Task-Level Robot Learning

dc.creator	Aboaf, Eric W.
dc.date	2004-10-20T20:11:45Z
dc.date	2004-10-20T20:11:45Z
dc.date	1988-08-01
dc.date.accessioned	2013-10-09T02:48:00Z
dc.date.available	2013-10-09T02:48:00Z
dc.date.issued	2013-10-09
dc.identifier	AITR-1079
dc.identifier	http://hdl.handle.net/1721.1/6972
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	We are investigating how to program robots so that they learn from experience. Our goal is to develop principled methods of learning that can improve a robot's performance of a wide range of dynamic tasks. We have developed task-level learning that successfully improves a robot's performance of two complex tasks, ball-throwing and juggling. With task- level learning, a robot practices a task, monitors its own performance, and uses that experience to adjust its task-level commands. This learning method serves to complement other approaches, such as model calibration, for improving robot performance.
dc.format	9997518 bytes
dc.format	3880924 bytes
dc.format	application/postscript
dc.format	application/pdf
dc.language	en_US
dc.relation	AITR-1079
dc.title	Task-Level Robot Learning

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