For single agent systems, probabilistic machine learning techniques such as Gaussian
process regression have been shown to be suitable methods for inferring models of …

In this work, we propose a novel safe and scalable decentralized solution for multi-agent
control in the presence of stochastic disturbances. Safety is mathematically encoded using …

In this paper, a distributed learning leader-follower consensus protocol based on Gaussian
process regression for a class of nonlinear multi-agent systems with unknown dynamics is …

In this section, we introduce some prerequisites on covariance steering and ADMM. First, we
define the notation that we follow throughout the paper. Next, we present the single-agent …

Decentralized non-convex optimization is important in many problems of practical relevance.
Existing decentralized methods, however, typically either lack convergence guarantees for …

While Gaussian process (GP) models promise to enable learning control, their application
creates theoretical and practical issues. In this thesis, theoretical challenges are addressed …

Formation control algorithms for multi-agent systems have gained much attention in the
recent years due to the increasing amount of mobile and aerial robotic swarms. The design …

This paper proposes a receding horizon active learning and control problem for dynamical
systems in which Gaussian processes (GPs) are utilized to model the system dynamics. The …

This paper focuses on distributed learning-based control of decentralized multi-agent
systems where the agents' dynamics are modeled by Gaussian Processes (GPs). Two …

This paper investigates the collision-free control problem for multi-agent systems. For such
multi-agent systems, it is the typical situation where conventional methods using either the …