@article{Kalakrishnan_IJRR_2010,
  title = {Learning, planning, and control for quadruped locomotion over challenging terrain},
  booktitle = {International Journal of Robotics Research},
  abstract = {We present a control architecture for fast quadruped locomotion over rough terrain. We approach the problem by decomposing
  it into many sub-systems, in which we apply state-of-the-art learning, planning, optimization, and control techniques
  to achieve robust, fast locomotion. Unique features of our control strategy include: (1) a system that learns optimal
  foothold choices from expert demonstration using terrain templates, (2) a body trajectory optimizer based on the Zero-
  Moment Point (ZMP) stability criterion, and (3) a floating-base inverse dynamics controller that, in conjunction with force
  control, allows for robust, compliant locomotion over unperceived obstacles. We evaluate the performance of our controller
  by testing it on the LittleDog quadruped robot, over a wide variety of rough terrains of varying difficulty levels. The
  terrain that the robot was tested on includes rocks, logs, steps, barriers, and gaps, with obstacle sizes up to the leg length
  of the robot. We demonstrate the generalization ability of this controller by presenting results from testing performed by
  an independent external test team on terrain that has never been shown to us.},
  volume = {30},
  number = {2},
  pages = {236-258},
  year = {2010},
  note = {clmc},
  slug = {kalakrishnan_ijrr_2010},
  author = {Kalakrishnan, M. and Buchli, J. and Pastor, P. and Mistry, M. and Schaal, S.},
  crossref = {p10420},
  url = {http://www-clmc.usc.edu/publications/K/kalakrishnan-IJRR2010.pdf}
}