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Carnegie Mellon Robotics Institute

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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",
}

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