Action extraction from teleoperated robots can be a crucial step in the direction of full -or shared- autonomy of tasks where human experience is indispensable. This is especially important in tasks that seek dynamic goals, where a human operator needs more control on how the machine behaves to provide assistance or perform a task. Open-die forging is a basic metal-forming process that lacks non-destructive product quality measures. Human experience is therefore imperative. During the process, a robot-arm is operated to place the work-piece between the dies of the forge where it is striked several times to reach a specific geometry. In this paper, we apply a white-box computer vision technique to discretize open-die forging robot-arm teleoperation data into actions as a step in learning the operator's behavior.