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To control the motion of a humanoid robot along a desired trajectory in contact with a rigid object, we need to take into account forces that arise from contact with the surface of the object. In this paper we propose a new method that enables the robot to adapt its motion to different surfaces. The initial trajectories are encoded by dynamic movement primitives, which can be learned from visual feedback using a two-layered imitation system. In our approach these initial trajectories are modified using regression methods. The data for learning is provided by force feedback. In this way new trajectories are learned that ensure that the robot can move along the object while maintaining contact and applying the desired force to the object. Active compliance can be used more effectively with such trajectories. We present the results for both movement imitation and force profile learning on two different surfaces. We applied the method to the ARMAR-IIIb humanoid robot, where we use the system for learning and imitating a periodic task of wiping a kitchen table.