  In this paper, we presented a data-driven framework to optimize the
out-of-plane stiffness for soft grippers to achieve mechanical properties as
hard-to-twist and easy-to-bend. The effectiveness of this method is
demonstrated in the design of a soft pneumatic bending actuator (SPBA). First,
a new objective function is defined to quantitatively evaluate the out-of-plane
stiffness as well as the bending performance. Then, sensitivity analysis is
conducted on the parametric model of an SPBA design to determine the optimized
design parameters with the help of Finite Element Analysis (FEA). To enable the
computation of numerical optimization, a data-driven approach is employed to
learn a cost function that directly represents the out-of-plane stiffness as a
differentiable function of the design variables. A gradient-based method is
used to maximize the out-of-plane stiffness of the SPBA while ensuring specific
bending performance. The effectiveness of our method has been demonstrated in
physical experiments taken on 3D-printed grippers.
