Learning to jump in granular media: Unifying optimal control synthesis with Gaussian process-based regression. Chang, A. H, Hubicki, C. M, Aguilar, J. J, Goldman, D. I, Ames, A. D, & Vela, P. A In Robotics and Automation (ICRA), 2017 IEEE International Conference on, pages 2154–2160, 2017. IEEE.
Learning to jump in granular media: Unifying optimal control synthesis with Gaussian process-based regression [pdf]Paper  bibtex   1 download  
@inproceedings{chang2017learning,
  title={Learning to jump in granular media: Unifying optimal control synthesis with Gaussian process-based regression},
  author={Chang, Alexander H and Hubicki, Christian M and Aguilar, Jeff J and Goldman, Daniel I and Ames, Aaron D and Vela, Patricio A},
  booktitle={Robotics and Automation (ICRA), 2017 IEEE International Conference on},
  pages={2154--2160},
  year={2017},
  organization={IEEE},
  url ={http://ames.caltech.edu/chang2017learning.pdf},
  keywords = {Robophysics, Machine Learning, Optimization}
}
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