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Curriculum(s) for 2024 - Artificial Intelligence and Robotics (30431)

Single curriculum

1st year

LessonSemesterCFULanguage
10600392 | Artificial Intelligence1st6ENG

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.
1022858 | MACHINE LEARNING1st6ENG

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.
1023235 | Robotics I1st6ENG

Educational objectives

General objectives.
The course provides basic tools for the control of robotic systems: kinematic analysis, trajectory planning, 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.

AAF2161 | Robot programming1st3ENG

Educational objectives

General Objectives
The course provides the basic tools for natively programming robotic systems, under limiter computational resources. We will illustrate the basics of C++ and the ROS middleware.a di middleware robotico ROS.

Specific Objectives
The student will be able to build a robot software, by either assembling existing components and by deveoping own ones.

Knowledge and Understanding
The student will be acquainterd wit several robotic applications. The student will understand the structure and the interfaces between the modules

Apply Knowledge and Understanding
The student will be able to decompose a complex application and approach specific problems by developing software.

Critical and Judgement Skills:
The student will be able to evaluate the performance and the efficiency of proposed solutions for robotic software.

Communication Skills
The student will be able to illustrate an application, highlight the maing problems and to illustrate the technical solutions used to approach them

Learning Skills:
The course aims at making the student autonomous when it comes to learning. To this extent we will present several examples of live programming with the task of demonstrating how to deal with compilers, simulators. Special care is taken in developing methodologies to highlight design/algorithmic issues as soon as possible during development.
1052229 | Computer Vision2nd6ENG

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.
1021883 | ROBOTICS II2nd6ENG

Educational objectives

General objectives.
The course provides advanced tools for the control of robotic systems: use of kinematic redundancy, dynamic modeling of robot manipulators, 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.

Elective course2nd6ENG

Educational objectives

Among other training activities are provided 12 credits are chosen by the student.
THREE-DIMENSIONAL MODELING
THREE-DIMENSIONAL MODELING

2nd year

LessonSemesterCFULanguage
1022775 | AUTONOMOUS AND MOBILE ROBOTICS1st6ENG

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.
Elective course1st6ENG

Educational objectives

Among other training activities are provided 12 credits are chosen by the student.
AAF1790 | SEMINARS IN ARTIFICIAL INTELLIGENCE AND ROBOTICS2nd3ENG

Educational objectives

The couse offers the opportunity to increase their knowledge through a series of seminars held by researchers and experts in Artificial Intelligence and Robotics.
In addition, the course requires the study of research papers on the most recent developments in the field and their presentation by the students in a seminar format.
AAF1028 | Final exam2nd30ENG

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.
THREE-DIMENSIONAL MODELING
THREE-DIMENSIONAL MODELING

Optional groups

The student must acquire 18 CFU from the following exams
LessonYearSemesterCFULanguage
1027171 | NETWORK INFRASTRUCTURES1st1st6ENG

Educational objectives

General objectives:
The course is intended as a broad overview to neural networks, as used today in a number of applicative fields. It provides a strong theoretical and practical understanding of how neural networks and modern deep networks are designed and implemented, highlighting the most common components, ideas, and current limitations.

Specific objectives:
From a theoretical point of view, we will review the general paradigm of building differentiable models that can be optimized end-to-end with gradient descent from data. We will then overview essential components to design architectures able to work on images (convolutive layers), sequences (recurrent layers), and sets (transformer layers). The last part of the course will then focus on a selection of important research topics, including graph neural networks, continual learning, and generative models.

Knowledge and understanding:
At the end of the course, the student will have a broad understanding of how deep networks work in practice, with the capability of implementing new components from scratch, re-using existing models, or designing new architectures for problems beyond the overview of the course.

Critical and judgment skills:
The student is expected to be able to analyze a new problem requiring machine learning, and design the appropriate neural network based solution to tackle it, understanding both its strengths and its drawbacks.

Communication skills:
The course will foster communication skills in terms of being able to describe (in both a technical and non-technical way) the mathematics underlying the models, as long as writing clear and understandable code for its implementation.

Learning ability:
Beyond the topics of the course, the student will be able to autonomously study new topics on the research frontier, and navigate the current scientific literature and software panorama.
10606827 | Reinforcement Learning1st1st6ENG

Educational objectives

General Objectives.
The Reinforcement Learning (RL) course aims to introduce students to fundamental and advanced techniques of RL, a significant area within artificial intelligence and machine learning. Students will gain skills to design and implement algorithms that enable systems to learn and improve autonomously through experience, optimizing their decisions in real-time.

