You are interested in working with real robots and want to understand how such machines ‘learn’?
This project will enable you to dig into the fascinating world of robot learning.
You will work alone or in a team on modern, state-of-the art hardware at the Chair of CPS.
We offer complex robotic systems, powerful PCs and GPU clusters to work with.
The course provides a structured and well motivated overview over modern techniques and tools which enable the students to define learning problems in Cyber-Physical-Systems.
Featured Topics
Links and Resources
Location & Time
- Location: Laboratories at the Chair of Cyber-Physical-Systems.
- Dates: Regular, typ. weekly meetings, with your supervisor(s).
Learning objectives / qualifications
- Students get a practical experience in working, modeling and simulating Cyber-Physical-Systems.
- Students understand and can apply advanced model learning and reinforcement techniques to real world problems.
- Students learn how to write scientific reports.
Literature
- The Probabilistic Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert.
- Bishop 2006. Pattern Recognition and Machine Learning, Springer.
- Barber 2007. Bayesian Reasoning and Machine Learning, Cambridge University Press.
- Murray, Li and Sastry 1994. A mathematical introduction to robotic manipulation, CRC Press.
- B. Siciliano, L. Sciavicco 2009. Robotics: Modelling,Planning and Control, Springer.
- Kevin M. Lynch and Frank C. Park 2017. MODERN ROBOTICS, MECHANICS, PLANNING, AND CONTROL, Cambridge University Press.