Data from: Active learning design: Modeling force output for axisymmetric soft pneumatic actuators

Soft pneumatic actuators (SPA) made from elastomeric materials can provide
large strain and large force. The behavior of locally strain-restricted
hyperelastic materials under inflation has been investigated thoroughly
for shape reconfiguration, but requires further investigation for
trajectories involving external force. In this work, we model
force-pressure-height relationships for a concentrically strain-limited
class of soft pneumatic actuators and demonstrate the use of this model to
design SPA response for object lifting. We predict relationships under
different loadings by solving energy minimization equations and verify
this theory by using an automated test rig to collect rich data for n = 22
Ecoflex 00-30 membranes. We collect data using an active learning pipeline
to efficiently model the design space. We show that this learned model
outperforms the theory-based model and a naive regression. We use our
model to optimize membrane design for different lift tasks and compare
this performance to other designs. These contributions represent a step
towards understanding the natural response for this class of actuator and
embodying intelligent lifts in a single-pressure input actuator system.