The use of aerial swarms to solve real-world problems has been increasing steadily,
accompanied by falling prices and improving performance of communication, sensing, and …

Contraction theory is an analytical tool to study differential dynamics of a non-autonomous
(ie, time-varying) nonlinear system under a contraction metric defined with a uniformly …

Diffusion models have recently attained significant interest within the community owing to
their strong performance as generative models. Furthermore, its application to inverse …

In this paper, a relationship is discussed between three common assumptions made in the
literature to prove local or global asymptotic stability of the synchronization manifold in …

Contraction theory provides an elegant way to analyze the behaviors of certain nonlinear
dynamical systems. Under sometimes easy to check hypotheses, systems can be shown to …

This paper addresses the problem of providing mathematical conditions that allow one to
ensure that biological networks, such as transcriptional systems, can be globally entrained …

In this article, we study necessary and sufficient conditions for contraction and incremental
stability of dynamical systems with respect to non-Euclidean norms. First, we introduce weak …

We present Neural Stochastic Contraction Metrics (NSCM), a new design framework for
provably-stable learning-based control and estimation for a class of stochastic nonlinear …

Learning-based control algorithms require data collection with abundant supervision for
training. Safe exploration algorithms ensure the safety of this data collection process even …

Recent trends pushing robots into unstructured environments with limited sensors have
motivated considerable work on planning under uncertainty and stochastic optimal control …