Meta-Learning Priors for Safe Bayesian Optimization

by   Jonas Rothfuss, et al.
ETH Zurich

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.


page 7

page 22

page 25

page 26

page 27


Meta-Learning Reliable Priors in the Function Space

Meta-Learning promises to enable more data-efficient inference by harnes...

Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning

Breaking safety constraints in control systems can lead to potential ris...

Safe Model-Based Meta-Reinforcement Learning: A Sequential Exploration-Exploitation Framework

Safe deployment of autonomous robots in diverse environments requires ag...

Safe Interactive Model-Based Learning

Control applications present hard operational constraints. A violation o...

Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization

Tuning machine parameters of particle accelerators is a repetitive and t...

Safe and Efficient Model-free Adaptive Control via Bayesian Optimization

Adaptive control approaches yield high-performance controllers when a pr...

Meta-Learning Hypothesis Spaces for Sequential Decision-making

Obtaining reliable, adaptive confidence sets for prediction functions (h...

Please sign up or login with your details

Forgot password? Click here to reset