Specific Objectives.
Students will explore key concepts of RL such as decision policies, Markov Decision Processes, Q-learning, and deep reinforcement learning. They will learn to:
Model complex problems using the RL approach.
Develop and implement algorithms like Q-learning and Deep Q-Networks (DQN).
Apply RL techniques in real-world scenarios like robotics, gaming, etc.

Knowledge and Understanding:
In-depth knowledge of basic and advanced RL algorithms.
Understanding of reward-based learning models and their practical applications.
Ability to interpret the results of RL algorithms and evaluate their effectiveness in various contexts.

Applying Knowledge and Understanding:
Use software frameworks like TensorFlow or PyTorch to implement and test RL algorithms.
Analyze current research case studies and projects to understand real-world RL applications.
Develop functional prototypes using RL to solve specific problems.

Autonomy of Judgment:
Students will develop the ability to critically assess RL algorithms, considering their applicability, efficiency, and potential biases. They will also be able to select the most appropriate algorithm for a given problem.

Communication Skills:
Students will learn to effectively communicate RL concepts, algorithm design decisions, and outcomes to both technical and non-technical audiences using a variety of communication media.

Next Study Abilities:
This course will prepare students to pursue advanced studies and research in RL, providing the necessary foundation to tackle open problems and innovate in the field. Students will be encouraged to actively contribute to the scientific community through publications, conferences, and collaborations.
1052218 | Probabilistic Robotics1st1st6ENG

Educational objectives

General Objectives:
Acquiring knowledge on the basic tools for probabilistic state estimation in robotics.
Being able to apply these tools to real study cases and to implement working solutions.
Evaluate the quality of a state estimator.

Specific Objectives:

Knowledge and Understanding:
- how to manipulate probability distributions, in particular Gaussians
- the basics of filtering (hisrogram filters, Gaussian filters, particle filters)
- the generic model for a stationary non-linear or linear
- Dense and Sparse formulation of minimization algorithms (Gauss-Newton, Levenberg Marquardt)
- The problem of Data Association, and typical tools to approach it (RANSAC, Heuristics)
- Typical study cases of estimation problems in robotics (Calibration, Localization, Mapping and SLAM)

Applying Knowledge and Understanding:
- Being able to model a problem and to adapt the tools to its solution.
- Develop a functioning estimator.

Making Judgements:
- Being able to analyze the pros and contra of different solutions to the same problem.
- Spot the tools applicable to solve all subtasks in the design of an estimator.
These abilities are supported by the Project to be developed as a part of the exam.
The course interleaves theory and practice. During the practicals the students are asked to
complete code snippets provided by the teacher and to run their programs on real study cases.

Communication Skills:
- Acquire a common language to describe estimators and a development methodology
that supports interaction between developers by defining a standard set of goals.

Learning Skills:
The student will possess the abilities and the skills to approach general estimation problems.
The examples in the domain of navigation provided during the course serve as study cases.
The indivudal topics learned (Gaussian Manipulation, Filtering Designs, Minimization)
are useful instruments to approach a far more general class of problems
1022870 | NEURAL NETWORKS1st2nd6ENG

Educational objectives

General objectives:
The course is intended as a broad overview to neural networks, as used today in a number of applicative fields. It provides a strong theoretical and practical understanding of how neural networks and modern deep networks are designed and implemented, highlighting the most common components, ideas, and current limitations.

Specific objectives:
From a theoretical point of view, we will review the general paradigm of building differentiable models that can be optimized end-to-end with gradient descent from data. We will then overview essential components to design architectures able to work on images (convolutive layers), sequences (recurrent layers), and sets (transformer layers). The last part of the course will then focus on a selection of important research topics, including graph neural networks, continual learning, and generative models.

Knowledge and understanding:
At the end of the course, the student will have a broad understanding of how deep networks work in practice, with the capability of implementing new components from scratch, re-using existing models, or designing new architectures for problems beyond the overview of the course.

Critical and judgment skills:
The student is expected to be able to analyze a new problem requiring machine learning, and design the appropriate neural network based solution to tackle it, understanding both its strengths and its drawbacks.

