Using Cost-regularized Kernel Regression with a high number of samples

The CrKR (Cost-regularized Kernel Regression) algorithm allows to optimize a function that dictates the meta-parameters to be used for a motor task according to a state variable. Particularly, it stood out at optimizing the meta-parameters of Dynamic Motion Primitives (DMPs) used in striking movements in robot table tennis. However, the high computational complexity of the algorithm limits the number of training samples that can be used before running out of computational resources to keep its execution. A method that minimizes this issue is here proposed, based on the idea that we can split the execution of the algorithm in several runs with a smaller number of rollouts, while transferring the learned state to meta-parameters function between successive runs. This solution allows circumventing the original algorithm limitations, making it possible to use with a high number of training samples without having to deal with an indefinitely increasing execution time per iteration. We compare the effectiveness and execution time of the algorithm before and after applying this transformation and verify that the first is almost unaffected while the latter decreases substantially.