This paper is concerned with the data-driven optimal control of nonlinear systems. We
present a convex formulation of the optimal control problem with a discounted cost function …

Reinforcement Learning (RL) has demonstrated impressive performance in various areas
such as video games and robotics. However, ensuring safety and stability, which are two …

In this paper, we present an approach based on the spectral analysis of the Koopman
operator for the approximate solution of the Hamilton Jacobi equation that arises while …

This letter considers the optimal control problem of nonlinear systems under safety
constraints with unknown dynamics. Departing from the standard optimal control framework …

We consider the problem of navigating a nonlinear dynamical system from some initial set to
some target set while avoiding collision with an unsafe set. We extend the concept of density …

For any suitable Optimal Control Problem (OCP) there exists a value function, defined as the
unique viscosity solution to the Hamilton-Jacobi-Bellman (HJB) Partial-Differential-Equation …

We consider the problem of navigation with safety constraints. The safety constraints are
probabilistic, where a given set is assigned a degree of safety, a number between zero and …

Most robotics applications are typically accompanied with safety restrictions that need to be
satisfied with a high degree of confidence even in environments under uncertainty …

This paper presents the implementation of of-road navigation on legged robots using convex
optimization through linear transfer operators. Given a traversability measure that captures …

This thesis is concerned with the data-driven solution to the optimal control problem with
safety constraints for a class of control-affine nonlinear systems. Designing optimal control …