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Direct imitation of human movement with a humanoid robot, which has a similar kinematic structure, does not guarantee a successful completion of the task because of different dynamical properties. Our research starts by showing how to apply a generalization algorithm to extract the desired movement primitives from multiple human demonstrations. The emphasis of the paper is on a method that constrains the extracted movement primitives when mapping them to a robot, taking into account a critical criterion of the task. As a practical example we study the stability of a robot, which is determined through a normalized zero-moment-point. Our approach is based on prioritized task control and allows direct movement transfer as long as the selected criterion is met. It only constrains the movement when the criterion approaches a critical condition. The critical condition thus triggers a reflexive, subconscious behavior, which has higher priority than the desired, conscious movement. We demonstrate the properties of the algorithm on a real, human inspired leg robot developed in our laboratory.