# Comparison and Experimental Validation of Predictive Models for Soft, Fiber-Reinforced Actuators

## TL;DR

This paper compares three different modeling approaches for soft fiber-reinforced actuators, evaluating their accuracy and generalizability using experimental data from eight samples, highlighting the trade-offs between data-driven and physics-based models.

## Contribution

It provides a systematic comparison of lumped-parameter, continuum mechanical, and neural network models for FREEs, revealing their strengths and limitations in soft actuator modeling.

## Key findings

- Neural network achieved highest peak performance.
- Physics-based models generalized better across design space.
- Model choice impacts accuracy and generalizability in soft actuator prediction.

## Abstract

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.