Task-Level Robot Learning: Ball Throwing

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

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