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Identifying Trajectory Classes in Dynamic Tasks
S. Anderson and S. Srinivasa
International Symposium on Approximate Dynamic Programming and Reinforcement Learning, April, 2007.

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Using domain knowledge to decompose difficult control problems is a widely used technique in robotics. Previous work has automated the process of identifying some qualitative behaviors of a system, finding a decomposition of the system based on that behavior, and constructing a control policy based on that decomposition. We introduce a novel method for automatically finding decompositions of a task based on observing the behavior of a preexisting controller. Unlike previous work, these decompositions define reparameterizations of the state space that can permit simplified control of the system.

S. Anderson and S. Srinivasa, "Identifying Trajectory Classes in Dynamic Tasks," International Symposium on Approximate Dynamic Programming and Reinforcement Learning, April, 2007.

@inproceedings{Anderson_2007_5606,
   author = "Stuart Anderson and Siddhartha Srinivasa",
   title = "Identifying Trajectory Classes in Dynamic Tasks",
   booktitle = "International Symposium on Approximate Dynamic Programming and Reinforcement Learning",
   month = "April",
   year = "2007"
}