  Robot learning of real-world manipulation tasks remains challenging and time
consuming, even though actions are often simplified by single-step manipulation
primitives. In order to compensate the removed time dependency, we additionally
learn an image-to-image transition model that is able to predict a next state
including its uncertainty. We apply this approach to bin picking, the task of
emptying a bin using grasping as well as pre-grasping manipulation as fast as
possible. The transition model is trained with up to 42000 pairs of real-world
images before and after a manipulation action. Our approach enables two
important skills: First, for applications with flange-mounted cameras, picks
per hours (PPH) can be increased by around 15% by skipping image measurements.
Second, we use the model to plan action sequences ahead of time and optimize
time-dependent rewards, e.g. to minimize the number of actions required to
empty the bin. We evaluate both improvements with real-robot experiments and
achieve over 700 PPH in the YCB Box and Blocks Test.
