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Legged robots, such as the Sony AIBO, create opportunity to design rich motions to be executed in specific situations. In particular, teams involved in robot soccer RoboCup competitions have developed many different motions for kicking the ball. Designing effective motions and determining their effects is a challenging problem that is traditionally approached through a generate and test methodology. In this paper, we present a method we developed for learning the effects of kicking motions. Our procedure acquires models of the kicks in terms of key values that describe their effects on the ball's trajectory, namely the angle and the distance reached. The successful automated acquisition of the models of different kicks is then followed by the incorporation of these models into the behaviors to select the most promising kick in a given state of the world. Using the robot soccer domain, we demonstrate that a robot that takes into account the learned predicted effects of its actions performs significantly better than its counterpart.