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.


Download
  • Adobe portable document format (pdf) (847KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
The Maximum Margin Planning (MMP) algorithm solves imitation learning problems by learning linear mappings from features to cost functions in a planning domain. The learned policy is the result of minimum-cost planning using these cost functions. These mappings are chosen so that example policies (or trajectories) given by a teacher appear to be lower cost (with a loss-scaled margin) than any other policy for a given planning domain. We provide a novel approach, MMPBoost, based on the functional gradient descent view of boosting that extends MMP by ``boosting'' in new features. This approach uses simple binary classification or regression to improve performance of MMP imitation learning, and naturally extends to the class of structured maximum margin prediction problems. Our technique is applied to navigation and planning problems for outdoor mobile robots and robotic legged locomotion.

Keywords
Structured Prediction, Boosting, imitation learning, robotics, Margin

Notes
Sponsor: Darpa (Learning for Locomotion and UPI)
Associated Center(s) / Consortia: National Robotics Engineering Center and Center for the Foundations of Robotics
Associated Lab(s) / Group(s): Planning and Autonomy Lab
Associated Project(s): UGCV PerceptOR Integrated, Learning Robots, Learning Locomotion
Number of pages: 10

Text Reference
Nathan Ratliff, David Bradley, J. Andrew (Drew) Bagnell, and Joel Chestnutt, "Boosting Structured Prediction for Imitation Learning," Advances in Neural Information Processing Systems 19, 2007.

BibTeX Reference
@inproceedings{Ratliff_2007_5604,
   author = "Nathan Ratliff and David Bradley and J. Andrew (Drew) Bagnell and Joel Chestnutt",
   editor = "B. Sch\"{o}lkopf and J.C. Platt and T. Hofmann",
   title = "Boosting Structured Prediction for Imitation Learning",
   booktitle = "Advances in Neural Information Processing Systems 19",
   publisher = "MIT Press",
   address = "Cambridge, MA",
   year = "2007",
}