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Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS 2010), Taiwan (2010).

dc.contributor.authorTowell, Christopheren
dc.contributor.authorHoward, Matthewen
dc.contributor.authorVijayakumar, Sethuen
dc.date.accessioned2010-08-18T15:39:29Z
dc.date.available2010-08-18T15:39:29Z
dc.date.issued2010
dc.identifier.urihttp://hdl.handle.net/1842/3649
dc.description.abstractMany everyday tasks performed by people, such as reaching, pointing or drawing, resolve redundant degrees of freedom in the arm in a similar way. In this paper we present a novel method for learning the strategy used to resolve redundancy by exploiting the variability in multiple observations of different tasks.We demonstrate the effectiveness of this method on three simulated plants: a toy example, a three link planar arm, and the KUKA lightweight arm.en
dc.language.isoen
dc.subjectInformaticsen
dc.subjectComputer Scienceen
dc.titleLearning Nullspace Policiesen
dc.typeConference Paperen
rps.titleProc. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS 2010), Taiwan (2010).en
dc.extent.noOfPages8en
dc.date.updated2010-08-18T15:39:30Z


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