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#41004 / #2

Seit SoSe 2022


AI and Robotics: Lab Course


Toussaint, Marc




Fakultät IV

Institut für Technische Informatik und Mikroelektronik

34342100 FG Intelligent Systems

No information


MAR 4-4

Toussaint, Marc


Learning Outcomes

The students can program robotics systems to perform object manipulation tasks. To this end, they can integrate basic methodologies covered by other introductory courses, in particular motion generation and perception, potentially also machine learning, task planning, and mobile navigation.


In this practical lab course students will directly work with robotic systems (or in simulation, if not possible otherwise). In the first half of the course, the major time is spend on practically solving (coding) a series of problems, with direct supervision by the instructor during the sessions. In some lectures the instructor introduces basic concepts. The series of problems includes, for instance, * generation of basic motion on the robot system, * leveraging state-of-the-art motion planning and optimization, * perceiving objects and mapping them into virtual representations, * pointing to, grasping, and pushing objects, * realizing longer manipulation sequences. In the second half, students work on a more involed project towards a final presentation of their system.

Module Components


All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
AI and Robotics: Lab CoursePraktikum3434 L 10658SoSeEnglish4

Workload and Credit Points

AI and Robotics: Lab Course (Praktikum):

Workload descriptionMultiplierHoursTotal
Exercise work15.04.0h60.0h
Prepare and revisit15.04.0h60.0h
180.0h(~6 LP)
The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

Practical work directly on the robot, solving a series of tasks, supervised by the teacher.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Students should have * in depth knowledge in robotics (passed a robotics course) * basic knowledge in AI or machine learning If these prerequisits are not met, the iunstructor needs to approve participation.

Mandatory requirements for the module test application:

This module has no requirements.

Module completion



Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt



Test elements

(Deliverable assessment) Implementation and documentation25writtenthroughout the course
(Deliverable assessment) Practical tasks50practicalabout 4 tasks throughout the course
(Deliverable assessment) Presentation of the solutions and a final project25oral5-10 minutes per week, plus 30 minutes final presentation

Grading scale

Notenschlüssel »Notenschlüssel 2: Fak IV (2)«


Test description (Module completion)

No information

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:

Maximum Number of Participants

The maximum capacity of students is 12.

Registration Procedures

Please register via the respective ISIS page. Also check for additional information on our teaching website https://argmin.lis.tu-berlin.de/teaching/

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable


Electronical lecture notes

Availability:  unavailable



Recommended literature
No recommended literature given

Assigned Degree Programs

This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Computer Engineering (M. Sc.)110SoSe 2022SoSe 2024
Computer Science (Informatik) (M. Sc.)110SoSe 2022SoSe 2024
Elektrotechnik (M. Sc.)15SoSe 2022SoSe 2024


No information