17 citations · 32 across the 20 of their papers we have counts for
6 papers · 1 filter
Nonlinear System Identification Nano-drone Benchmark
Riccardo Busetto, Elia Cereda, Marco Forgione +3
We introduce a benchmark for system identification based on 75k real-world samples from the Crazyflie 2.1 Brushless nano-quadrotor, a sub-50g aerial vehicle widely adopted in robot…
Distributionally robust minimization in meta-learning for system identification
Matteo Rufolo, Dario Piga, Marco Forgione
Meta learning aims at learning how to solve tasks, and thus it allows to estimate models that can be quickly adapted to new scenarios. This work explores distributionally robust mi…
Learning Low-Dimensional Embeddings for Black-Box Optimization
Riccardo Busetto, Manas Mejari, Marco Forgione +2
When gradient-based methods are impractical, black-box optimization (BBO) provides a valuable alternative. However, BBO often struggles with high-dimensional problems and limited t…
Manifold meta-learning for reduced-complexity neural system identification
Marco Forgione, Ankush Chakrabarty, Dario Piga +2
System identification has greatly benefited from deep learning techniques, particularly for modeling complex, nonlinear dynamical systems with partially unknown physics where tradi…
In-Context Learning for Zero-Shot Speed Estimation of BLDC motors
Alessandro Colombo, Riccardo Busetto, Valentina Breschi +3
Accurate speed estimation in sensorless brushless DC motors is essential for high-performance control and monitoring, yet conventional model-based approaches struggle with system n…
dynoGP: Deep Gaussian Processes for dynamic system identification
Alessio Benavoli, Dario Piga, Marco Forgione +1
In this work, we present a novel approach to system identification for dynamical systems, based on a specific class of Deep Gaussian Processes (Deep GPs). These models are construc…