@inproceedings{Pastor_RAIIC_2011,
  title = {Skill learning and task outcome prediction for manipulation},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  abstract = {Learning complex motor skills for real world tasks
  is a hard problem in robotic manipulation that often requires
  painstaking manual tuning and design by a human expert.
  In this work, we present a Reinforcement Learning based
  approach to acquiring new motor skills from demonstration.
  Our approach allows the robot to learn fine manipulation skills
  and significantly improve its success rate and skill level starting
  from a possibly coarse demonstration. Our approach aims to
  incorporate task domain knowledge, where appropriate, by
  working in a space consistent with the constraints of a specific
  task. In addition, we also present an approach to using sensor
  feedback to learn a predictive model of the task outcome. This
  allows our system to learn the proprioceptive sensor feedback
  needed to monitor subsequent executions of the task online and
  abort execution in the event of predicted failure. We illustrate
  our approach using two example tasks executed with the PR2
  dual-arm robot: a straight and accurate pool stroke and a box
  flipping task using two chopsticks as tools.},
  address = {Shanghai, China, May 9-13},
  year = {2011},
  note = {clmc},
  slug = {pastor_raiic_2011},
  author = {Pastor, P. and Kalakrishnan, M. and Chitta, S. and Theodorou, E. and Schaal, S.},
  crossref = {p10446},
  url = {http://www-clmc.usc.edu/publications/P/pastor-ICRA2011.pdf}
}