Berkenkamp, Angela P. Schoellig

Publication date: 24 October 2023

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1602.04450

zbMATH Keywords

reinforcement learningroboticsBayesian optimizationsafety constraintssafe exploration

Mathematics Subject Classification ID

Bayesian inference (62F15) Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Cites Work

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• Provably safe and robust learning-based model predictive control
• Posterior consistency of Gaussian process prior for nonparametric binary regression
• A taxonomy of global optimization methods based on response surfaces
• Kernels for Vector-Valued Functions: A Review
• Support Vector Machines
• Safe Exploration of State and Action Spaces in Reinforcement Learning
• A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems
• Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting
• Convergence rates of efficient global optimization algorithms

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