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In this thesis we tackle the problem of goal-oriented adaptation of a robot hitting motion. We propose the parameters that must be learned in order to use and adapt a basic hitting motion to play minigolf. Then, two different statistical methods are used to learn these parameters. The two methods are evaluated and compared. To validate the proposed approach, a minigolf control module is developed for a robotic arm. Using the different learning techniques, we show that a robot can learn the non-trivial task of deciding how the ball should be hit for a given position on a minigolf field. The result is a robust minigolf-playing system that outperforms most human players using only a small set of training examples.