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Master in Intelligent Robotic Systems (MIRS)

Curriculum

Check out the subjects offered

Subjects

Curriculum

Specialisation I30 credits

Name of subject Type Credits Duration Offer for 2022-2023

Manipulació RobóticaManipulación RobóticaRobot Manipulation  (3501MO3351)

OBCompulsory 6.00 SSemester long YES

Robòtica ProbabilísticaRobótica ProbabilísticaProbabilistic Robotics  (3501MO3352)

OBCompulsory 6.00 SSemester long YES

Sistemes AutònomsSistemas AutónomosAutonomous Systems  (3501MO3353)

OBCompulsory 6.00 SSemester long YES

Geometria MultivistaGeometría MultivistaMultiview Geometry  (3501MO3354)

OBCompulsory 6.00 SSemester long YES

Aprenentatge AutomàticAprendizaje AutomáticoMachine Learning  (3501MO3355)

OBCompulsory 6.00 SSemester long YES

Specialisation Extension I30 credits

Name of subject Type Credits Duration Offer for 2022-2023

Projecte de IntervencióProyecto de IntervenciónHands-on Intervention  (3501MO3356)

OBCompulsory 6.00 SSemester long YES

Projecte de localitzacióProyecto de localizaciónHands-on Localization  (3501MO3357)

OBCompulsory 6.00 SSemester long YES

Projecte de PercepcióProyecto de PercepciónHands-on Perception  (3501MO3358)

OBCompulsory 6.00 SSemester long YES

Projecte de PlanificacióProyecto de planificaciónHands-on Planning  (3501MO3359)

OBCompulsory 6.00 SSemester long YES

Gestió i emprenedoriaGestión y emprendimientoManagement & Entrepreneurship  (3501MO3360)

OBCompulsory 3.00 SSemester long YES

Escriptura científica i bones pràctiques en la investigacióEscritura científica y buenas prácticas en la investigaciónScientific Writing & Research best practices  (3501MO3361)

OBCompulsory 3.00 SSemester long YES

Specialisation II30 credits

Final Master’s Project30.00 credits

Name of subject Type Credits Duration Offer for 2022-2023

Tesi de MàsterTesis de MásterMaster Thesis  (3501MO3382)

PFFinal year project 30.00 SSemester long YES

The MIRS master is structured in 2 courses of two semesters of 30 ECTS each.

During the 1st semester courses on industrial robotics, probabilistic robotics, autonomous systems, computer vision and machine learning will be held. In the 2nd semester, students will work on 4 projects, one on each of the pillars that form the master: intervention, localization and mapping, perception and artificial intelligence. In addition to the projects, students will take cross-cutting subjects such as project management and entrepreneurship or scientific writing and best practices in research. In the third semester, the classical concepts of robotics and computer vision will be complemented by subjects such as 3D perception and object detection and segmentation. In addition, new artificial intelligence techniques based on deep learning and reinforcement learning will be addressed. Students will also take a course to understand the statistical bases as well as specific techniques that are part of the corpus of data science methodologies. In the 4th semester, students will complete a master's thesis.

The timing of the modules, together with the subjects and ECTS credits that comprise them, is shown in the following table:

M1: Specialisation I

Credits

Semester 1

ECTS

Robotic manipulation

RM

6

Probabilistic robotics

PR

6

Autonomous systems

ACE

6

Multiview geometry

MG

6

Automatic learning

ML

6

M2: Specialisation I Extension

 

Semester 2

ECTS

Hands-on Intervention project

HI

6

Hands-on Localization project

HL

6

Hands-on Perception project

HP

6

Planning project

HPl

6

Management and entrepreneurship

ME

3

Writing & Research best practices in research

WRBP

3

M3: Specialisation II

Credits

Semester 3

ECTS

Statistics for data science

SDS

6

3D perception and sensory fusion

3DP

7

Object detection and segmentation

ODS

5

Reinforcement learning

RL

6

Advanced machine learning techniques

AML

6

M4: Master's Thesis

Credits

Semester 4

ECTS

Master's thesis

MT

30

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