We propose a novel class of risk-aware control barrier functions (RA-CBFs) for the control of
stochastic safety-critical systems. Leveraging a result from the stochastic level-crossing …

Inspired by the success of control barrier functions (CBFs) in addressing safety, and the rise
of data-driven techniques for modeling functions, we propose a non-parametric approach for …

Reinforcement learning is a powerful technique for developing new robot behaviors.
However, typical lack of safety guarantees constitutes a hurdle for its practical application on …

The growing interest in robust designs and data-driven technologies for safe control
problems underscores the critical need to understand uncertainty for ensuring reliable safety …

A ski-stunt maneuver is a type of aggressive vehicle motions in which a four-wheel vehicle
runs on two wheels on one side, and the other two wheels are lifted in the air. It is a …

Safe stabilization is a significant challenge for quadrotors, which involves reaching a goal
position while avoiding obstacles. Most of the existing solutions for this problem rely on …

Safety has been a critical issue for the deployment of learning-based approaches in real-
world applications. To address this issue, control barrier function (CBF) and its variants have …

This article addresses the quadrotors' safety-critical landing control problem with external
uncertainties and collision avoidance. A geometrically robust hierarchical control strategy is …

In Gaussian Process (GP) dynamical model learning for robot control, particularly for
systems constrained by computational resources like small quadrotors equipped with low …

This article presents a method for guaranteeing the safety of a system with both parametric
and nonparametric uncertainties, while at the same time decreasing the conservatism …