Many manipulators at work in factories today repeat their motions over and over in cycles and if there are errors in following the trajectory these errors will also be repeated cycle after cycle. The basic idea behind iterative learning control (ILC) is that the controller should learn from previous cycles and perform better every cycle. Iterative learning control is used in combination with conventional feed-back and feed-forward control, and it is shown that learning control signal can handle the effects of unmodeled dynamics and friction. Convergence and disturbance effects as well as the choice of filters in the updating scheme are also addressed.