The course gives 12 credits within the
Master in Artificial Intelligence and Robotics
It is structured in the three modules described in the right frame. Please refer to each of the modules, following the links in this page for additional details.
Professor in charge of exam registration is Prof. Luca Iocchi.
IMPORTANT Any request for change of a module must be authorized by the professor in charge for the A.Y. (see above) before attending the class and giving the exam of the module involved in the change.
An e-mail should be sent to ask for a change (including detailed motivations for the change). Notification about acceptance/rejection of the change will be communicated soon after receiving the e-mail.
VERY IMPORTANT Since January 2018, exams including unauthorized changes of the modules will not be registered. If you are unsure about your status, please contact the professor in charge for the A.Y. (see above) as soon as possible.
All the lectures will be given in the second semester.
Additional information are provided in the web sites of the modules.
Each module has specific modalities and dates.
Please check the module web sites.
After completing all the modules, contact the Professor in charge of the registration providing the following information: name, matricola, list of given exams (title of the module, professor of the module, and grade).
Total grade for 12 CFU is computed as follows:
- 30 with Laude in each module is counted as 31
- all the votes are weighted averaged
- the final grade is rounded to the closest integer (0.5 is rounded to the highest value, e.g. 29.5 -> 30)
- Laude is granted with weighted average >= 30.5 (e.g., 30 L x 6 CFU + 30 x 6 CFU -> 30 L)
Prof. Giuseppe De Giacomo
3 CFU - 2nd Semester
The development of intelligent agents (e.g. robots) that deliberate their course of actions is one of the most important goals of Artificial Intelligence. In this course we will present different enabling techniques to realize this goal, focussing in particular on advanced forms of automated behaviour synthesis based on temporal logics and action theories in both deterministic and non-deterministic settings.
The students are encouraged to do projects where they can integrate such high-level forms of reasoning with state-of-the-art probabilistic robotics, computer vision, and machine learning tools.
Prof. Luca Iocchi
3 CFU - 2nd Semester
Applications involving robots interacting with people are gaining increasing interest. The course will provide an overview of recent methods and techniques for Human-Robot Interaction and Social Robotics. Several interaction modalities will be discussed in the course, including: vision, speech, body motion, user interfaces, etc.
Prof. Christian Napoli
6 CFU - 2nd Semester
As computer science, assistive technologies and robotics evolve towards application fields in which humans cooperate with machines, working closer and closer, the requirements for human computer interactions increase. Visual perception is an important component for human–machine interaction processes in all kinds of computer systems. Interaction between humans and computers depends on the reliability of the perception systems, and, above all, the vision system. The analysis of activities, motions, skills, and behaviors of humans and robots are generally addressed by using the features of a moving human body (or body part). The human motion behavior is then analyzed by body movement kinematics, and the trajectory of the target is used to identify the objects and the human target. The process of human target identification and gesture recognition in a quite non-trivial problem. In this series of lectures we will focus on the context of Human-Robot Interaction (HRI) along with the related problems on the field of vision and perception, applied to robotic systems. We will devise the typical characteristics of vision and perception related hardware device, as well as the relative software systems and solutions. We will explore the known approaches characterizing well known visual recognition systems, as well as the most important algorithmic solutions for people targeting and body parts recognition. A theoretical and practical framework will be given with several example. Finally we will discuss the state of the art on human-centric vision analysis and explain the importance of the matter relatively to human-based interfaces of computer/robots with special interest in human motion and activity recognition. We will also devise several tracking systems and motion oriented context and object recognition techniques, with emphasis on deep learning techniques applied to visual recognition. Finally we will compare the applicability of such techniques to human motion classification and the related application on the field of Human-Computer Interaction.