  Learning meaningful and compact representations with disentangled semantic
aspects is considered to be of key importance in representation learning. Since
real-world data is notoriously costly to collect, many recent state-of-the-art
disentanglement models have heavily relied on synthetic toy data-sets. In this
paper, we propose a novel data-set which consists of over one million images of
physical 3D objects with seven factors of variation, such as object color,
shape, size and position. In order to be able to control all the factors of
variation precisely, we built an experimental platform where the objects are
being moved by a robotic arm. In addition, we provide two more datasets which
consist of simulations of the experimental setup. These datasets provide for
the first time the possibility to systematically investigate how well different
disentanglement methods perform on real data in comparison to simulation, and
how simulated data can be leveraged to build better representations of the real
world. We provide a first experimental study of these questions and our results
indicate that learned models transfer poorly, but that model and hyperparameter
selection is an effective means of transferring information to the real world.
