  Accurate simulation of soft mechanisms under dynamic actuation is critical
for the design of soft robots. We address this gap with our differentiable
simulation tool by learning the material parameters of our soft robotic fish.
On the example of a soft robotic fish, we demonstrate an
experimentally-verified, fast optimization pipeline for learning the material
parameters from quasi-static data via differentiable simulation and apply it to
the prediction of dynamic performance. Our method identifies physically
plausible Young's moduli for various soft silicone elastomers and stiff acetal
copolymers used in creation of our three different robotic fish tail designs.
We show that our method is compatible with varying internal geometry of the
actuators, such as the number of hollow cavities. Our framework allows high
fidelity prediction of dynamic behavior for composite bi-morph bending
structures in real hardware to millimeter-accuracy and within 3 percent error
normalized to actuator length. We provide a differentiable and robust estimate
of the thrust force using a neural network thrust predictor; this estimate
allows for accurate modeling of our experimental setup measuring bollard pull.
This work presents a prototypical hardware and simulation problem solved using
our differentiable framework; the framework can be applied to higher
dimensional parameter inference, learning control policies, and computational
design due to its differentiable character.
