Data-driven Kinematic Modeling in Soft Robots: System Identification and Uncertainty Quantification
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
Precise kinematic modeling is critical in calibration and controller design
for soft robots, yet remains a challenging issue due to their highly nonlinear
and complex behaviors. To tackle the issue, numerous data-driven machine
learning approaches have been proposed for modeling nonlinear dynamics.
However, these models suffer from prediction uncertainty that can negatively
affect modeling accuracy, and uncertainty quantification for kinematic modeling
in soft robots is underexplored. In this work, using limited simulation and
real-world data, we first investigate multiple linear and nonlinear machine
learning models commonly used for kinematic modeling of soft robots. The
results reveal that nonlinear ensemble methods exhibit the most robust
generalization performance. We then develop a conformal kinematic modeling
framework for soft robots by utilizing split conformal prediction to quantify
predictive position uncertainty, ensuring distribution-free prediction
intervals with a theoretical guarantee.
