Robot Learning
This course is intended for students of computer science and electrical or mechanical engineering. It deals with the use of Machine Learning, particularly Reinforcement Learning, in robotics.
Overview
The Online Course "Robot Learning" builds a bridge between the essential AI subfields of Machine Learning and Robotics. After a basic introduction into these fields, three different approaches of "Robot Reinforcement Learning" will be presented. To enable problem-based learning, Jupyter notebooks are used in addition to learning videos, various quiz formats and audience response systems. Robot learning methods can be tested in exemplary applications and new approaches can be developed using a modular principle.
Which topics will be covered?
- Basics of Robotics and Machine Learning
- Robot modeling with Machine Learning
- Learning new strategies for action through exploration and imitation of experts
What will I achieve?
By the end of the course, you‘ll be able to…
- derive and apply Machine Learning methods to simulate and commission a robot system.
- to use Machine Learning for modeling real robot and other technical real-time systems. .
- briefly describe existing methods and their application as well as approaches for the development of new methods.
- name current application examples of the presented robot learning methods from current research.
Which prerequisites do I need to fulfill?
- Computer Science, Mechanical Engineering, Electrical Engineering and Information Technology
- Students in Bachelor (from 3rd semester)
