DocumentCode
2717412
Title
Identifying trajectory classes in dynamic tasks
Author
Anderson, Stuart O. ; Srinivasa, Siddhartha S.
Author_Institution
Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA
fYear
2007
fDate
1-5 April 2007
Firstpage
172
Lastpage
177
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
robots; control problems; domain knowledge; dynamic tasks; robotics; system qualitative behaviors; trajectory classes; Automatic control; Control systems; Convergence; Dynamic programming; Humans; Learning; Motion control; Robot control; Robotics and automation; State-space methods;
fLanguage
English
Publisher
ieee
Conference_Titel
Approximate Dynamic Programming and Reinforcement Learning, 2007. ADPRL 2007. IEEE International Symposium on
Conference_Location
Honolulu, HI
Print_ISBN
1-4244-0706-0
Type
conf
DOI
10.1109/ADPRL.2007.368185
Filename
4220830
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