Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …

A current survey of the emerging field of networked control systems is provided. The aim is to
introduce the fundamental issues involved in designing successful networked control …

This self-contained introduction to the distributed control of robotic networks offers a
distinctive blend of computer science and control theory. The book presents a broad set of …

This article presents the development of a robot-integrated smart home (RiSH) which can be
used for research in assistive technologies for elderly care. The RiSH integrates a home …

The deployment of multiple robots for achieving a common goal helps to improve the
performance, efficiency, and/or robustness in a variety of tasks. In particular, the observation …

In this note, we study optimal estimation design for sampled linear systems where the
sensors measurements are transmitted to the estimator site via a generic digital …

This paper presents Markov chain Monte Carlo data association (MCMCDA) for solving data
association problems arising in multitarget tracking in a cluttered environment. When the …

We propose a class of convex relaxations to solve the sensor network localization problem,
based on a maximum likelihood (ML) formulation. This class, as well as the tightness of the …

This paper presents an algorithm and analysis of distributed learning and cooperative
control for a multi-agent system so that a global goal of the overall system can be achieved …

In this paper, we discuss coordination problems of a group of autonomous agents, including
the target aggregation to a convex set and the state agreement. The aggregation of the …