The last half decade has seen a steep rise in the number of contributions on safe learning
methods for real-world robotic deployments from both the control and reinforcement learning …

Modern autonomous systems, such as flying, legged, and wheeled robots, are generally
characterized by high-dimensional nonlinear dynamics, which presents challenges for …

Learning-enabled control systems have demonstrated impressive empirical performance on
challenging control problems in robotics, but this performance comes at the cost of reduced …

Many safety-critical applications of neural networks, such as robotic control, require safety
guarantees. This article introduces a method for ensuring the safety of learned models for …

Today's control engineering problems exhibit an unprecedented complexity, with examples
including the reliable integration of renewable energy sources into power grids, safe …

Inspired by the success of imitation and inverse reinforcement learning in replicating expert
behavior through optimal control, we propose a learning based approach to safe controller …

In this paper, the issue of model uncertainty in safety-critical control is addressed with a data-
driven approach. For this purpose, we utilize the structure of an input-ouput linearization …

This paper works towards unifying two popular approaches in the safety control community:
Hamilton-Jacobi (HJ) reachability and Control Barrier Functions (CBFs). HJ Reachability has …

A new framework is developed for control of constrained nonlinear systems with structured
parametric uncertainty. Forward invariance of a safe set is achieved through online …

This survey provides a brief overview on the control Lyapunov function (CLF) and control
barrier function (CBF) for general nonlinear-affine control systems. The problem of control is …