Communication skills:
The course will foster communication skills in terms of being able to describe (in both a technical and non-technical way) the mathematics underlying the models, as long as writing clear and understandable code for its implementation.

Learning ability:
Beyond the topics of the course, the student will be able to autonomously study new topics on the research frontier, and navigate the current scientific literature and software panorama.
1022863 | MEDICAL ROBOTICS1st2nd6ENG

Educational objectives

Introduction to the basic robotic technologies in the medical context, with particular emphasis on surgical robotics.

Expected learning results: Knowledge of the main robotic surgical systems, of the challenges and methodologies of medical robot design and control.
Expected competence in:

- critically reading a scientific paper describing medical robotics technologies;
- discussing in detail the state of the art of robotic applications in medicine;
- estimating potential benefits deriving from the introduction of robotic technologies in a medical procedure;
- arguing the development of a particular technology not yet available or experimentally validated;
- communicating and collaborating with people with different technical background;
- evaluating clinical, social and economical constraints in implementing a robotic technology in a medical context;
- design control scheme for teleoperation of medical robots and for shared execution of surgical tasks between humans and robots.
10592834 | Neuroengineering1st2nd6ENG

Educational objectives

* General objectives
The course aims to introduce the principles, methodologies, and applications of the main engineering techniques used to study and interact with neural systems.

* Specific objectives

- Knowledge and understanding
Students will learn the basics of the human brain functioning and organization at different scales, and to the main applications of engineering and information technologies to neuroscience

- Applying knowledge and understanding
Students will familiarize with basic tools to utilize to acquire, process and decode neurophysiological and muscular signals and to interface them with artificial devices

- Critical and judgment skills
Students will learn how to choose the most suitable control methodology for a specific problem and to evaluate the complexity of the proposed solution.

- Communication skills
Students will learn to communicate in a multidisciplinary context the main issues of interfacing neurophysiological signals with artificial systems, and to convey possible design choices for this purpose.

- Learning ability
Students will develop a mindset oriented to independent learning of advanced concepts not covered in the course.
10606869 | Multilingual natural language processing1st2nd6ENG

Educational objectives

General Objectives
The goal of the course is to provide an overview of state-of-the-art natural language processing techniques and their applications.

Specific Objectives
Students will learn the principles of automatic language processing, understanding how machines can interpret, generate and respond to human language. This includes topics such as word representation, word and sense embeddings, neural architectures for NLP, machine translation, and more general text generation.

Knowledge and Understanding
-) Knowledge of neural network architectures, such as recurrent neural networks and Transformers, used for natural language processing.
-) Knowledge of supervised and unsupervised learning methods in NLP.-) Knowledge of lexical and phrasal computational semantics techniques.
-) Understanding of language models for interpreting and generating text.

Applying knowledge and understanding:
-) How to develop models for understanding language
-) How to develop models for generating language
-) How to use neural architectures for NLPAutonomy of Judgment.

Autonomy of Judgment
Students will be able to evaluate the effectiveness of NLP techniques in different applications.

Communication Skills
Students will be able to explain the principles and techniques of natural language processing.

Next Study Abilities
Students interested in research will discover what are the main open challenges in the area of NLP, obtaining the necessary foundation for more in-depth studies in the field.
1052222 | Planning and Reasoning2nd1st6ENG

Educational objectives

This course introduces the main ideas of automated planning and mechanism for
formal logic reasoning within the field of artificial intelligence. The aim of
the sources is to prepare the student so that they can use the existing systems
for automated planning and understand their inner workings, which is
fundamental to adapt them to cope with issues arising from specific problems.
Furthermore, the student will understand the theoretical bases of the uses of
formal logics in artificial intelligence.
10607048 | Research topics in Artificial Intelligence 2nd2nd6ENG

Educational objectives

General objectives.

The aim of the course is to provide an overview to specific research topics. The topics are presented by active researchers in order to present the student with research problems and relevant and recent application themes in Artificial Intelligence. To this end, the courses include both the presentation and discussion of scientific articles, and an advanced project work.

The learning objective of the course is to provide the knowledge needed to undertake research work in these fields using practical tools for experimental validation.

Specific objectives.

Knowledge and understanding:
The topics are covered by researchers active in the field and with the aim of introducing the student to research problems and recent and relevant applications in Artificial Intelligence and Robotics.

