  Perception is essential for the active interaction of physical agents with
the external environment. The integration of multiple sensory modalities, such
as touch and vision, enhances this perceptual process, creating a more
comprehensive and robust understanding of the world. Such fusion is
particularly useful for highly deformable bodies such as soft robots.
Developing a compact, yet comprehensive state representation from multi-sensory
inputs can pave the way for the development of complex control strategies. This
paper introduces a perception model that harmonizes data from diverse
modalities to build a holistic state representation and assimilate essential
information. The model relies on the causality between sensory input and
robotic actions, employing a generative model to efficiently compress fused
information and predict the next observation. We present, for the first time, a
study on how touch can be predicted from vision and proprioception on soft
robots, the importance of the cross-modal generation and why this is essential
for soft robotic interactions in unstructured environments.
