Meta-Learning Priors for Safe Bayesian Optimization

• URL: http://arxiv.org/abs/2210.00762v3
• Date: Mon, 12 Jun 2023 14:05:33 GMT
• Title: Meta-Learning Priors for Safe Bayesian Optimization
• Authors: Jonas Rothfuss, Christopher Koenig, Alisa Rupenyan, Andreas Krause
• Abstract summary: We build on a meta-learning algorithm, F-PACOH, capable of providing reliable uncertainty quantification in settings of data scarcity. As core contribution, we develop a novel framework for choosing safety-compliant priors in a data-riven manner. On benchmark functions and a high-precision motion system, we demonstrate that our meta-learned priors accelerate the convergence of safe BO approaches.
• Score: 72.8349503901712
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In robotics, optimizing controller parameters under safety constraints is an important challenge. Safe Bayesian optimization (BO) quantifies uncertainty in the objective and constraints to safely guide exploration in such settings. Hand-designing a suitable probabilistic model can be challenging, however. In the presence of unknown safety constraints, it is crucial to choose reliable model hyper-parameters to avoid safety violations. Here, we propose a data-driven approach to this problem by meta-learning priors for safe BO from offline data. We build on a meta-learning algorithm, F-PACOH, capable of providing reliable uncertainty quantification in settings of data scarcity. As core contribution, we develop a novel framework for choosing safety-compliant priors in a data-riven manner via empirical uncertainty metrics and a frontier search algorithm. On benchmark functions and a high-precision motion system, we demonstrate that our meta-learned priors accelerate the convergence of safe BO approaches while maintaining safety.

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