Learning sequences with Neural Gas for robot motion planning
Ignazio Aleo, Paolo Arena, Luca Patané · 2009
The aim of this paper is to investigate novel solutions for motion sequence learning based on an extension of the Neural Gas with local Principal Component Analy-sis (NGPCA) algorithm. As an abstract Recurrent Neu-ral Network (RNN), this model is able to complete a partially given pattern. Under this point of view it is possible to generalize the model as a dynamical system in which for a given actual configuration and a particu-lar task the desired state variables are retrieved as out-puts converging to a particular state iteratively. The de-veloped architecture has been tested in the control of a redundant manipulator in simple forward and inverse kinematic problem solving and in motion sequence re-production. Key words Robotics, sequence learning, RNN, PCA, Neural Gas