Experiments on repositioning control of robots by learning

Experimental results on the repositioning of a rigid robot link from one equilibrium point to another are presented. By means of a learning algorithm, which uses only the positioning error at the end of a trial, the control torque needed to move the robot is found. After a high gain feedback linearization of the robot dynamics, a simple, finite dimensional learning algorithm is applied. The results of the experiments, carried out on a two link robot under gravity, show that the algorithm is robust and performs quite satisfactorily