MSc in Management Engineering
Prof.
Paolo Rocco, Prof. Jessica Leoni
TH 14:15-18:15 room BL27.06
Artificial intelligence (AI) is
a combination of methods, techniques, and tools, that are revolutionizing
today's life. At the core of AI is machine learning, i.e. the elaboration of
data that allows systems to learn, recognize properties in data, improve
performance, without being explicitly programmed. On the other hand, robotics
is a well-established technology that is making a significant shift towards
broader application thanks to the use of AI.
The purpose of this course is to familiarize the students with the most common
tools of machine learning and expose them to both basic elements of robotics
and new trends (like collaborative and humanoid robotics), with special
emphasis on the application of AI to robotics.
Course syllabus is as follows:
1. Introduction to
Artificial Intelligence and Machine Learning
Learning from
data.
AI and Machine
Learning applications in engineering
2. Machine learning for
atomic data
Data quality
and quantity.
Univariate and
multivariate visualization techniques.
Data
normalization and pre-processing.
Features
extraction and selection, learning paradigms, performance assessment,
interpretability for atomic data.
3. Machine learning for time
series
Time series:
filtering, detrend, feature extraction and selection.
Learning
paradigms, performance assessment, interpretability for time series data.
4. Introduction to robotics
Industrial
robots: basic concepts and examples.
Market and
trends of industrial robotics.
5. Robot kinematics
Position and
orientation of a rigid body.
Direct and
inverse kinematics.
Differential
kinematics: Jacobian and singularities.
6. Robot planning and control
Robot
programming.
Trajectory
generation in joint space and in operational space.
Closed loop
control (elements).
7. Collaborative and humanoid robotics
Collaborative
robotics: motivation and use cases.
Safety
standards.
Humanoid
robots: a general introduction.
Prerequisites
Basics
in linear algebra and calculus.
Bibliography
Lecture
notes
B. Siciliano,
L. Villani, G. Oriolo, A. De Luca: Foundations
of Robotics., Springer, 2025 (in English)
Lecture
notes are available in the WeBeep
channel for students enrolled in the course.
Students will take a written examination, consisting of open-ended questions. An optional project work on the AI part of the course will be proposed as well.
Texts of exams will be published here.
Results of the exams will be notified to the students through the online services.
For the robotics part, students may also want to look at the exams of the previously offered course Industrial automation and robotics