1 citations · 1 across the 4 of their papers we have counts for
5 papers
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…
Hierarchical Policy-Gradient Reinforcement Learning for Multi-Agent Shepherding Control of Non-Cohesive Targets
Stefano Covone, Italo Napolitano, Francesco De Lellis +1
We propose a decentralized reinforcement learning solution for multi-agent shepherding of non-cohesive targets using policy-gradient methods. Our architecture integrates target-sel…
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…
Emergent Cooperative Strategies for Multi-Agent Shepherding via Reinforcement Learning
Italo Napolitano, Andrea Lama, Francesco De Lellis +1
We present a decentralized reinforcement learning (RL) approach to address the multi-agent shepherding control problem, departing from the conventional assumption of cohesive targe…