Multi-task Gaussian process learning of robot inverse dynamics
Kian Ming Chai, Christopher Williams, Stefan Klanke and Sethu Vijayakumar
In: Advances in Neural Information Processing Systems 21, 8-11 Dec 2008, Vancouver, Canada.
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.