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Learning Locomotion
Head: Chris Atkeson, J. Andrew (Drew) Bagnell, and James Kuffner
Contact: Chris Atkeson
Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213
Associated center(s) / consortia:
 Center for the Foundations of Robotics (CFR)
Associated lab(s) / group(s):
 Planning and Autonomy Lab
Project Homepage
Publications
  • An Optimization Approach to Rough Terrain Locomotion

    Matthew Zucker, J. Andrew (Drew) Bagnell, Chris Atkeson, and James Kuffner
    IEEE Conference on Robotics and Automation, May, 2010.
    Details | pdf (2MB) | Copyrighted

  • Reinforcement Planning: RL for Optimal Planners

    Matthew Zucker and J. Andrew (Drew) Bagnell
    tech. report CMU-RI-TR-10-14, Robotics Institute, Carnegie Mellon University, May, 2010
    Details | pdf (3MB) | Copyrighted

  • 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.
    Details | pdf (6MB) | Copyrighted

  • A Data-Driven Approach to High Level Planning

    Matthew Zucker
    tech. report CMU-RI-TR-09-42, Robotics Institute, Carnegie Mellon University, January, 2009
    Details | pdf (5MB) | Copyrighted

  • Finding and Transferring Policies Using Stored Behaviors

    Martin Stolle
    doctoral dissertation, tech. report CMU-RI-TR-08-27, Robotics Institute, Carnegie Mellon University, May, 2008
    Details | pdf (10MB) | Copyrighted

  • Adaptive Workspace Biasing for Sampling Based Planners

    Matthew Zucker, James Kuffner, and J. Andrew (Drew) Bagnell
    Proc. IEEE Int'l Conf. on Robotics and Automation, May, 2008.
    Details | pdf (977KB) | Copyrighted

  • (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.
    Details | pdf (375KB) | Copyrighted

  • Kernel Conjugate Gradient for Fast Kernel Machines

    Nathan Ratliff and J. Andrew (Drew) Bagnell
    International Joint Conference on Artificial Intelligence, January, 2007.
    Details | pdf (7MB) | Copyrighted

  • 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
    Details | pdf (2MB) | Copyrighted

  • Imitation Learning for Locomotion and Manipulation

    Nathan Ratliff, J. Andrew (Drew) Bagnell, and Siddhartha Srinivasa
    IEEE-RAS International Conference on Humanoid Robots, December, 2007.
    Details | pdf (2MB) | Copyrighted

  • Transfer of policies based on trajectory libraries

    Martin Stolle, Hanns Tappeiner, Joel Chestnutt, and Chris Atkeson
    Proceedings of the International Conference on Intelligent Robots and Systems, 2007.
    Details | pdf (2MB) | Copyrighted

  • Knowledge transfer using local features

    Martin Stolle and Chris Atkeson
    Proceedings of the IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning, 2007.
    Details | pdf (1MB) | Copyrighted

  • 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.
    Details | pdf (847KB) | Copyrighted

  • Maximum Margin Planning

    Nathan Ratliff, J. Andrew (Drew) Bagnell, and Martin Zinkevich
    International Conference on Machine Learning, July, 2006.
    Details | pdf (2MB) | Copyrighted

  • Kernel Conjugate Gradient

    Nathan Ratliff and J. Andrew (Drew) Bagnell
    tech. report CMU-RI-TR-05-30, Robotics Institute, Carnegie Mellon University, July, 2005
    Details | pdf (103KB) | Copyrighted