Robot Learning Lab
Department for Empirical Inference & Machine Learning (AG
Schoelkopf)
Max
Planck Institute for Biological Cybernetics
Member » Jens Kober
Jens Kober joined the Max-Planck Institute of Biological Cybernetics in 2007 as a Master's Student in the Department of Bernhard Schölkopf under the supervision of Jan Peters. Before doing so, he studied at the University of Stuttgart and at the Ecole Centrale Paris (ECP).
In 2008 he completed the double degree program T.I.M.E. and graduated from the University of Stuttgart with a Diplom-Ingenieur in Engineering Cybernetics (German M.Sc. majoring in automation & control) as well as from ECP as a Centralien (French engineering degree with an integrated multidisciplinary approach). He has been a visiting research student at the Advanded Telecommunication Research (ATR) Center in Japan. Please see his curriculum vitae for more biographical information.
Jens Kober is currently working towards a Ph.D. in the area of motor skill learning with a strong focus on learning motor primitives and on reinforcement learning.
Integrating highly complex robots in daily life requires them to become more independent of preprogrammed behaviors and exception handling. As biological research has shown different complex movements are actually a result of the combination of simple motor primitives. This concept is also successfully applied to robotics but there remains still a lot of research to be done in order to make the current formulations more versatile. In nature as in robotics tasks are often learned by observation and imitation. In many cases the imitation is imperfect and has to be improved. Reinforcement Learning is a natural choice for this step.
Research Interests: Robotics, Machine Learning
Biographical Information: Please see his curriculum vitae.
Publications: For the complete list of his publication, see here.
Collaborators: Betty Mohler, Silvia Chiappa, Katharina Muelling, Oliver Kroemer, Christoph Lampert, Bernhard Schölkopf, Erhan Oztop, Jan Peters.
Software
A basic MATLAB/Octave implementation of the PoWER algorithm [1]: matlab_PoWER.zip
The required motor primitive code can be downloaded from
http://www-clmc.usc.edu/Resources/Software.
Key References
Kober, J.; Peters, J. (2009). Policy Search for Motor Primitives in Robotics, Advances in Neural Information Processing Systems 22 (NIPS 2008), Cambridge, MA: MIT Press. [PDF]
Kober, J.; Peters, J. (2009). Learning Motor Primitives for Robotics, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
Chiappa, S.; Kober, J.; Peters, J. (2009). Using Bayesian Dynamical Systems for Motion Template Libraries, Advances in Neural Information Processing Systems 22 (NIPS 2008), Cambridge, MA: MIT Press. [PDF]
Kober,J.; Muelling,K.; Kroemer, O.; Lampert,C.H.; Schoelkopf, B.; Peters, J. (2010). Movement Templates for Learning of Hitting and Batting, IEEE International Conference on Robotics and Automation.
Contact Information
Mail: Jens Kober, Spemannstr. 38, 72076 Tübingen, Germany
Phone: +49-7071-601-548
Fax: +49-7071-601-552
Email:
