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 …

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 survey, we review the recent advances in control design methods for robotic multi-
agent systems (MAS), focusing on learning-based methods with safety considerations. We …

We study the multi-agent safe control problem where agents should avoid collisions to static
obstacles and collisions with each other while reaching their goals. Our core idea is to learn …

This tutorial paper presents recent work of the authors that extends the theory of Control
Barrier Functions (CBFs) to address practical challenges in the synthesis of safe controllers …

Modeling realistic fluid and plasma flows is computationally intensive, motivating the use of
reduced-order models for a variety of scientific and engineering tasks. However, it is …

Many existing tools in nonlinear control theory for establishing stability or safety of a
dynamical system can be distilled to the construction of a certificate function which …

Autonomous Driving (AD) encounters significant safety hurdles in long-tail unforeseen
driving scenarios, largely stemming from the non-interpretability and poor generalization of …

Safety is the major consideration in controlling complex dynamical systems using
reinforcement learning (RL), where the safety certificates can provide provable safety …

Motivated by the lack of systematic tools to obtain safe control laws for hybrid systems, we
propose an optimization-based framework for learning certifiably safe control laws from data …