no code implementations • 25 Mar 2024 • Gian Carlo Maffettone, Mario di Bernardo, Maurizio Porfiri
This paper investigates the robustness of a novel high-dimensional continuification control method for complex multi-agent systems.
no code implementations • 11 Mar 2024 • Francesco De Lellis, Marco Coraggio, Nathan C. Foster, Riccardo Villa, Cristina Becchio, Mario di Bernardo
We present a data-driven control architecture for modifying the kinematics of robots and artificial avatars to encode specific information such as the presence or not of an emotion in the movements of an avatar or robot driven by a human operator.
no code implementations • 15 Dec 2023 • Sara Maria Brancato, Davide Salzano, Francesco De Lellis, Davide Fiore, Giovanni Russo, Mario di Bernardo
Our work showcases the viability of learning-based strategies for the control of cellular density in bioreactors, making a step forward toward their use for the control of the composition of microbial consortia.
1 code implementation • 16 Nov 2023 • Francesco De Lellis, Marco Coraggio, Giovanni Russo, Mirco Musolesi, Mario di Bernardo
In addressing control problems such as regulation and tracking through reinforcement learning, it is often required to guarantee that the acquired policy meets essential performance and stability criteria such as a desired settling time and steady-state error prior to deployment.
no code implementations • 19 Sep 2023 • Marco Coraggio, Mario di Bernardo
We consider the problem of optimizing the interconnection graphs of complex networks to promote synchronization.
1 code implementation • 22 Apr 2023 • Andrea Lama, Mario di Bernardo
We present preliminary results on the problem of driving the dynamics of a group of agents, the herders, so as to steer the collective behaviour of another group of agents, the targets, interacting with them.
no code implementations • 23 Mar 2023 • Gian Carlo Maffettone, Maurizio Porfiri, Mario di Bernardo
We investigate the stability and robustness properties of a continuification-based strategy for the control of large-scale multiagent systems.
1 code implementation • 21 Mar 2023 • Andrea Giusti, Marco Coraggio, Mario di Bernardo
Geometric pattern formation is an important emergent behavior in many applications involving large-scale multi-agent systems, such as sensor networks deployment and collective transportation.
no code implementations • 3 Jan 2023 • Francesco Lo Iudice, Ricardo Cardona-Rivera, Antonio Grotta, Marco Coraggio, Mario di Bernardo
The problem of partitioning a power grid into a set of islands can be a solution to restore power dispatchment in sections of a grid affected by an extreme failure.
no code implementations • 2 Dec 2022 • Francesco De Lellis, Marco Coraggio, Giovanni Russo, Mirco Musolesi, Mario di Bernardo
One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy.
no code implementations • 29 Sep 2022 • Gian Carlo Maffettone, Alain Boldini, Mario di Bernardo, Maurizio Porfiri
In this paper, we propose a method to control large-scale multiagent systems swarming in a ring.
1 code implementation • 6 Jun 2022 • Fabrizia Auletta, Rachel W. Kallen, Mario di Bernardo, Micheal J. Richardson
This study uses supervised machine learning (SML) and explainable artificial intelligence (AI) to model, predict and understand human decision-making during skillful joint-action.
no code implementations • 11 Apr 2022 • Sara Maria Brancato, Francesco De Lellis, Davide Salzano, Giovanni Russo, Mario di Bernardo
We investigate the problem of using a learning-based strategy to stabilize a synthetic toggle switch via an external control approach.
no code implementations • 10 Jan 2022 • Marco Coraggio, Saber Jafarpour, Francesco Bullo, Mario di Bernardo
Given a flow network with variable suppliers and fixed consumers, the minimax flow problem consists in minimizing the maximum flow between nodes, subject to flow conservation and capacity constraints.
no code implementations • 12 Nov 2021 • Marco Coraggio, Pietro DeLellis, S. John Hogan, Mario di Bernardo
We study convergence in networks of piecewise-smooth (PWS) systems that commonly arise in applications to model dynamical systems whose evolution is affected by macroscopic events such as switches and impacts.
no code implementations • 12 Nov 2021 • Daniel A. Burbano-Lombana, Marco Coraggio, Mario di Bernardo, Franco Garofalo, Michele Pugliese
Shimmy is a dangerous phenomenon that occurs when aircraft's nose landing gears oscillate in a rapid and uncontrollable fashion.
no code implementations • 26 Jul 2021 • Ricardo Cardona-Rivera, Francesco Lo Iudice, Antonio Grotta, Marco Coraggio, Mario di Bernardo
The problem of partitioning a power grid into a set of microgrids, or islands, is of interest for both the design of future smart grids, and as a last resort to restore power dispatchment in sections of a grid affected by an extreme failure.
1 code implementation • 25 Mar 2021 • Marco Coraggio, Shihao Xie, Francesco De Lellis, Giovanni Russo, Mario di Bernardo
This paper is concerned with the design of intermittent non-pharmaceutical strategies to mitigate the spread of the COVID-19 epidemic exploiting network epidemiological models.
no code implementations • 12 Dec 2020 • Francesco De Lellis, Giovanni Russo, Mario di Bernardo
We introduce a control-tutored reinforcement learning (CTRL) algorithm.
no code implementations • 15 May 2020 • Fabio Della Rossa, Davide Salzano, Anna Di Meglio, Francesco De Lellis, Marco Coraggio, Carmela Calabrese, Agostino Guarino, Ricardo Cardona, Pietro DeLellis, Davide Liuzza, Francesco Lo Iudice, Giovanni Russo, Mario di Bernardo
Using the model, we confirm the effectiveness at the regional level of the national lockdown strategy implemented so far by the Italian government to mitigate the spread of the disease and show its efficacy at the regional level.
Physics and Society Populations and Evolution 93C10, 92D30, 92D25 J.2
no code implementations • 12 Dec 2019 • Francesco De Lellis, Fabrizia Auletta, Giovanni Russo, Piero De Lellis, Mario di Bernardo
We introduce a control-tutored reinforcement learning (CTRL) algorithm.
no code implementations • 26 Nov 2019 • Francesco De Lellis, Fabrizia Auletta, Giovanni Russo, Mario di Bernardo
In this extended abstract we introduce a novel control-tutored Q-learning approach (CTQL) as part of the ongoing effort in developing model-based and safe RL for continuous state spaces.
no code implementations • 9 Jul 2019 • Davide Marchese, Marco Coraggio, S. John Hogan, Mario di Bernardo
The Painlev\'e paradox is a phenomenon that causes instability in mechanical systems subjects to unilateral constraints.
no code implementations • 11 Jun 2019 • Maria Lombardi, Davide Liuzza, Mario di Bernardo
In many joint-action scenarios, humans and robots have to coordinate their movements to accomplish a given shared task.