Controlling a four degree of freedom arm in 3D using the XCSF learning classifier system

Abstract

This paper shows for the first time that a Learning Classifier System, namely XCSF, can learn to control a realistic arm model with four degrees of freedom in a three-dimensional workspace. XCSF learns a locally linear approximation of the Jacobian of the arm kinematics, that is, it learns linear predictions of hand location changes given joint angle changes, where the predictions are conditioned on current joint angles. To control the arm, the linear mappings are inverted-deriving appropriate motor commands given desired hand movement directions. Due to the locally linear model, the inversely desired joint angle changes can be easily derived, while effectively resolving kinematic redundancies on the fly. Adaptive PD controllers are used to finally translate the desired joint angle changes into appropriate motor commands. This paper shows that XCSF scales to three dimensional workspaces. It reliably learns to control a four degree of freedom arm in a three dimensional work space accurately and effectively while flexibly incorporating additional task constraints. © 2009 Springer Berlin Heidelberg.