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Dynamic Movement Primitives (DMPs) are nowadays widely used as movement parametrization for learning robot trajectories, because of their linearity in the parameters, rescaling robustness and continuity. However, when learning a movement with DMPs, a very large number of Gaussian approximations needs to be performed. Adding them up for all joints yields too many parameters to be explored when using Reinforcement Learning (RL), thus requiring a prohibitive number of experiments/simulations to converge to a solution with a (locally or globally) optimal reward. In this paper we address the process of simultaneously learning a DMP-characterized robot motion and its underlying joint couplings through linear Dimensionality Reduction (DR), which will provide valuable qualitative information leading to a reduced and intuitive algebraic description of such motion. The results in the experimental section show that not only can we effectively perform DR on DMPs while learning, but we can also obtain better learning curves, as well as additional information about each motion: linear mappings relating joint values and some latent variables.

Peer Reviewed