Implementation of a neural network model for control in grasping a moving target
Roland S. L. Lim, Peter Horan, R.A. Jarvis · 2003
An approach to the control of a robot manipulator in grasping a simple moving target with constant speed is presented. A layered neural network architecture-based controller has been developed. It can automatically learn visual motor coordination for the fast reaching movements required in grasping a moving target. A learning scheme known as two-phase learning is described for teaching the skill to the controller. Learning in the controller is achieved through a sequence of trial movements without the presence of a 'teacher'. Visual feedback showing the action of the controller is used to adapt it so as to reduce the error between the target and the robot's gripper.>