1 code implementation • 21 Oct 2024 • Angelo Moroncelli, Vishal Soni, Asad Ali Shahid, Marco Maccarini, Marco Forgione, Dario Piga, Blerina Spahiu, Loris Roveda
We analyze the use of foundation models as action planners, the development of robotics-specific foundation models, and the mutual benefits of combining FMs with RL.
no code implementations • 4 Oct 2024 • Matteo Rufolo, Dario Piga, Gabriele Maroni, Marco Forgione
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 of a whole class of systems.
no code implementations • 31 May 2024 • Gabriele Maroni, Filip Stojceski, Lorenzo Pallante, Marco A. Deriu, Dario Piga, Gianvito Grasso
The CPP predictive model works synergistically with an optimization algorithm, which is tuned to enhance computational efficiency while maintaining optimization performance.
1 code implementation • 21 Mar 2024 • 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.
1 code implementation • 8 Mar 2024 • Dario Piga, Matteo Rufolo, Gabriele Maroni, Manas Mejari, Marco Forgione
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 model generalization and robustness in scenarios characterized by data scarcity.
1 code implementation • 21 Feb 2024 • Loddo Fabio, Dario Piga, Michelucci Umberto, El Ghazouali Safouane
Within this context, this paper focus on the cloud segmentation from remote sensing imagery.
no code implementations • 7 Dec 2023 • Riccardo Busetto, Valentina Breschi, Marco Forgione, Dario Piga, Simone Formentin
State estimation has a pivotal role in several applications, including but not limited to advanced control design.
1 code implementation • 7 Dec 2023 • Dario Piga, Filippo Pura, Marco Forgione
In-context system identification aims at constructing meta-models to describe classes of systems, differently from traditional approaches that model single systems.
1 code implementation • 23 Nov 2023 • Gabriele Maroni, Loris Cannelli, Dario Piga
Common regularization algorithms for linear regression, such as LASSO and Ridge regression, rely on a regularization hyperparameter that balances the tradeoff between minimizing the fitting error and the norm of the learned model coefficients.
no code implementations • 21 Sep 2023 • Francesca Venturini, Silvan Fluri, Manas Mejari, Michael Baumgartner, Dario Piga, Umberto Michelucci
This work systematically investigates the oxidation of extra virgin olive oil (EVOO) under accelerated storage conditions with UV absorption and total fluorescence spectroscopy.
no code implementations • 6 Sep 2023 • Raffaele Giuseppe Cestari, Gabriele Maroni, Loris Cannelli, Dario Piga, Simone Formentin
The calibration and training of a neural network is a complex and time-consuming procedure that requires significant computational resources to achieve satisfactory results.
no code implementations • 4 Sep 2023 • Manas Mejari, Ankit Gupta, Dario Piga
We present a direct data-driven approach to synthesize robust control invariant (RCI) sets and their associated gain-scheduled feedback control laws for linear parameter-varying (LPV) systems subjected to bounded disturbances.
6 code implementations • 25 Aug 2023 • Marco Forgione, Filippo Pura, Dario Piga
Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavior of other systems within the same class?
1 code implementation • 13 Apr 2023 • Marco Forgione, Dario Piga
Effective quantification of uncertainty is an essential and still missing step towards a greater adoption of deep-learning approaches in different applications, including mission-critical ones.
no code implementations • 27 Feb 2023 • Le Anh Dao, Loris Roveda, Marco Maccarini, Matteo Lavit Nicora, Marta Mondellini, Matteo Meregalli Falerni, Palaniappan Veerappan, Lorenzo Mantovani, Dario Piga, Simone Formentin, Matteo Malosio
Black-box optimization refers to the optimization problem whose objective function and/or constraint sets are either unknown, inaccessible, or non-existent.
1 code implementation • 1 Feb 2023 • Alessio Benavoli, Dario Azzimonti, Dario Piga
We propose a Gaussian Process model to learn choice functions from choice-data.
no code implementations • 4 Oct 2022 • Manas Mejari, Dario Piga
The key idea for direct CT identification is based on an integral architecture consisting of an LSS model followed by an integral block.
5 code implementations • 26 Jun 2022 • Marco Forgione, Manas Mejari, Dario Piga
In recent years, several algorithms for system identification with neural state-space models have been introduced.
1 code implementation • 21 Jan 2022 • Marco Forgione, Aneri Muni, Dario Piga, Marco Gallieri
The system dynamics are then assumed to change, leading to an unacceptable degradation of the nominal model performance on the perturbed system.
no code implementations • 15 Oct 2021 • Alessio Benavoli, Dario Azzimonti, Dario Piga
In this work we introduce a new framework for multi-objective Bayesian optimisation where the multi-objective functions can only be accessed via choice judgements, such as ``I pick options A, B, C among this set of five options A, B, C, D, E''.
no code implementations • 24 Jul 2021 • Umberto Michelucci, Michela Sperti, Dario Piga, Francesca Venturini, Marco A. Deriu
This paper presents the intrinsic limit determination algorithm (ILD Algorithm), a novel technique to determine the best possible performance, measured in terms of the AUC (area under the ROC curve) and accuracy, that can be obtained from a specific dataset in a binary classification problem with categorical features {\sl regardless} of the model used.
1 code implementation • 20 Apr 2021 • Dario Piga, Marco Forgione, Manas Mejari
The dynamical operator is included as {the} last layer of a neural network in order to obtain the optimal one-step-ahead prediction error.
1 code implementation • 12 Dec 2020 • Alessio Benavoli, Dario Azzimonti, Dario Piga
In a recent contribution we showed that SkewGP and probit likelihood are conjugate, which allows us to compute the exact posterior for non-parametric binary classification and preference learning.
no code implementations • 21 Sep 2020 • Ankit Gupta, Manas Mejari, Paolo Falcone, Dario Piga
This paper presents an iterative algorithm to compute a Robust Control Invariant (RCI) set, along with an invariance-inducing control law, for Linear Parameter-Varying (LPV) systems.
no code implementations • 15 Aug 2020 • Alessio Benavoli, Dario Azzimonti, Dario Piga
In this paper, we prove that the true posterior distribution of the preference function is a Skew Gaussian Process (SkewGP), with highly skewed pairwise marginals and, thus, show that Laplace's method usually provides a very poor approximation.
1 code implementation • 3 Jun 2020 • Marco Forgione, Dario Piga
This paper presents tailor-made neural model structures and two custom fitting criteria for learning dynamical systems.
3 code implementations • 3 Jun 2020 • Marco Forgione, Dario Piga
This paper introduces a network architecture, called dynoNet, utilizing linear dynamical operators as elementary building blocks.
1 code implementation • 26 May 2020 • Alessio Benavoli, Dario Azzimonti, Dario Piga
In spite of their success, GPs have limited use in some applications, for example, in some cases a symmetric distribution with respect to its mean is an unreasonable model.
1 code implementation • 29 Nov 2019 • Marco Forgione, Dario Piga, Alberto Bemporad
Model Predictive Control (MPC) is a powerful and flexible design tool of high-performance controllers for physical systems in the presence of input and output constraints.
Systems and Control Systems and Control Optimization and Control
1 code implementation • 29 Nov 2019 • Marco Forgione, Dario Piga
This paper focuses on the identification of dynamical systems with tailor-made model structures, where neural networks are used to approximate uncertain components and domain knowledge is retained, if available.
no code implementations • 28 Sep 2019 • Alberto Bemporad, Dario Piga
The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning a surrogate of the latent (unknown and perhaps unquantifiable) objective function from past sampled decision vectors and pairwise preferences.