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Admission requirements

Not applicable.


During the last decade we have seen an "explosion" of all kinds of robots designed for tasks that previously were deemed too challenging for machines. Robots have evolved from robotic arms and karts that could execute repetitive or simple tasks such as painting, welding and vacuum cleaning to autonomous cars, drones and humanoid helpers and servants that execute their tasks in much less controlled and even natural settings. For this modern robots require sophisticated adaptive capabilities.
During the course we will have a thorough look at the important aspects of robot-architectures used in modern and state of the art robots. The use of various actuators and sensors will be studied. Algorithms for low level tasks such as movement, dead reckoning, obstacle-detection, and balancing will be presented. Intermediate level tasks such as mapping, obstacle recognition and avoidance, and more advanced modes of reckoning, navigation and object manipulation will be studied. Finally, high level tasks such as human-robot-interaction and adaptive behavior in natural environments will be studied and proto-typed using state of the art sensor analysis, computer vision and audio recognition techniques.

Course objectives

After successfully finishing the Robotics course the student:

  • Has a thorough insight and understanding of the underlying architectures and operating systems of modern state of the art robotic platforms.

  • Is capable of designing, developing and implementing algorithms for low-, and mid-level robotic tasks on different robotic simulators and platforms.

  • Has a good understanding of the challenges and progress in robotics research.

  • Has insights in the design and implementation of high level robotic tasks using state of the art tools for sensor analysis, computer vision and audio recognition techniques.

  • Is capable of implementing a prototype for autonomous Human Robot Interaction.


The most recent timetable can be found at the Computer Science (MSc) student website.

Mode of instruction

  • Lectures

  • Projects

  • Workshops

  • Student discussions

  • Presentations

  • Homework and assignments

Course load

Hours of study: 168 (= 6 EC)
Lectures: 26
Practical work: 72
Other (self-study): 70

Assessment method

The final grade is based on:

  • 2 workshops (20% of the grade)

  • YetiBorg Challenge (20% of the grade)

  • Robotics Project (60% of the grade)

The teacher will inform the students how the inspection of and follow-up discussion of the work will take place.

Reading list

Conferences and journals on Robotics.


  • You have to sign up for courses and exams (including retakes) in uSis. Check this link for information about how to register for courses.


Lecturer: dr Erwin M. Bakker
Assistent: To be announced.
Website: Robotics