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dc.contributor.authorAboaf, Eric W.en_US
dc.contributor.authorAtkeson, Christopher G.en_US
dc.contributor.authorReinkensmeyer, David J.en_US
dc.date.accessioned2004-10-04T14:37:02Z
dc.date.available2004-10-04T14:37:02Z
dc.date.issued1987-12-01en_US
dc.identifier.otherAIM-1006en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/6055
dc.description.abstractWe 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.en_US
dc.format.extent18 p.en_US
dc.format.extent2480509 bytes
dc.format.extent978972 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesAIM-1006en_US
dc.subjectroboticsen_US
dc.subjectlearningen_US
dc.subjecttasksen_US
dc.titleTask-Level Robot Learning: Ball Throwingen_US


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