Autonomous Robots and Agents
A.Y. 2025/2026
Learning objectives
The course aims to introduce Autonomous Robotics, with a particular focus on the synergies between this and the Multiagent Systems area. The goal is to equip students with the theoretical and practical knowledge necessary to design and develop the computational modules of robots and autonomous agents in a variety of application domains. Building on the foundations of the mathematical and computational models of the most widely used robotic systems, such as mobile robots or manipulators, we will explore the techniques that today and, in the future, will play a key role in equipping systems composed by one or more robots with the ability to plan and act autonomously in their environment.
Expected learning outcomes
The student will gain an understanding, from a computer science perspective, of the fundamental concepts underlying autonomous robotics and multi-agent systems and how to apply them to a selection of relevant problems. On a more practical side, the student will learn to develop and experiment with autonomous robotic platforms in simulation and/or reality.
Lesson period: Third four month period
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Third four month period
Course syllabus
Introduction (2h)
- Types of robots: perception, world modeling, actuation.
- Robots as agents: overview of applications and open challenges.
Methods (16h)
- Mathematics for robotics: review of linear algebra, probabilistic methods and optimization.
- Filtering and recursive state estimation.
- Sequential decision making with uncertainty and dynamic environments (MDP, POMDP).
Problems (20h)
- Kinematics for mobile robots.
- Mapping and localization.
- Autonomous navigation and planning.
- Multiagent robotic systems: coordination and cooperation.
Tools (10h)
- Hands-on lectures on robotic architectures and ROS, the operating system for robots.
- Types of robots: perception, world modeling, actuation.
- Robots as agents: overview of applications and open challenges.
Methods (16h)
- Mathematics for robotics: review of linear algebra, probabilistic methods and optimization.
- Filtering and recursive state estimation.
- Sequential decision making with uncertainty and dynamic environments (MDP, POMDP).
Problems (20h)
- Kinematics for mobile robots.
- Mapping and localization.
- Autonomous navigation and planning.
- Multiagent robotic systems: coordination and cooperation.
Tools (10h)
- Hands-on lectures on robotic architectures and ROS, the operating system for robots.
Prerequisites for admission
The course does not require any prior knowledge of the subject. However, a basic understanding of linear algebra, algorithms, and probability and statistics is recommended for better and easier comprehension of the topics covered. Successfully attending the courses in Discrete Mathematics, Algorithms and Data Structures, and Statistics and Data Analysis is more than sufficient. Attending the Artificial Intelligence I course may also be helpful.
Teaching methods
The theory part is given with frontal lectures where slides are presented. Slides are made available in PDF format through the myAriel platform. Attendance is very much recommended.
Teaching Resources
Sebastian Thrun, Wolfram Burgard, and Dieter Fox, Probabilistic Robotics, The MIT Press (Intelligent Robotics and Autonomous Agents Series), 2005
For specific insights into mobile robotics:
Siegwart, Illah Reza Nourbakhsh, and Davide Scaramuzza, Introduction to Autonomous Mobile Robots - Second Edition, The MIT Press (Intelligent Robotics and Autonomous Agents Series), 2011
For some basic concepts and further insights on autonomous agents and multi-agent systems:
Peter Norvig and Stuart J. Russell, Artificial Intelligence: A Modern Approach - Fourth Edition, Pearson Education, 2021
Additional support materials, including slides and other resources, will be provided via the myAriel platform throughout the course.
For specific insights into mobile robotics:
Siegwart, Illah Reza Nourbakhsh, and Davide Scaramuzza, Introduction to Autonomous Mobile Robots - Second Edition, The MIT Press (Intelligent Robotics and Autonomous Agents Series), 2011
For some basic concepts and further insights on autonomous agents and multi-agent systems:
Peter Norvig and Stuart J. Russell, Artificial Intelligence: A Modern Approach - Fourth Edition, Pearson Education, 2021
Additional support materials, including slides and other resources, will be provided via the myAriel platform throughout the course.
Assessment methods and Criteria
The exam consists of a written test lasting at most 3 hours where exercises and open questions with short answers are proposed.
The exercises require the application of the techniques discussed in class to problems of complexity appropriate to the duration of the test. Open questions assess knowledge of basic concepts and how they can be applied to problem-solving in the real world.
During the test, it is not allowed to consult any material.
The vote is out of thirty and will be communicated through the myAriel platform.
The assessments will take into account the mastery of techniques, correctness, and elegance of the solutions, clarity of presentation, knowledge of the concepts, and the ability to apply them in new settings. The exam and its evaluation will not be differentiated based on frequency.
The exercises require the application of the techniques discussed in class to problems of complexity appropriate to the duration of the test. Open questions assess knowledge of basic concepts and how they can be applied to problem-solving in the real world.
During the test, it is not allowed to consult any material.
The vote is out of thirty and will be communicated through the myAriel platform.
The assessments will take into account the mastery of techniques, correctness, and elegance of the solutions, clarity of presentation, knowledge of the concepts, and the ability to apply them in new settings. The exam and its evaluation will not be differentiated based on frequency.
Professor(s)