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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.

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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.

Sponsor: Darpa (Learning for Locomotion and UPI)

Associated centers: CFR and NREC
Associated lab/group: Planning and Autonomy Lab
Associated projects: UGCV PerceptOR Integrated, Learning Applied to Ground Robots Platform, and Learning Locomotion

Number of pages: 10

N. Ratliff, D. Bradley, J. Bagnell, and J. Chestnutt, "Boosting Structured Prediction for Imitation Learning," Advances in Neural Information Processing Systems 19, MIT Press, Cambridge, MA, 2007.

@inproceedings{Ratliff_2007_5604,
   author = "Nathan Ratliff and David Bradley and James (Drew) Bagnell and Joel Chestnutt",
   title = "Boosting Structured Prediction for Imitation Learning",
   booktitle = "Advances in Neural Information Processing Systems 19",
   year = "2007",
   publisher = "MIT Press",
   address = "Cambridge, MA"
}