Multi-task Gaussian Process Learning of Robot Inverse Dynamics

Kian Ming Chai, Christopher K. I. Williams, Stefan Klanke, Sethu Vijayakumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationProc. Advances in Neural Information Processing Systems (NIPS '08)
Number of pages8
Publication statusPublished - 2008


  • Informatics


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