Applied knowledge and understanding:
The course provides the knowledge necessary to undertake research work in these fields using practical tools for experimental validation.

Critical and judgment skills:
The course proposes advanced methods to study, understand and apply results reported on scientific articles, and integrate these results to create innovative Artificial Intelligence applications. The student learns how to use results from the literature as a basis for new research.

Communication skills:
Group activities in the classroom and the need to make presentations to the class allow the student to develop the ability to communicate and share the knowledge acquired and to compare herself with others on the topics of the course.

Learning ability:
In addition to the classic learning skills provided by the theoretical study of the teaching material, the course develops methods stimulate the student to deepen his knowledge of some of the topics she presents to the course and to the work group. Furthemroe the course stimulates the student to effectively apply both the concepts and the techniques learned during the course.
The student must acquire 6 CFU from the following exams
LessonYearSemesterCFULanguage
10600428 | Deep Learning1st2nd6ENG

Educational objectives

General Objectives
Upon completion of the course, students will have a solid understanding and practical skills in the field of Deep Learning, essential for addressing and solving complex artificial intelligence problems.

Specific Objectives
Knowledge and Understanding
Gain an in-depth understanding of supervised and unsupervised learning principles.
Learn about the structures and mechanisms of neural networks, both shallow and deep.
Critical Thinking and Judgment
Critically evaluate the performance of deep learning models, integrating techniques for regularization and compression.
Analyze challenges related to noise robustness in deep learning models and develop effective solutions.
Communication Skills
Present and discuss the outcomes of deep learning projects, demonstrating proficiency in using advanced tools such as Pytorch and HuggingFace.
Learning Abilities
Experiment with emerging technologies in the field of deep learning, such as CNNs, Resnets, Transformers, geometric and equivariant neural network models, as well as self-directed learning and meta-learning approaches.
Apply theoretical knowledge in practical projects to tackle real-world problems.
1044398 | INTERACTIVE GRAPHICS1st2nd6ENG

Educational objectives

Knowledge and understanding:

Have the student acquire the basics of 3D graphic programming with particular emphasis on animation and interactive visualization techniques. In particular the topics covered include: Fundamentals of computer graphics, interactive rendering and animation, graphics pipeline, transformations, visualizations, rasterization, lighting and shading, texture-mapping, animation techniques based on keyframes, physical simulations, particle systems and animation of characters. An introduction to computing on specialized graphics hardware (GPGU) will also be provided.

Applying knowledge and understanding:

To make the student familiar with the mathematical techniques underlying 3D graphics, as well as the ability to program complex and interactive environments in 3D graphics using the OpenGL library or one of its variants

Making judgements:

Deep understanding of the operation of a 3D graphics system in its hardware and software components. Knowledge of the HTML5 standard and the Javascript language, application of the WebGL library and some higher level libraries. Understanding of the problems of efficiency and visual quality of 3D graphics applications

Communication skills:

Development of interactive applications on the web in 3D graphics.

Learning skills:

Ability to understand the technical complexities in the realization of interactive applications in 3D graphics. Ability to critically analyze the solutions on the market and analyze strengths and weaknesses.
The student must acquire 12 CFU from the following exams
LessonYearSemesterCFULanguage
1056413 | Elective in Artificial Intelligence2nd1st12ENG

Educational objectives

General objectives:

The aim of the course, which is the most advanced within the Master's Degree in Artificial Intelligence and Robotics, is to provide an overview to the following research topics: learning methods in computational vision, model recognition, human-robot interaction and cognitive robotics.

The topics are presented by active researchers in these fields in order to present the student with research problems and relevant and recent application themes in Artificial Intelligence and Robotics. To this end, the courses include both the presentation and discussion of scientific articles, and an advanced project work.

The learning objective of the course is to provide the knowledge needed to undertake research work in these fields using practical tools for experimental validation.

Specific objectives:

Knowledge and understanding:
The course is the most advanced in the Master for Artificial Intelligence and Robotics and offers an overview of different research topics, such as: learning methods in computational vision, pattern recognition, person-robot interaction, and automatic reasoning in robots.

The topics are covered by researchers active in the field and with the aim of introducing the student to research problems and recent and relevant applications in Artificial Intelligence and Robotics.

