Identifying Trajectory Classes in Dynamic Tasks

Stuart Anderson and Siddhartha Srinivasa
International Symposium on Approximate Dynamic Programming and Reinforcement Learning, May, 2007.


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Abstract
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.

Keywords
Trajectories, Control, Task Identification

Notes
Associated Center(s) / Consortia: Quality of Life Technology Center, National Robotics Engineering Center, and Center for the Foundations of Robotics
Associated Lab(s) / Group(s): Personal Robotics

Text Reference
Stuart Anderson and Siddhartha Srinivasa, "Identifying Trajectory Classes in Dynamic Tasks," International Symposium on Approximate Dynamic Programming and Reinforcement Learning, May, 2007.

BibTeX Reference
@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 = "May",
   year = "2007",
}