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Reliable predictability is one of the main factors that determine human behaviors. The authors developed a model that searches and generates robot motions based on reliable predictability. Training of the model consists of three phases. In the first phase, the model trains a sequential learner, namely recurrent neural network with parametric bias, to self-organize robot and object dynamics. In the second phase, steepest descent method is utilized to search for robot motion that induces the most predictable object motion. In the third phase, a hierarchical neural network is trained to link object image with the searched motion. Experiments were conducted with cylindrical objects. Analysis of the results have shown that the robot has acquired the most reliable robot motion, shifting it according to the posture of the object. Twenty motion generation experiments have resulted in generation of robot motion that induces consistent rolling motion of the objects.