  Soft robots are typically approximated as low-dimensional systems, especially
when learning-based methods are used. This leads to models that are limited in
their capability to predict the large number of deformation modes and
interactions that a soft robot can have. In this work, we present a
deep-learning methodology to learn high-dimensional visual models of a soft
robot combining multimodal sensorimotor information. The models are learned in
an end-to-end fashion, thereby requiring no intermediate sensor processing or
grounding of data. The capabilities and advantages of such a modelling approach
are shown on a soft anthropomorphic finger with embedded soft sensors. We also
show that how such an approach can be extended to develop higher level
cognitive functions like identification of the self and the external
environment and acquiring object manipulation skills. This work is a step
towards the integration of soft robotics and developmental robotics
architectures to create the next generation of intelligent soft robots.
