@inproceedings{6745,
  title = {Learning Table Tennis with a Mixture of Motor Primitives},
  journal = {Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2010)},
  abstract = {Table tennis is a sufficiently complex motor task
  for studying complete skill learning systems. It consists of several
  elementary motions and requires fast movements, accurate
  control, and online adaptation. To represent the elementary
  movements needed for robot table tennis, we rely on dynamic
  systems motor primitives (DMP). While such DMPs have been
  successfully used for learning a variety of simple motor tasks,
  they only represent single elementary actions. In order to select
  and generalize among different striking movements, we present
  a new approach, called Mixture of Motor Primitives that uses
  a gating network to activate appropriate motor primitives. The
  resulting policy enables us to select among the appropriate
  motor primitives as well as to generalize between them. In
  order to obtain a fully learned robot table tennis setup, we
  also address the problem of predicting the necessary context
  information, i.e., the hitting point in time and space where
  we want to hit the ball. We show that the resulting setup
  was capable of playing rudimentary table tennis using an
  anthropomorphic robot arm.},
  pages = {411-416},
  publisher = {IEEE},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = dec,
  year = {2010},
  author = {M{\"u}lling, K. and Kober, J. and Peters, J.},
  doi = {10.1109/ICHR.2010.5686298},
  month_numeric = {12}
}