Data-driven Kinematic Modeling in Soft Robots: System Identification and Uncertainty Quantification
This work addresses the underexplored issue of uncertainty quantification in kinematic modeling for soft robots, which is critical for calibration and controller design, representing an incremental improvement by applying conformal prediction to an existing domain.
The paper tackled the problem of prediction uncertainty in data-driven kinematic modeling for soft robots, which can affect modeling accuracy, by developing a conformal kinematic modeling framework that quantifies predictive position uncertainty with theoretical guarantees.
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.