  Successful soft robot modeling approaches appearing in recent literature have
been based on a variety of distinct theories, including traditional robotic
theory, continuum mechanics, and machine learning. Though specific modeling
techniques have been developed for and validated against already realized
systems, their strengths and weaknesses have not been explicitly compared
against each other. In this paper, we show how three distinct model structures
---a lumped-parameter model, a continuum mechanical model, and a neural
network--- compare in capturing the gross trends and specific features of the
force generation of soft robotic actuators. In particular, we study models for
Fiber Reinforced Elastomeric Enclosures (FREEs), which are a popular choice of
soft actuator and that are used in several soft articulated systems, including
soft manipulators, exoskeletons, grippers, and locomoting soft robots. We
generated benchmark data by testing eight FREE samples that spanned broad
design and kinematic spaces and compared the models on their ability to predict
the loading-deformation relationships of these samples. This comparison shows
the predictive capabilities of each model on individual actuators and each
model's generalizability across the design space. While the neural net achieved
the highest peak performance, the first principles-based models generalized
best across all actuator design parameters tested. The results highlight the
essential roles of mathematical structure and experimental parameter
determination in building high-performing, generalizable soft actuator models
with varying effort invested in system identification.
