  Many soft-body organisms found in nature flourish underwater. Similarly, soft
robots are potentially well-suited for underwater environments partly because
the problematic effects of gravity, friction, and harmonic oscillations are
less severe underwater. However, it remains a challenge to design, fabricate,
waterproof, model, and control underwater soft robotic systems. Furthermore,
submersible robots usually do not have configurable components because of the
need for sealed electronics and mechanical elements. This work presents the
development of a modular and submersible soft robotic arm driven by hydraulic
actuators which consists of mostly 3D printable parts which can be assembled or
modified in a relatively short amount of time. Its modular design enables
multiple shape configurations and easy swapping of soft actuators. As a first
step to exploring machine learning control algorithms on this system, we also
present preliminary forward and inverse kinematics models developed using deep
neural networks.
