257 citations · 996 across the 51 of their papers we have counts for
7 papers · 1 filter
Control of over-redundant cooperative manipulation via sampled communication
Enrica Rossi, Marco Tognon, Ruggero Carli +2
In this work we consider the problem of mobile robots that need to manipulate/transport an object via cables or robotic arms. We consider the scenario where the number of manipulat…
Coordinated Multi-Robot Trajectory Tracking Control over Sampled Communication
Enrica Rossi, Marco Tognon, Luca Ballotta +4
In this paper, we propose an inverse-kinematics controller for a class of multi-robot systems in the scenario of sampled communication. The goal is to make a group of robots perfor…
EASE: Energy-Aware Job Scheduling for Vehicular Edge Networks With Renewable Energy Resources
Giovanni Perin, Francesca Meneghello, Ruggero Carli +2
The energy sustainability of multi-access edge computing (MEC) platforms is here addressed by developing Energy-Aware job Scheduling at the Edge (EASE), a computing resource schedu…
Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models
Alberto Dalla Libera, Fabio Amadio, Daniel Nikovski +2
In this paper, we consider the use of black-box Gaussian process (GP) models for trajectory tracking control based on feedback linearization, in the context of mechanical systems.…
A Stochastic Operator Framework for Optimization and Learning with Sub-Weibull Errors
Nicola Bastianello, Liam Madden, Ruggero Carli +1
This paper proposes a framework to study the convergence of stochastic optimization and learning algorithms. The framework is modeled over the different challenges that these algor…
Model-based Policy Search for Partially Measurable Systems
Fabio Amadio, Alberto Dalla Libera, Ruggero Carli +2
In this paper, we propose a Model-Based Reinforcement Learning (MBRL) algorithm for Partially Measurable Systems (PMS), i.e., systems where the state can not be directly measured,…
Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application
Fabio Amadio, Alberto Dalla Libera, Riccardo Antonello +3
In this paper, we present a Model-Based Reinforcement Learning (MBRL) algorithm named \emph{Monte Carlo Probabilistic Inference for Learning COntrol} (MC-PILCO). The algorithm reli…