Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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2° Year It will be activated in the A.Y. 2027/2028
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2 modules among the following:
- 1st year - Knowledge representation, Natural Language Processing, HCI - Multimodal Systems - delivered in 2026/2027
- 2nd year - AI & cloud, Advanced programming for AI - delivered in 2027/2028
- 1st and 2nd year - Computer vision & deep learning - delivered in 2026/2027 and in 2027/20282 modules among the following (mutually exclusive with the previous ones):
- 1st year - Knowledge representation, Natural language processing, HCI - multimodal systems - delivered in 2026/2027
- 2nd year - AI & cloud, Advanced programming for AI, Visual intelligence - delivered in 2027/2028
- 1st and 2nd year - Computer Vision & deep learning, Statistical learning - delivered in 2026/2027 and in 2027/2028 Two modules among the following
A.A. 2026/2027: Complex Systems not activatedOne module among the followingLegend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
AI and Robotics (2026/2027)
Teaching code
4S012304
Credits
6
Language
English
Also offered in courses:
- Mobile robotics of the course Master's Degree in Computer Engineering for Intelligent Systems
- Mobile robotics of the course Master's Degree in Computer Engineering for Intelligent Systems
- AI and Robotics - Teoria of the course Master's degree in Artificial intelligence
- AI and Robotics - Laboratorio of the course Master's degree in Artificial intelligence
- Mobile robotics - Teoria of the course Master's Degree in Computer Engineering for Intelligent Systems
- Mobile robotics - Laboratorio of the course Master's Degree in Computer Engineering for Intelligent Systems
- Mobile robotics - Teoria of the course Master's Degree in Computer Engineering for Intelligent Systems
- Mobile robotics - Laboratorio of the course Master's Degree in Computer Engineering for Intelligent Systems
Scientific Disciplinary Sector (SSD)
IINF-05/A - Information Processing Systems
Courses Single
Authorized
The teaching is organized as follows:
Teoria
Laboratorio
Learning objectives
This course presents the main issues related to Artificial Intelligence techniques for mobile robotic platforms. The objective is to provide the students with the ability to design, apply and evaluate algorithms that allow mobile robotic platforms to interact with the surrounding environment by performing complex tasks with a high level of autonomy. At the end of the course the students must demonstrate to understand the fundamental concepts related to localization, trajectory planning, task planning, decision-making under uncertainty and machine learning in the context of mobile robotic platforms. Moreover, the students must demonstrate to be able to work with the main development tools for mobile robotic applications and to be able to define technical specifications for designing and integrating software modules for mobile robotic platforms. The students must also be able to deal with professional figures to design solutions for the high level control of mobile robotic platforms and to continue the studies independently following the technical evolution in the field of mobile robotics and developing innovative approaches to improve the state of the art.
Prerequisites and basic notions
No specific requirements.
Program
– Localization and mapping (e.g., recursive state estimation)
– Motion planning for mobile robots (e.g., path planning, obstacle avoidance);
– Decision-making under uncertainty (e.g., Markov Decision Process) .
– Reinforcement learning for mobile robotic platforms (e.g., model-based and model free approaches, Deep RL);
- Advanced methods for high level control of intelligent robots combining classical control methods with MAchine Learning bsed approaches (Deep RL combined with Control Barrier Functions and Model Predictive Control);
– Lab: implementation of autonomous behaviors for mobile robotic platforms using state of the art development toolkits (e.g., ROS2), simulation environments for empirical evaluation (e.g., Coppelia), validation on simple mobile platforms (e.g., Limo).
Bibliography
Didactic methods
Lectures in classrooms and in lab with mobile robotic platforms. The slides used during the lessons and other material (eg, access to code and mobile robotic platforms) will be provided.
Learning assessment procedures
The exam consists of an oral test focused on the laboratory activities and a second test that can be chosen between two options: i) a project focused on the implementation of some of the techniques studied during the course; ii) an oral exam focused on the topics studied during the course.
Evaluation criteria
To pass the exam, students must demonstrate:
- to have understood the principles behind programming for mobile robots
- to be able to present arguments on the topics of the course in a precise and organic way without digressions
- to know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions and projects.
Criteria for the composition of the final grade
The final mark will be obtained from the average of the marks of the two tests.
Exam language
English
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