Multi-task Gaussian Process Learning of Robot Inverse Dynamics

Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)

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Christopher Williams, Stefan Klanke, Sethu Vijayakumar, Kian Chai


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 given robot manipulator will often need to be controlled while holding different loads in its end effector, giving rise to a multi-task learning problem. We show how the structure of the inverse dynamics problem gives rise to a multi-task Gaussian process prior over functions, where the inter-task similarity depends on the underlying dynamic parameters. Experiments demonstrate that this multi-task formulation generally improves performance over either learning only on single tasks or pooling the data over all tasks.