The digital implementation of two repetitive control schemes on a SCARA-type two-axes manipulator is described. The controllers consist of a linear proportional and derivative feedback term and a feedforward repetitive term. The repetitive feedforward is adjusted periodically by using a linear combination of the position and velocity tracking errors of the current and/or previous cycles. Explicit knowledge of the structure of the manipulator model is not required. Two different learning algorithms for updating the feedforward repetitive control action are investigated. A simulation is first conducted to evaluate the performance of the repetitive controllers. The implementation of the controllers on the Berkeley/NSK SCARA robot arm is then discussed. Simulation and implementation results are in agreement and show that the use of the repetitive feedforward action in the control scheme significantly improves the tracking performance of the robot.<>