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Proc. Advances in Neural Information Processing Systems (NIPS '08)

dc.contributor.authorChai, Kian Ming
dc.contributor.authorKlanke, Stefan
dc.contributor.authorWilliams, Chris
dc.contributor.authorVijayakumar, Sethu
dc.date.accessioned2010-08-23T12:26:22Z
dc.date.available2010-08-23T12:26:22Z
dc.date.issued2008
dc.identifier.urihttp://hdl.handle.net/1842/3664
dc.description.abstractThe inverse dynamics problem for a robotic manipulator is to compute the torques needed at the joints to drive it along a given trajectory; it is beneficial to be able to learn this function for adaptive control. A robotic manipulator will often need to be controlled while holding different loads in its end effector, giving rise to a multi-task learning problem. By placing independent Gaussian process priors over the latent functions of the inverse dynamics, we obtain a multi-task Gaussian process prior for handling multiple loads, where the inter-task similarity depends on the underlying inertial parameters. Experiments demonstrate that this multi-task formulation is effective in sharing information among the various loads, and generally improves performance over either learning only on single tasks or pooling the data over all tasks.en
dc.language.isoenen
dc.subjectInformaticsen
dc.titleMulti-task Gaussian Process Learning of Robot Inverse Dynamicsen
dc.typeConference Paperen
rps.titleProc. Advances in Neural Information Processing Systems (NIPS '08)en
dc.extent.noOfPages8en
dc.date.updated2010-08-23T12:26:23Z


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