Using a context-sensitive learning for robot arm control

A class of networks called context-sensitive learning networks is proposed for use in the learning of complex nonlinear mappings. Particular attention is given to a network architecture for learning to control a robot arm by learning independently the different entries of the inverse Jacobian matrix. Computer simulation results show that the network is able to learn the inverse Jacobian of the PUMA 560 arm for inverse kinematic control. The network also generalized well when unseen testing examples are presented to it