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2008

Multi-task Gaussian Process Learning of Robot Inverse Dynamics

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Multi-task Gaussian Process Learning of Robot Inverse Dynamics
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.
Kian Ming Adam Chai, Christopher K. I. Williams, S
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Kian Ming Adam Chai, Christopher K. I. Williams, Stefan Klanke, Sethu Vijayakumar
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