Robotics
Robotics is an interdisciplinary field of research and at the same time a key technology that contributes significantly to solving societal and economic challenges and to improving our quality of life. The study profile is designed in an interdisciplinary way, so that the main issues of autonomous and cognitive robotics are addressed both from the algorithmic point of view (e.g. perception, action generation, learning) and from the technical point of view (construction and function of robot components and robot systems).
Graduates of the study profile will have competencies to design, program, and evaluate robotic systems. These include: mathematical and algorithmic foundations of robotics, programming by demonstration and imitation learning, active visual and haptic perception, learning procedures in robotics; grasping and mobile packaging, architectures in robotics, simulation in robotics, humanoid robotics, service robotics, medical robotics, wearable robot technologies (exoskeletons, prostheses/orthotics), industrial robotics, Industry 4.0, process automation, and human-robot interfaces for intuitive robot programming.
German name: Robotik
Designated Speaker / Deputy Speaker: Prof. Tamim Asfour/ Prof. Torsten Kröger
Specific competencies acquired in the profile:
- Graduates know the fundamentals and advanced methods of robotics and can apply these methods when designing robot systems.
- They can handle current technologies and tools for the development and programming of robot systems.
- Master thesis in the field of robotics.
- One of the two root modules "Robotik" or "Echtzeitsysteme" must be taken. If the root modules have already been examined in the Bachelor's degree, more CP from the event list must be taken.
- At least 44 CP from the event list must be taken.
- Further thematically appropriate seminars, practical courses or practice of research can be taken in consultation with the profile coordinator.
- One of the complementary subjects "Mathematik", "Physik", "Elektrotechnik und Informationstechnik" or "Informationsmanagement im Ingenieurwesen" must be taken.
- A total of at least 50 LP from 2.-4 must be completed.
V=Vorlesung (Lecture), S=Seminar (Seminar), P=Praktikum (Practical course), Ü=Übung (Practice)
Root modules (at least 6 CP) |
Course | Module | Partial achievement | CP | Course type |
Echtzeitsysteme (root module) | M-INFO-100803 | T-INFO-101340 | 6 | V | |
Robotik I - Einführung in die Robotik (root module) | M-INFO-100893 | T-INFO-108014 | 6 | V | |
List of courses (at least 44 CP) | Course | Module | Partial achievement | CP | Course type |
Anziehbare Robotertechnologien | M-INFO-103294 | T-INFO-106557 |
4 | V | |
Biologisch Motivierte Robotersysteme | M-INFO-100814 | T-INFO-101351 | 3 | V | |
Deep Learning for Robotics | M-INFO-105480 | T-INFO-111024 | 6 | P | |
Explainable Artificial Intelligence |
M-INFO-106302 |
T-INFO-112774 |
3 |
V |
|
Forschungspraktikum Autonome Lernende Roboter | M-INFO-105378 | T-INFO-110861 | 6 | P | |
Industrie 4.0 | M-INFO-103528 | T-INFO-107045 | 3 | V | |
Innovative Konzepte zur Programmierung von Industrierobotern | M-INFO-100791 | T-INFO-101328 | 4 | V | |
Lokalisierung mobiler Agenten | M-INFO-100840 | T-INFO-101377 | 6 | V | |
Maschinelles Lernen - Grundlagen und Algorithmen |
M-INFO-105778 | INFO-111558 | 6 | V/Ü | |
Maschinelles Lernen 1 - Grundverfahren (cancelled) | M-INFO-100817 | T-INFO-101354 | 3 | V | |
Maschinelles Lernen 2 - Fortgeschrittene Verfahren (cancelled) | M-INFO-100855 | T-INFO-101392 | 3 | V | |
Maschinelles Lernen - Grundverfahren (from WS 19/20 - 3 CP / new version from SS 20 - 5 LP) | M-INFO-105252 | T-INFO-110630 | 3/5 | V | |
Mustererkennung | M-INFO-100825 | T-INFO-101362 | 3 | V | |
Neuronale Netze | M-INFO-100846 | T-INFO-101383 | 6 | V | |
Praktikum: Mobile Roboter OLD!!!! | M-INFO-102977 | T-INFO-105951 | 6 | V | |
Praktikum: Biologisch Motivierte Roboter new from WS 23/24 |
M-INFO-105495 | T-INFO-111039 | 6 | P | |
Projektpraktikum: Robotik und Automation I (Software) | M-INFO-102224 | T-INFO-104545 | 6 | P | |
Projektpraktikum: Robotik und Automation II (Hardware) | M-INFO-102230 | T-INFO-104552 | 6 | P | |
Reinforcement Learning | M-INFO-105623 | T-INFO-111255 | 5 | V | |
Riemannsche Methoden zum Lernen in der Robotik | M-INFO-105791 | T-INFO-111589 | 3 | V | |
Roboterpraktikum | M-INFO-102522 | T-INFO-105107 | 6 | V | |
Robotik II - Humanoide Robotik | M-INFO-102756 | T-INFO-105723 | 3 | V | |
Robotik III - Sensoren in der Robotik | M-INFO-100815 | T-INFO-101352 | 3 | V | |
Robotik in der Medizin (not applicable from WS 22/23) | M-INFO-100820 | T-INFO-101357 | 3 | V | |
Seminar: Humanoide Roboter | M-INFO-102561 | T-INFO-105144 | 3 | S | |
Seminar: Intelligente Industrieroboter | M-INFO-102212 | T-INFO-104526 | 3 | V | |
Seminar: Motion in Man and Machine | M-INFO-102555 | T-INFO-105140 | 3 | S | |
Seminar: Neuronale Netze und künstliche Intelligenz | M-INFO-102412 | T-INFO-104777 | 3 | S | |
Seminar: Robotik und Medizin | M-INFO-102211 | T-INFO-104525 | 3 | S | |
Seminar: Robot Reinforcement Learning | M-INFO-105379 | T-INFO-110862 | 3 | S | |
maximum 6 CP from: | |||||
Automatische Sichtprüfung und Bildverarbeitung | M-INFO-100826 | T-INFO-101363 | 6 | V | |
Barrierefreiheit - Assistive Technologien für Sehgeschädigte | M-INFO-100764 | T-INFO-101301 | 3 | V | |
Einführung in die Bildfolgenauswertung | M-INFO-100736 | T-INFO-101273 | 3 | V | |
Informationsverarbeitung in Sensornetzwerken | M-INFO-100895 | T-INFO-101466 | 6 | V |