Simplifying Data-Driven Modeling of the Volume–Flow–Pressure Relationship in Hydraulic Soft Robotic Actuators: A Practical and Balanced Solution
Abstract
Soft robotic systems are known for their flexibility and adaptability, but traditional physics-based models struggle to capture their complex, nonlinear behaviors. This study explores a data-driven approach to modeling the volume-flowpressure relationship in hydraulic soft actuators, focusing on lowcomplexity models with high accuracy. We perform regression analysis on a stacked balloon actuator system using exponential, polynomial, and neural network models with or without autoregressive inputs. The results demonstrate that simpler models, particularly multivariate polynomials, effectively predict pressure dynamics with fewer parameters. This research offers a practical solution for real-time soft robotics applications, balancing model complexity and computational efficiency. Moreover, the approach may benefit various techniques that require explicit analytical models.
Fields of Study & Subjects
Soft Robotics and Applications
Model Reduction and Neural Networks
Micro and Nano Robotics
Publication details
- Journal / source
- IEEE Robotics & Automation Magazine
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 33
- Issue
- 1
- Pages
- 115–129
- Publication date
- 12 January 2026
- Language
- EN