@inproceedings{Stulp_RAIIC_2011,
  title = {Learning to grasp under uncertainty},
  booktitle = {Robotics and Automation (ICRA), 2011 IEEE International Conference on},
  abstract = {We present an approach that enables robots to
  learn motion primitives that are robust towards state estimation
  uncertainties. During reaching and preshaping, the robot learns
  to use fine manipulation strategies to maneuver the object into
  a pose at which closing the hand to perform the grasp is more
  likely to succeed. In contrast, common assumptions in grasp
  planning and motion planning for reaching are that these tasks
  can be performed independently, and that the robot has perfect
  knowledge of the pose of the objects in the environment.
  We implement our approach using Dynamic Movement
  Primitives and the probabilistic model-free reinforcement learning
  algorithm Policy Improvement with Path Integrals (PI2 ).
  The cost function that PI2 optimizes is a simple boolean that
  penalizes failed grasps. The key to acquiring robust motion
  primitives is to sample the actual pose of the object from a
  distribution that represents the state estimation uncertainty.
  During learning, the robot will thus optimize the chance of
  grasping an object from this distribution, rather than at one
  specific pose.
  In our empirical evaluation, we demonstrate how the motion
  primitives become more robust when grasping simple cylindrical
  objects, as well as more complex, non-convex objects. We
  also investigate how well the learned motion primitives generalize
  towards new object positions and other state estimation
  uncertainty distributions.},
  address = {Shanghai, China, May 9-13},
  year = {2011},
  note = {clmc},
  slug = {stulp_raiic_2011},
  author = {Stulp, F. and Theodorou, E. and Buchli, J. and Schaal, S.},
  crossref = {p10445},
  url = {http://www-clmc.usc.edu/publications/S/stulp-ICRA2011.pdf}
}