Task-Level Robot Learning: Ball Throwing

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6055

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DC Field	Value	Language
dc.creator	Aboaf, Eric W.	-
dc.creator	Atkeson, Christopher G.	-
dc.creator	Reinkensmeyer, David J.	-
dc.date	2004-10-04T14:37:02Z	-
dc.date	2004-10-04T14:37:02Z	-
dc.date	1987-12-01	-
dc.date.accessioned	2013-10-09T02:42:35Z	-
dc.date.available	2013-10-09T02:42:35Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1006	-
dc.identifier	http://hdl.handle.net/1721.1/6055	-
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 tasks from practice. One method, task-level learning, provides advantages over simply perfecting models of the robot's lower level systems. Task-level learning can compensate for the structural modeling errors of the robot's lower level control systems and can speed up the learning process by reducing the degrees of freedom of the models to be learned. We demonstrate two general learning procedures---fixed-model learning and refined-model learning---on a ball-throwing robot system.	-
dc.format	18 p.	-
dc.format	2480509 bytes	-
dc.format	978972 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1006	-
dc.subject	robotics	-
dc.subject	learning	-
dc.subject	tasks	-
dc.title	Task-Level Robot Learning: Ball Throwing	-
Appears in Collections:	MIT Items

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