@inproceedings{5880,
  title = {Learning Complex Motions by Sequencing Simpler Motion Templates},
  journal = {Proceedings of the 26th International Conference on Machine Learning (ICML 2009)},
  booktitle = {ICML 2009},
  abstract = {Abstraction of complex, longer motor tasks
  into simpler elemental movements enables
  humans and animals to exhibit motor skills
  which have not yet been matched by robots.
  Humans intuitively decompose complex motions
  into smaller, simpler segments. For
  example when describing simple movements
  like drawing a triangle with a pen, we can
  easily name the basic steps of this movement.
  Surprisingly, such abstractions have rarely
  been used in artificial motor skill learning algorithms.
  These algorithms typically choose
  a new action (such as a torque or a force) at a
  very fast time-scale. As a result, both policy
  and temporal credit assignment problem become
  unnecessarily complex - often beyond
  the reach of current machine learning methods.
  We introduce a new framework for temporal
  abstractions in reinforcement learning (RL),
  i.e. RL with motion templates. We present a
  new algorithm for this framework which can
  learn high-quality policies by making only
  few abstract decisions.},
  pages = {753-760},
  editors = {Danyluk, A. , L. Bottou, M. Littman},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jun,
  year = {2009},
  slug = {5880},
  author = {Neumann, G. and Maass, W. and Peters, J.},
  month_numeric = {6}
}