8 papers
Sparse shepherding control of large-scale multi-agent systems via Reinforcement Learning
Luigi Catello, Italo Napolitano, Davide Salzano +1
We propose a Reinforcement Learning framework for sparse indirect control of large-scale multi-agent systems, where few controlled agents shape the collective behavior of many unco…
Optimal Transport for Time-Varying Multi-Agent Coverage Control
Italo Napolitano, Mario di Bernardo
Coverage control algorithms have traditionally focused on static target densities, where agents are deployed to optimally cover a fixed spatial distribution. However, many applicat…
Adaptive Optimal Control for Avatar-Guided Motor Rehabilitation in Virtual Reality
Francesco De Lellis, Maria Lombardi, Egidio De Benedetto +2
A control-theoretic framework for autonomous avatar-guided rehabilitation in virtual reality, based on interpretable, adaptive motor guidance through optimal control, is presented.…
Decentralized Shepherding of Non-Cohesive Swarms Through Cluttered Environments via Deep Reinforcement Learning
Cristiana Punzo, Italo Napolitano, Cinzia Tomaselli +1
This paper investigates decentralized shepherding in cluttered environments, where a limited number of herders must guide a larger group of non-cohesive, diffusive targets toward a…
A bioreactor-based architecture for in vivo model-based and sim-to-real learning control of microbial consortium composition
Sara Maria Brancato, Davide Salzano, Davide Fiore +3
Microbial consortia offer significant biotechnological advantages over monocultures for bioproduction. However, industrial deployment is hampered by the lack of scalable architectu…
Controlling Complex Systems
Marco Coraggio, Davide Salzano, Mario di Bernardo
This chapter provides a comprehensive overview of controlling collective behavior in complex systems comprising large ensembles of interacting dynamical agents. Building upon tradi…