4 citations · 15 across the 9 of their papers we have counts for
9 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.…
Online Phase Estimation of Human Oscillatory Motions using Deep Learning
Antonio Grotta, Francesco De Lellis
Accurately estimating the phase of oscillatory systems is essential for analyzing cyclic activities such as repetitive gestures in human motion. In this work we introduce a learnin…
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…
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…
CT-DQN: Control-Tutored Deep Reinforcement Learning
Francesco De Lellis, Marco Coraggio, Giovanni Russo +2
One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy. Motivated by this, we present the design of the Contr…
External control of a genetic toggle switch via Reinforcement Learning
Sara Maria Brancato, Francesco De Lellis, Davide Salzano +2
We investigate the problem of using a learning-based strategy to stabilize a synthetic toggle switch via an external control approach. To overcome the data efficiency problem that…
Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control
F. De Lellis, M. Coraggio, G. Russo +2
We present an architecture where a feedback controller derived on an approximate model of the environment assists the learning process to enhance its data efficiency. This architec…
Control-Tutored Reinforcement Learning
Francesco De Lellis, Fabrizia Auletta, Giovanni Russo +2
We introduce a control-tutored reinforcement learning (CTRL) algorithm. The idea is to enhance tabular learning algorithms so as to improve the exploration of the state-space, and…
Control-Tutored Reinforcement Learning: an application to the Herding Problem
Francesco De Lellis, Fabrizia Auletta, Giovanni Russo +1
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 s…