Bi-level Optimization for Learning Cost Functions from Demonstration

Chris Mills-Price, Weng-Keen Wong, Prasad Tadepalli, Ethan Dereszynski

An effective way for a novice to learn a new complex task is to observe an expert demonstrate how the task should be accomplished. While the expert demonstration provides all the necessary information for solving that particular instance of the task, the novice needs to be able to generalize from the demonstration in order to accomplish similar tasks in different settings. One way for the novice to generalize to other situations is to learn the expert's preferences, or equivalently the expert's cost function, over goal configurations. In order to do so, we propose a bi-level optimization algorithm for learning cost functions from demonstration.

Subjects: 1.11 Planning; 12. Machine Learning and Discovery

Submitted: May 16, 2007

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