A regression model for Digital Representation of Juggling

The present paper presents a methodology to identify regularities in motion trajectories and encode them into a reduced order model. The model has been developed for being trained with real data captured during the execution of complex and articulated motions, having several phases each. The presented model possesses relevant features that make it adequate for motion representation, among these we will discuss: stability, generalization, optimization, adaptation and external dynamic synchronization. Practical examples taken from ball throwing and catching will be given.