  Anticipating what might happen as a result of an action is an essential
ability humans have in order to perform tasks effectively. On the other hand,
robots capabilities in this regard are quite lacking. While machine learning is
used to increase the ability of prospection it is still limiting for novel
situations. A possibility to improve the prospection ability of robots is
through simulation of imagined motions and the physical results of these
actions. Therefore, we present URoboSim, a robot simulator that allows robots
to perform tasks as mental simulation before performing this task in reality.
We show the capabilities of URoboSim in form of mental simulations, generating
data for machine learning and the usage as belief state for a real robot.