Applied knowledge and understanding:
The course provides the knowledge necessary to undertake research work in these fields using practical tools for experimental validation.

Critical and judgment skills:
The course proposes advanced methods to study, understand and apply results reported on scientific articles, and integrate these results to create innovative Artificial Intelligence applications. The student learns how to use results from the literature as a basis for new research.

Communication skills:
Group activities in the classroom and the need to make presentations to the class allow the student to develop the ability to communicate and share the knowledge acquired and to compare herself with others on the topics of the course.

Learning ability:
In addition to the classic learning skills provided by the theoretical study of the teaching material, the course develops methods stimulate the student to deepen his knowledge of some of the topics she presents to the course and to the work group. Furthemroe the course stimulates the student to effectively apply both the concepts and the techniques learned during the course.
Elective in Artificial Intelligence II2nd1st6ENG

Educational objectives

General objectives:

The aim of the course, which is the most advanced within the Master's Degree in Artificial Intelligence and Robotics, is to provide an overview to the following research topics: learning methods in computational vision, model recognition, human-robot interaction and cognitive robotics.

The topics are presented by active researchers in these fields in order to present the student with research problems and relevant and recent application themes in Artificial Intelligence and Robotics. To this end, the courses include both the presentation and discussion of scientific articles, and an advanced project work.

The learning objective of the course is to provide the knowledge needed to undertake research work in these fields using practical tools for experimental validation.

Specific objectives:

Knowledge and understanding:
The course is the most advanced in the Master for Artificial Intelligence and Robotics and offers an overview of different research topics, such as: learning methods in computational vision, pattern recognition, person-robot interaction, and automatic reasoning in robots.

The topics are covered by researchers active in the field and with the aim of introducing the student to research problems and recent and relevant applications in Artificial Intelligence and Robotics.

Applied knowledge and understanding:
The course provides the knowledge necessary to undertake research work in these fields using practical tools for experimental validation.

Critical and judgment skills:
The course proposes advanced methods to study, understand and apply results reported on scientific articles, and integrate these results to create innovative Artificial Intelligence applications. The student learns how to use results from the literature as a basis for new research.

Communication skills:
Group activities in the classroom and the need to make presentations to the class allow the student to develop the ability to communicate and share the knowledge acquired and to compare herself with others on the topics of the course.

Learning ability:
In addition to the classic learning skills provided by the theoretical study of the teaching material, the course develops methods stimulate the student to deepen his knowledge of some of the topics she presents to the course and to the work group. Furthemroe the course stimulates the student to effectively apply both the concepts and the techniques learned during the course.
1056414 | Elective in Robotics2nd1st12ENG

Educational objectives

​General objectives

The course presents a selection of advanced topics in Robotics and is intended as an introduction to research.
Guided through case studies taken from the research activities of the teachers, the student will be able to fully develop a problem in Robotics, from its analysis to the proposal of solution methods and their implementation.

Specific objectives

Knowledge and understanding:
Students will learn some advanced control techniques used in some robotic research areas where the lecturers are active.

Apply knowledge and understanding:
Students will be able to use and design complex control systems for advanced robotic problems.

Critical and judgment skills:
Students will be able to evaluate some methodologies used in the difference robotic applied illustrated areas.

Communication skills:
The course activities will allow students to be able to communicate and share the different solutions, adopted in a research framework, for the different illustrated robotic areas.

Learning ability:
The course development aims at giving the student the capacity to design complex control systems for advanced robotic systems.
Elective in Robotics I2nd1st6ENG

Educational objectives

​General objectives

The course presents a selection of advanced topics in Robotics and is intended as an introduction to research.
Guided through case studies taken from the research activities of the teachers, the student will be able to fully develop a problem in Robotics, from its analysis to the proposal of solution methods and their implementation.

Specific objectives

Knowledge and understanding:
Students will learn some advanced control techniques used in some robotic research areas where the lecturers are active.

Apply knowledge and understanding:
Students will be able to use and design complex control systems for advanced robotic problems.

Critical and judgment skills:
Students will be able to evaluate some methodologies used in the difference robotic applied illustrated areas.

Communication skills:
The course activities will allow students to be able to communicate and share the different solutions, adopted in a research framework, for the different illustrated robotic areas.

Learning ability:
The course development aims at giving the student the capacity to design complex control systems for advanced robotic systems.