  Being aware of our body has great importance in our everyday life. This is
the reason why we know how to move in a dark room or to grasp a complex object.
These skills are important for robots as well, however, robotic bodily
awareness is still an unsolved problem. In this paper we present a novel method
to implement bodily awareness in soft robots by the integration of
exteroceptive and proprioceptive sensors. We use a combination of a stacked
convolutional autoencoder and a recurrent neural network to map internal
sensory signals to visual information. As a result, the simulated soft robot
can learn to \textit{imagine} its motion even when its visual sensor is not
available.
