Recent years have seen significant progress in the realm of robot autonomy, accompanied
by the expanding reach of robotic technologies. However, the emergence of new …

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

This paper introduces a new parameterization of deep neural networks (both fully-connected
and convolutional) with guaranteed $\ell^ 2$ Lipschitz bounds, ie limited sensitivity to input …

Abstract Control Barrier Functions (CBFs) are a popular approach for safe control of
nonlinear systems. In CBF-based control, the desired safety properties of the system are …

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 …

Deep reinforcement learning (DRL) is a powerful machine learning paradigm for generating
agents that control autonomous systems. However, the" black box" nature of DRL agents …

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 …

In this paper, we deal with output tracking control problems for input-affine nonlinear
systems. We propose a deep learning-based solution whose foundations lay in control …

We study the optimal control of multiple-input and multiple-output dynamical systems via the
design of neural network-based controllers with stability and output tracking guarantees …

In this paper, we describe a lightweight Python framework that provides integrated learning
and verification of neural Lyapunov functions for stability analysis. The proposed tool …