17 citations · 21 across the 16 of their papers we have counts for
6 papers · 1 filter
One controller to rule them all
Riccardo Busetto, Valentina Breschi, Marco Forgione +2
Imagine having a system to control and only know that it belongs to a certain class of dynamical systems. Would it not be amazing to simply plug in a controller and have it work as…
Enhanced Transformer architecture for in-context learning of dynamical systems
Matteo Rufolo, Dario Piga, Gabriele Maroni +1
Recently introduced by some of the authors, the in-context identification paradigm aims at estimating, offline and based on synthetic data, a meta-model that describes the behavior…
The Duality of Generative AI and Reinforcement Learning in Robotics: A Review
Angelo Moroncelli, Vishal Soni, Marco Forgione +3
Recently, generative AI and reinforcement learning (RL) have been redefining what is possible for AI agents that take information flows as input and produce intelligent behavior. A…
RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling
Manuel Bianchi Bazzi, Asad Ali Shahid, Christopher Agia +6
The landscape of Deep Learning has experienced a major shift with the pervasive adoption of Transformer-based architectures, particularly in Natural Language Processing (NLP). Nove…
Model order reduction of deep structured state-space models: A system-theoretic approach
Marco Forgione, Manas Mejari, Dario Piga
With a specific emphasis on control design objectives, achieving accurate system modeling with limited complexity is crucial in parametric system identification. The recently intro…
Synthetic data generation for system identification: leveraging knowledge transfer from similar systems
Dario Piga, Matteo Rufolo, Gabriele Maroni +2
This paper addresses the challenge of overfitting in the learning of dynamical systems by introducing a novel approach for the generation of synthetic data, aimed at enhancing mode…