- CHOMP: Covariant Hamiltonian Optimization for Motion Planning
Matthew Zucker, Nathan Ratliff, Anca Dragan, Mihail Pivtoraiko, Matthew Klingensmith, Christopher Dellin, J. Andrew (Drew) Bagnell, and Siddhartha Srinivasa
International Journal of Robotics Research, , May, 2013,
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- Manipulation Planning with Goal Sets Using Constrained Trajectory Optimization
Anca Dragan, Nathan Ratliff, and Siddhartha Srinivasa
2011 IEEE International Conference on Robotics and Automation, May, 2011.
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- Planning-based Prediction for Pedestrians
Brian D. Ziebart, Nathan Ratliff, Garratt Gallagher, Christoph Mertz, Kevin Peterson, J. Andrew (Drew) Bagnell, Martial Hebert, Anind Dey, and Siddhartha Srinivasa
Proc. IROS 2009, October, 2009.
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- Learning to search: Functional gradient techniques for imitation learning
Nathan Ratliff, David Silver, and J. Andrew (Drew) Bagnell
Autonomous Robots, Vol. 27, No. 1, July, 2009, pp. 25-53.
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- Self-supervised aerial image analysis for extracting parking lot structure
Young-Woo Seo, Nathan Ratliff, and Christopher Urmson
Proceedings of International Joint Conference on Artificial Intelligence (IJCAI-09), July, 2009.
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- CHOMP: Gradient Optimization Techniques for Efficient Motion Planning
Nathan Ratliff, Matthew Zucker, J. Andrew (Drew) Bagnell, and Siddhartha Srinivasa
IEEE International Conference on Robotics and Automation (ICRA), May, 2009.
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- Learning to Search: Structured Prediction Techniques for Imitation Learning
Nathan Ratliff
doctoral dissertation, tech. report CMU-RI-TR-09-19, Robotics Institute, Carnegie Mellon University, May, 2009
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- Inverse Optimal Heuristic Control for Imitation Learning
Nathan Ratliff, Brian D. Ziebart, Kevin Peterson, J. Andrew (Drew) Bagnell, Martial Hebert, Anind Dey, and Siddhartha Srinivasa
Twelfth International Conference on Artificial Intelligence and Statistics (AIStats), April, 2009.
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- BiSpace Planning: Concurrent Multi-Space Exploration
Rosen Diankov, Nathan Ratliff, David Ferguson, Siddhartha Srinivasa, and James Kuffner
Robotics: Science and Systems, June, 2008.
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- Imitation Learning for Locomotion and Manipulation
Nathan Ratliff, J. Andrew (Drew) Bagnell, and Siddhartha Srinivasa
tech. report CMU-RI-TR-07-45, Robotics Institute, Carnegie Mellon University, December, 2007
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- Imitation Learning for Locomotion and Manipulation
Nathan Ratliff, J. Andrew (Drew) Bagnell, and Siddhartha Srinivasa
IEEE-RAS International Conference on Humanoid Robots, November, 2007.
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- (Online) Subgradient Methods for Structured Prediction
Nathan Ratliff, J. Andrew (Drew) Bagnell, and Martin Zinkevich
Eleventh International Conference on Artificial Intelligence and Statistics (AIStats), March, 2007.
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- Kernel Conjugate Gradient for Fast Kernel Machines
Nathan Ratliff and J. Andrew (Drew) Bagnell
International Joint Conference on Artificial Intelligence, January, 2007.
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- Boosting Structured Prediction for Imitation Learning
Nathan Ratliff, David Bradley, J. Andrew (Drew) Bagnell, and Joel Chestnutt
Advances in Neural Information Processing Systems 19, 2007.
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- Maximum Margin Planning
Nathan Ratliff, J. Andrew (Drew) Bagnell, and Martin Zinkevich
International Conference on Machine Learning, July, 2006.
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- Subgradient Methods for Maximum Margin Structured Learning
Nathan Ratliff, J. Andrew (Drew) Bagnell, and Martin Zinkevich
April, 2006.
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- Kernel Conjugate Gradient
Nathan Ratliff and J. Andrew (Drew) Bagnell
tech. report CMU-RI-TR-05-30, Robotics Institute, Carnegie Mellon University, June, 2005
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pdf (103KB) | Copyrighted
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