Robotic manipulation of textiles is a very hard task that is receiving lot of attention from the vision and robotics research community. It is hard because perception of textiles should account for many different configurations and varying shapes, and manipulation differs a lot from classical object manipulation in precision and ability. This Master Thesis simplifies the perception part to concentrate in the manipulation problem. Real experiments are performed using a TIAGo robot and custom made grippers. We first realize that manipulation skills require a rather high precision in the positioning, so the hand-eye calibration of the robot is evaluated in the task of grasping a folded cloth. Second, several baseline skills (simple grasps and motions in open loop) are developed and combined to construct two different complex manipulations: spreading a tablecloth and folding a towel. Finally, the manipulations are evaluated using a new proposed benchmark.