Educational objectives General objectives:
Acquire the basic principles of the field of Artificial Intelligence, specifically the modeling of intelligent systems through the notion of intelligent agent.
Acquire the basic techniques developed in the field of Artificial Intelligence, concerning symbol manipulation and, more speicifically, discrete models.
Acquire the basic principles of the interaction among intelligent agents and, specifically, of the interaction between intelligent agents and humans, through natural language.
Specific objectives:
Knowledge and understanding:
Automated search in the space state: general methods, heuristic driven methods, local Search. Factored representations: constraint satisfaction problems, automated planning.
Knowledge Representation through formal systems: propositional logic, first order logic, description logic (hints), non monotonic reasoning (hints). Usage of logic as a programming language: PROLOG.
Cooperation and coordination, distributed task assignment, distributed constraint optimization, lexical, syntactic and semantic analysis of natural language.
Applying knowledge and understanding:
Modeling problems by means of the manifold representation techniques acquired through the course. Analysis of the behavior of the basic algorithms for automated reasoning.
Design and implement frameworks for multi agent interaction.
Making judgements:
Being able to evaluate the quality of a representation model for a problem and the results of the application of the reasoning algorithms when run on it.
Analyse and evaluate the key elements of the interaction among multiple agents.
Communication:
The oral communication skills are stimulated through the interaction during class, while the writing skills will be developed thorugh the analysis of exercises and answers to open questions, that are included in the final test.
The communication skills are also exercised through the presentation of a group project and its associated written report.
Lifelong learning skills:
In addition to the learning capabilities arising from the study of the theoretical models presented in the course, the problem solving capabilities of the student will be improved through the exercises where the acquired knowledge is applied.
The design and implementation of a prototype system for multi agent interaction support the learning of teamwork.
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Educational objectives General Objectives:
The objectives of this course are to present a wide spectrum of Machine
Learning methods and algorithms, discuss their properties, convergence
criteria and applicability. The course will also present examples of
successful application of Machine Learning algorithms in different
application scenarios.
The main outcome of the course is the capability of the students of
solving learning problems, by a proper formulation of the problem, a
proper choice of the algorithm suitable to solve the problem and the
execution of experimental analysis to evaluate the results obtained.
Specific Objectives:
Knowledge and understanding:
Providing a wide overview of the main machine learning methods and
algorithms
for the classification, regression, unsupervised learning and
reinforcement learning problems. All the problems are formally defined
and theoretical basis as well as technical and implementation details
are provided in order to understand the proposed solutions.
Applying knowledge and understanding:
Solving specific machine learning problems starting from training data,
through a proper application of the studied methods and algorithms. The
development of two homeworks (small projects to be developed at home)
allows the students to apply the acquired knowledge.
Making judgements:
Ability of evaluating performance of a machine learning system using
proper metrics and evaluation methodologies.
Communication skills:
Ability of writing a technical report describing the results of the
homeworks, thus showing abilities in communicating results obtained from
the application of the acquired knowledge in solving a specific problem.
Being exposed to examples of communication of results obtained in
practical cases given by experts within seminars offered during the course.
Learning skills:
Self-study of specific application domains, problems and solutions
during the homeworks, with possible application of teamwork for the
solution of specific problems.
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Educational objectives General objectives
The course provides the basic tools for the kinematic analysis, trajectory planning, and programming of motion tasks for robot manipulators in industrial and service environments.
Specific objectives
Knowledge and understanding:
Students will learn how actuation units and sensing components of robots operate, the basic methods for the kinematic modeling, analysis and control of robot manipulators, as well as the main algorithms for trajectory planning.
Apply knowledge and understanding:
Students will be able to analyze the kinematic structures of industrial robots and to design algorithms and modules for planning and controlling robot trajectories.
Critical and judgment skills:
Students will be able to characterize the functionality of a robotic system with reference to a given industrial or service task, analyzing the complexity of the solution, its performance, and the possible weaknesses.
Communication skills:
The course will allow students to be able to present the main problems and the technical solutions related to the use and application of robotic systems.
Learning ability:
The course aims at developing autonomous learning abilities in the students, oriented to the analysis and solution of problems in the use of robots.
