Efficient movement representation by embedding Dynamic Movement Primitives in deep autoencoders

Abstract

Predictive modeling of human or humanoid movement becomes increasingly complex as the dimensionality of those movements grows. Dynamic Movement Primitives (DMP) have been shown to be a powerful method of representing such movements, but do not generalize well when used in configuration or task space. To solve this problem we propose a model called autoencoded dynamic movement primitive (AE-DMP) which uses deep autoencoders to find a representation of movement in a latent feature space, in which DMP can optimally generalize. The architecture embeds DMP into such an autoencoder and allows the whole to be trained as a unit. To further improve the model for multiple movements, sparsity is added for the feature layer neurons; therefore, various movements can be observed clearly in the feature space. After training, the model finds a single hidden neuron from the sparsity that can efficiently generate new movements. Our experiments clearly demonstrate the efficiency of missing data imputation using 50-dimensional human movement data.