@inproceedings{6072,
  title = {Learning new basic Movements for Robotics},
  journal = {Autonome Mobile Systeme 2009: 21. Fachgespr{\"a}ch},
  booktitle = {AMS 2009},
  abstract = {Obtaining novel skills is one of the most important problems
  in robotics. Machine learning techniques may be a promising approach
  for automatic and autonomous acquisition of movement policies. However,
  this requires both an appropriate policy representation and suitable
  learning algorithms. Employing the most recent form of the dynamical
  systems motor primitives originally introduced by Ijspeert et al. [1],
  we show how both discrete and rhythmic tasks can be learned using
  a concerted approach of both imitation and reinforcement learning, and
  present our current best performing learning algorithms. Finally, we show
  that it is possible to include a start-up phase in rhythmic primitives. We
  apply our approach to two elementary movements, i.e., Ball-in-a-Cup
  and Ball-Paddling, which can be learned on a real Barrett WAM robot
  arm at a pace similar to human learning.},
  pages = {105-112},
  editors = {Dillmann, R. , J. Beyerer, C. Stiller, M. Z{\"o}llner, T. Gindele},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = dec,
  year = {2009},
  slug = {6072},
  author = {Kober, J. and Peters, J.},
  month_numeric = {12}
}