Carlo Alberto Avizzano

Avizzano research activities aim at introducing intelligence in automation and robotics to make human-robot interaction easier, more intuitive, and allow robots to interact with the users and the environment while showing advanced perception and cognitive capabilities. The methods of my research combine a background in control, non-linear control, robust control, and mechatronics with artificial intelligence, computer vision, machine learning, and statistics to implement autonomous systems that adapt to changing environments while preserving predictable and robust control performances.

The research goal is to create new systems that can learn from experience, model different levels of cognitive human behaviors, generalize them and reproduce these skills in unforeseen contexts through extended real-time perception and analysis capabilities that allow the robot to perceive visual, geometric, force and localization information from its own sensors network. The research explores different types of control and automation systems including traditional robots, customized automation systems, haptic interfaces for virtual and telepresence interaction, robotized simulators for different types of vehicles, autonomous vehicles (ground, air, and underwater), wearable sensors and actuators. The research copes with the design of complex and distributed computing architecture, including the electronic and mechatronics components, microcontrollers and embedded computing systems, the core real-time control kernel, and the sensor network; the development of novel methods and algorithms to model the robot behavior, to extract information from sensor flow (e.g. inertial, audio, video), and to transfer knowledge from human examples to robot memory; the application of the design results on the field and in fully operational environments. Research results have constantly been applied to real-case scenarios, including numerous industrial applications, medical and rehabilitation systems, civil, military, and social applications. As a result, they generated relevant outcomes in terms of commercial products, patents, and directly participated or promoted spin-off companies.

PhD Classes

• 2023-Current Sensors for construction (1 ECTS)
• 2021-Current Introduction to Python programming for HealthScience (1 ECTS)
• 2021-2022 Digital Perception and Deep Learning for Medicine (2 ECTS)
• 2018-Current Digital Perception & Computer Vision (4 ECTS)
• 2019-2023 Mechatronics (6 ECTS)
• 2016-2019 Design and Development of uC software (ARM32) (4 ECTS)
• 2012-2018 Lab. Of Perceptual Robotics (6 ECTS)
• 2017-2019 Lab. Of Matlab & Simulink (3 ECTS)
• 2014-2018 Introduction to Dynamic Systems (3 ECTS)
• 2012-2013 Lab. of Virtual Environments (6 ECTS)

Master and Bachelor

• 2022-Current Laboratorio di Visione e Meccatronica, Univ. Pisa (6 ECTS)
• 2011-2023 Laboratorio di Meccanica e Meccatronica, Univ. Di Pisa – (6 ECTS)
• 2019-Current Digital Perception I & II (3+3 ECTS)
• 2018-Current Introduction to Computer Vision (3 ECTS)
• 2015-2019 Digital Control Systems & Mechatronics (6 ECTS)
• 2017-2018 Frontiers in Engineering (2 ECTS)
• 2014-2015 microcontroller software development (3 ECTS)
• 2014-2015 Introduction to Perceptual Robotics (1 ECTS)
• 2013-2014 Skills and Perception (3 ECTS)

Ph.D. Boards attivi

• Dottorato Emerging Digital Technologies
• Dottorato Health Science Technology and Management
• Dottorato Monitoraggio e Protezione delle opere d’arte infrastrutturali