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Multi-task Gaussian Process Learning of Robot Inverse Dynamics-Spotlight.pdf182.3 kBAdobe PDFView/Open
Multi-task Gaussian Process Learning of Robot Inverse Dynamics.pdf161.35 kBAdobe PDFView/Open
Title: Multi-task Gaussian Process Learning of Robot Inverse Dynamics
Authors: Chai, Kian Ming
Klanke, Stefan
Williams, Chris
Vijayakumar, Sethu
Issue Date: 2008
Journal Title: Proc. Advances in Neural Information Processing Systems (NIPS '08)
Abstract: The 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.
Keywords: Informatics
Appears in Collections:Informatics Publications

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