Control and Learning by the State Space Model: Experimental Findings

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6273

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DC Field	Value	Language
dc.creator	Raibert, Marc	-
dc.date	2004-10-04T14:48:13Z	-
dc.date	2004-10-04T14:48:13Z	-
dc.date	1977-04-01	-
dc.date.accessioned	2013-10-09T02:44:35Z	-
dc.date.available	2013-10-09T02:44:35Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-412	-
dc.identifier	http://hdl.handle.net/1721.1/6273	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	This is the second of a two part presentation of a model for motor control and learning. The model was implemented using a small computer and the MIT -Scheinman manipulator. Experiments were conducted which demonstrate the controller's ability to learn new movements, adapt to mechanical changes caused by inertial and elastic loading, and generalize its behavior among similar movements. A second generation model, based on improvements suggested by these experiments is suggested.	-
dc.format	12327356 bytes	-
dc.format	9840929 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-412	-
dc.title	Control and Learning by the State Space Model: Experimental Findings	-
Appears in Collections:	MIT Items

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