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Nathan Ratliff
PhD Student

Email address: ndr@andrew.cmu.edu

Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213

For more information, see my personal homepage.

Jump to: Research interests | Projects | Publications

I am interested in solving the problem of imitation learning. Utilizing theory from mathematical areas such as convex optimization and functional analysis, I am developing analyzing and implementing computationally efficient and robust algorithms for solving large-scale and structured prediction problems in both batch and online settings. Much of my inspiration comes from difficult problems found in autonomous mobile and legged robotics, including the problem of training a robot to make decisions intelligently while considering long-term consequences of these decisions.

• Learning Locomotion - Robust planning and control of the quadruped robot "Little Dog" to traverse rough terrain (DARPA sponsored).

Note: This list may not be comprehensive. It contains only those publications in the RI publications database. Entries are listed in reverse chronological order.

• Imitation Learning for Locomotion and Manipulation
  N. Ratliff, J. Bagnell, and S. Srinivasa
  tech. report CMU-RI-TR-07-45, Robotics Institute, Carnegie Mellon University, December, 2007. [Abstract]
  Download: pdf [2236 KB] copyrighted

• Imitation Learning for Locomotion and Manipulation
  N. Ratliff, J. Bagnell, and S. Srinivasa
  IEEE-RAS International Conference on Humanoid Robots, November, 2007. [Abstract]

• (Online) Subgradient Methods for Structured Prediction
  N. Ratliff, J. Bagnell, and M. Zinkevich
  Eleventh International Conference on Artificial Intelligence and Statistics (AIStats), March, 2007. [Abstract]
  Download: pdf [375 KB] copyrighted

• Kernel Conjugate Gradient for Fast Kernel Machines
  N. Ratliff and J. Bagnell
  International Joint Conference on Artificial Intelligence, Vol. 20, January, 2007. [Abstract]
  Download: pdf [6617 KB] copyrighted

• Boosting Structured Prediction for Imitation Learning
  N. Ratliff, D. Bradley, J. Bagnell, and J. Chestnutt
  Advances in Neural Information Processing Systems 19, MIT Press, Cambridge, MA, 2007. [Abstract]
  Download: pdf [847 KB] copyrighted

• Maximum Margin Planning
  N. Ratliff, J. Bagnell, and M. Zinkevich
  International Conference on Machine Learning, July, 2006. [Abstract]
  Download: pdf [2212 KB] copyrighted

• Kernel Conjugate Gradient
  N. Ratliff and J. Bagnell
  tech. report CMU-RI-TR-05-30, Robotics Institute, Carnegie Mellon University, June, 2005. [Abstract]
  Download: pdf [102 KB] copyrighted