Reliable and efficient trajectory generation methods are a fundamental need for
autonomous dynamical systems. The goal of this article is to provide a comprehensive …

Reliable and efficient trajectory generation methods are a fundamental need for
autonomous dynamical systems of tomorrow. The goal of this article is to provide a …

Trajectory optimization under uncertainty underpins a wide range of applications in robotics.
However, existing methods are limited in terms of reasoning about sources of epistemic and …

We revisit the sample average approximation (SAA) approach for nonconvex stochastic
programming. We show that applying the SAA approach to problems with expected value …

This work considers the optimal covariance steering problem for stochastic systems subject
to both additive noise and uncertain parameters which may enter multiplicatively with the …

In this paper, we introduce a novel approach to solve the (mean-covariance) steering
problem for a fairly general class of linear continuous-time stochastic systems subject to …

ABSTRACT A tailored optimal control software package is developed based on ensemble
optimal control theory, desensitized ensemble optimal control, contraction metrics and …

In this paper, we consider continuous-time stochastic optimal control problems where the
cost is evaluated through a coherent risk measure. We provide an explicit gradient descent …

We derive first-order Pontryagin optimality conditions for stochastic optimal control with
deterministic controls for systems modeled by rough differential equations (RDE) driven by …

We propose a novel non-parametric learning paradigm for the identification of drift and
diffusion coefficients of non-linear stochastic differential equations, which relies upon …