A general internal model approach for motion learning

Abstract

In this article, we present a general internal model (GIM) approach for motion skill learning at the elementary level and coordination level. In the past, internal models with two different configurations are used to describe the two classes of Dynamic Movement Primitives (DMPs): discrete and rhythmic movement. In this work, we developed a unified internal model which can describe both classes of DMPs. In particular, a discrete movement can be modeled as a fraction of a rhythmic movement. The general internal model retains the temporal and spatial scalabilities which are defined as the ability to generate similar movement patterns directly by means of tuning some parameters of the internal model. The advantage of scalability lies in that the learning or training process can be avoided while dealing with similar tasks. Complex motions require movement coordinations, hence coordination of multiple internal models. In the general internal model approach, the coordination is implemented with appropriate phase shifts among multiple internal models. Further in the GIM, the phase shift can be achieved by means of adjusting the initial state values of internal models. Through two illustrative examples, we show that the human behavior patterns with single or multiple limbs can be easily learned and established by the GIM at elementary and coordination levels. © 2007 IEEE.