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Educational objectives GENERAL OBJECTIVES
The course aims to introduce the student to the fundamental concepts of artificial vision and to the construction of autonomous systems of interpretation and reconstruction of a scene through images and video. The course deals with basic elements of projective and epipolar geometry, methods for 3D vision and vision based on multiple views, and methods for metric reconstruction and image and video interpretation methods. Furthermore the course illustrates the main techniques for the recognition and segmentation of images and videos based on machine learning.
SPECIFIC OBJECTIVES
Knowledge and Understanding
The course stimulates students' curiosity towards new methodologies for the analysis and generation of images
and video. The student learns new concepts that allow him to acquire a basic knowledge of
computational vision.
Apply Knowledge and Understanding
Students deepen and learn programming languages ??to apply the acquired knowledge.
In particular they deepen the Python language and learn Tensorflow. The latter offers students
the possibility of programming deep learning applications. They use this brand new technology to make
a project to recognize specific elements in images and videos.
Critical and Judgment skills
The student acquires the ability to distinguish between what he can achieve with the tools he/she has learned,
such as generating images or recognizing objects using deep learning techniques,
and what is actually required for the realization of an automatic vision system.
In this way she/he is able to elaborate a critical judgment on the vision systems available to the state
of art and to assess what can actually be achieved and what requires further progress
in research.
Communication skills
The realization of the project, as part of the exam program, requires the student to work and give a
contribution within a small work group. This together with the solution of exercises in the classroom,
and to the discussions on the most interesting topics it stimulates the student's communication skills.
Learning ability
In addition to the classic learning skills provided by the theoretical study of the teaching material,
the course development methods, in particular the project activities, stimulate the student
to the self-study of some topics presented in the course, to group work, and to the application
concrete knowledge and techniques learned during the course.
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Educational objectives General objectives
The course provides tools for the dynamic modeling of robot manipulators, the use of kinematic redundancy, the design of feedback control laws for free motion and interaction tasks, including visual servoing.
Specific objectives
Knowledge and understanding:
Students will learn the methods for the dynamic modelling of manipulators, for the use of kinematic redundancy, as well as how control laws can be designed to execute robotic tasks in free motion or involving interaction with the environment.
Apply knowledge and understanding:
Students will be able to analyze the robot dynamics and to design algorithms and modules for controlling robot trajectories and contact forces with the environment.
Critical and judgment skills:
Students will be able to characterize the dynamic functionality of a robotic system with reference to a given task, analyzing the complexity of the solution, its performance, and the possible weaknesses.
Communication skills:
The course will allow students to be able to present the advanced problems and related technical solutions when using robots in dynamic conditions.
Learning ability:
The course aims at developing autonomous learning abilities in the students, oriented to the analysis and solution of advanced problems in the use of robots.
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Educational objectives Among other training activities are provided 12 credits are chosen by the student.
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Educational objectives General objectives
The course presents the basic methods for achieving mobility and autonomy in robots.
Specific objectives
Knowledge and understanding:
Students will learn (1) basic methods for the modeling, analysis and control of mobile robots, both wheeled and legged, and (2) fundamental algorithms for autonomous motion planning.
Apply knowledge and understanding:
Students will be able to analyze and design architectures, algorithms and modules for planning, control and localization of autonomous mobile robots.
Critical and judgment skills:
Students will be able to choose the most suitable functional control architecture for a specific robotic system and to analyze its complexity as well as possible weaknesses.
Communication skills:
The course activities will allow students to be able to communicate/share the main problems concerning autonomous mobile robots, as well as the possible design choices for the control of such systems.
Learning ability:
The course development aim at giving the student a mindset oriented to the development of modules for the autonomous mobility of robots.
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Educational objectives Among other training activities are provided 12 credits are chosen by the student.
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Educational objectives Seminar series title: "Computer Vision for Intelligent Robotics"
The objective of this course is to provide an overview of the recent trends in computer vision applied to autonomous and industrial robotics. Students will acquire basic and specific knowledge related to the addressed topics. Moreover, they will gain experience in presenting and discussing scientific papers.
After an introduction into the presented topics, two or three research papers for each lecture will be presented and discussed by the students. Topics addressed in the lectures include low-level vision, 3D reconstruction from images, vision based ego-motion estimation, visual servoing, object detection and localization and semantic scene segmentation.
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Educational objectives The student will present and discuss the results of a technical activity, producing a written thesis supervised by a professor and showing the ability to master the methodologies of Computer Science Engineering and/or their application.
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