A general internal model approach for motion learning

Abstract

In this paper, we present a general internal model (GIM) approach for motion skill learning at elementary and coordination levels. A unified internal model (IM) is developed for describing discrete and rhythmic movements. Through analysis, we show that the GIM possesses temporal and spatial scalabilities which are defined as the ability to generate similar movement patterns directly by means of tuning some parameters of the IM. With scalability, the learning or training process can be avoided when dealing with similar tasks. The coordination is implemented in the GIM with appropriate phase shifts among multiple IMs under an overall architecture. To facilitate the establishment of the GIM, in this paper, we further explored algorithms for detecting periodicity of and phase difference between rhythmic movements, and neural network structures suitable for learning motion patterns. Through three illustrative examples, we show that the human behavior patterns with single or multiple limbs can be easily learned and established by the GIM at the elementary and coordination levels. © 2008 IEEE.