  Autonomous systems face the intricate challenge of navigating unpredictable
environments and interacting with external objects. The successful integration
of robotic agents into real-world situations hinges on their perception
capabilities, which involve amalgamating world models and predictive skills.
Effective perception models build upon the fusion of various sensory modalities
to probe the surroundings. Deep learning applied to raw sensory modalities
offers a viable option. However, learning-based perceptive representations
become difficult to interpret. This challenge is particularly pronounced in
soft robots, where the compliance of structures and materials makes prediction
even harder. Our work addresses this complexity by harnessing a generative
model to construct a multi-modal perception model for soft robots and to
leverage proprioceptive and visual information to anticipate and interpret
contact interactions with external objects. A suite of tools to interpret the
perception model is furnished, shedding light on the fusion and prediction
processes across multiple sensory inputs after the learning phase. We will
delve into the outlooks of the perception model and its implications for
control purposes.
