We are interested in predicting failures of cyber-physical systems during their operation.
Particularly, we consider stochastic systems and signal temporal logic specifications, and we …

Learning-based control approaches have shown great promise in performing complex tasks
directly from high-dimensional perception data for real robotic systems. Nonetheless, the …

In this survey, we design formal verification and control algorithms for autonomous systems
with practical safety guarantees using conformal prediction (CP), a statistical tool for …

Computing Lipschitz constants of neural networks allows for robustness guarantees in
image classification, safety in controller design, and generalization beyond the training data …

Learning-based approaches are emerging as an effective approach for safety filters for black-
box dynamical systems. Existing methods have relied on certificate functions like Control …

Learning from Demonstration (LfD) is a powerful method for non-roboticists end-users to
teach robots new tasks, enabling them to customize the robot behavior. However, modern …

Safety is a critical property for control systems in medicine, transportation, manufacturing,
and other applications, and can be defined as ensuring positive invariance of a predefined …

We consider perception-based control using state estimates that are obtained from high-
dimensional sensor measurements via learning-enabled perception maps. However, these …

The wide availability of data coupled with the computational advances in artificial
intelligence and machine learning promise to enable many future technologies such as …

We study the problem of verification and synthesis of robust control barrier functions (CBF)
for control-affine polynomial systems with bounded additive uncertainty and convex …