Home Research System Identification

Machine Learning, Identification and Estimation

Faculty: A. Chiuso, A. Ferrante, M.Pavon, G. Pillonetto, G. Picci, M. Zorzi
Post-docs: -
Ph.D. students: G. Prando, D. Romeres
Description: The focus of this area is about developing mothodoligal tools for enabling artifical systems to safely and efficiently operate without or limited need of human intervention. Application areas are the most disparate, ranging from automatic speech or visual recognition systems to robots that are able to interact with an unkwnon enviroment while performing assigned tasks, to advanced process control systems (such as, e.g. in the pretrochemical industry).



Computer Vision

Computer vision is the science of retrieving information from images and movies (sequences of images indexed by time). Our goal is to develop methodologies to enable artificial systems to use visual sensors (such as cameras) to interct with the environment similarly to what us humans do. Basic tasks we have in mind are navigation in an unknown and unstructured environment, recognition and classification of objects and scenes etc.

This, which we call the computer vision problem, is a formidable inverse problem which has to cope with high dimensional data (long sequences of possibly high resolution images), hampered by nuisances (factors the images depend on but we are not interested in) and outliers (uninteresting parts of the image which not only do not carry information on our primary task but also disturb our inference problem).

We tackle the computer vision problem  through the systematic use of modeling and dynamical systems ideas, together with the most advanced tools for statistical estimation and learning. See our latest work on "Robust visual odometry'' video (supplementary material for our ICRA 2015 submission)


Machine Learning and System Identification

Machine Learning is about endowing artificial systems the ability to learn from experience. The field spans a broad range of topics and include building models from measured data as well as learning actions from "experience". When  focusing on learning dynamical models this is often refered to as System identification.

In the recent past the research has been
mainly focused on the development and analysis of so-called
``Subspace Methods'' for identification of linear MIMO systems.
Our current interest is on identification of large scale, possibly
structured, systems. To this purpose we are focusing in the
integration of classical identification methods (such as PEM)
with tecqniques developed in Statistics and Machine Learning to solve large scale, il conditioned inverse problems; in particular nonparametric methods and sparsity inducing priors are being considered, as well as with state of the art optimization algorithms.




Adaptive Optics

Adaptive optics (AO) is used in astronomy to obtain high resolution images, close to diffraction limited, of stars and galaxies with ground telescopes, otherwise blurred by atmospheric turbulence. 
In order to succesfully compensate for the distorsion induced by athmospheric turbulence one (or more) deformable mirros are deformed; the control action used measurements of the wavefront distorsion. 
Next generation AO systems have thousand of sensors and actuators and as such pose non-trivial issues both for modeling and control.
Identification, Monitoring and Control of Smart Greenhouses and Buildings
This project addresses the problem of developing identification, monitoring and control tools that can provide to improve energy efficiency in buildings and greenhouses due to the steadily increasing prices of energy resources and environmental concerns about climate changes.  In particular we are interested in a-posteriori evaluation of the
thermal efficiency of a building, i.e. energy labelling based on experimental data; in thermal monitoring and comfort control systems especially in large buildings; in energy saving quantification after remodeling and energy-specific retrofitting of existing buildings; and in automatic fault-detection, monitoring an control of Heating Ventilation and Air Conditioning (HVAC) systems. Particularly useful in this context is the adoption of Wireless Sensor and Actuators Networks (WSANs), which provide easy installation  and maintenance. The main focus of the project is on the experimental aspects of the applications and the application of advanced control tools like 
adaptive estimation and model predictive control.





FIRB PROJECT: "Learning meets time: a computational approach to learning in dynamic systems"

Project Coordinator: A. Chiuso
Local Coordinator: A. Chiuso
People:  A. ChiusoG. Pillonetto, M. Zorzi, G. Prando, D. Romeres
Partners: Universita di Genova, Universita d Ferrara, IIT, Univ. Linkoping, Univ. of Seattle
Target applications: Autonomous Robots
Related research areas: System Identification
Keywords: System Identification, Adaption, Learning, Computational methods
Funding: MIUR
Amount: 728 Keuro
Starting date: March 2013
Ending date: March 2017
Official Webpage: 

The goal of this proposal is to achieve fundamental scientific advances in the context of learning dynamical systems, which will lay the grounds for the design of autonomous and artificially intelligent systems. We will pursue this goal through a new synergic and integrative approach that will draw, and merge, concepts and methods from control theory and machine learning. Effective computational frameworks will be developed exploiting in novel ways optimization techniques for processing complex high dimensional data sets.

Our research will be ultimately aimed at the development of intelligent systems that can be applied, e.g., to a new generation of robot human helpers and co-bots in domestic environments. These new robot (hereby called ``agents’’) will have adaptive effective cognitive systems to process sensory data, such as for example skin sensors and cameras, as well as adaptive control systems to be applied to novel actuators.







PRIN: New Methods and Algorithms for Identification and Adaptive Control of Technological Systems
Funding: MIUR
Duration: 2006-2008