This survey presents recent research on determining control-theoretic properties and
designing controllers with rigorous guarantees using semidefinite programming and for …

We present a framework for systematically combining data of an unknown linear time-
invariant system with prior knowledge on the system matrices or on the uncertainty for robust …

Algorithmic recommendations and decisions have become ubiquitous in today's society.
Many of these and other data-driven policies, especially in the realm of public policy, are …

The safe operation of physical systems typically relies on high-quality models. Since a
continuous stream of data is generated during run-time, such models are often obtained …

Safe deployment of autonomous robots in diverse scenarios requires agents that are
capable of efficiently adapting to new environments while satisfying constraints. In this …

Gaussian Process Regression (GPR) is a popular regression method, which unlike most
Machine Learning techniques, provides estimates of uncertainty for its predictions. These …

This paper presents a data-driven strategy to streamline the deployment of model-based
controllers in legged robotic hardware platforms. Our approach leverages a model-free safe …

This article proposes a data-driven modeling framework with physically consistent Gaussian
processes (GPs), enabling learning-based disturbance estimation for uncertain mechanical …

Gaussian processes have become a promising tool for various safety-critical settings, since
the posterior variance can be used to directly estimate the model error and quantify risk …

Learning optimal control policies directly on physical systems is challenging. Even a single
failure can lead to costly hardware damage. Most existing model-free learning methods that …