  Soft robots promise improved safety and capability over rigid robots when
deployed in complex, delicate, and dynamic environments. However, the infinite
degrees of freedom and highly nonlinear dynamics of these systems severely
complicate their modeling and control. As a step toward addressing this open
challenge, we apply the data-driven, Hankel Dynamic Mode Decomposition (HDMD)
with time delay observables to the model identification of a highly inertial,
helical soft robotic arm with a high number of underactuated degrees of
freedom. The resulting model is linear and hence amenable to control via a
Linear Quadratic Regulator (LQR). Using our test bed device, a dynamic,
lightweight pneumatic fabric arm with an inertial mass at the tip, we show that
the combination of HDMD and LQR allows us to command our robot to achieve
arbitrary poses using only open loop control. We further show that Koopman
spectral analysis gives us a dimensionally reduced basis of modes which
decreases computational complexity without sacrificing predictive power.
