Modeling and Control of a Pneumatic Soft Robotic Catheter Using Neural Koopman Operators

TL;DR

This paper introduces a neural Koopman operator framework for modeling and controlling soft robotic catheters, achieving high accuracy in position and orientation without continuous imaging, thus enhancing cardiac intervention procedures.

Contribution

It presents a novel neural network-enhanced Koopman operator method that jointly learns system dynamics and control strategies for soft robotic catheters in a data-driven, end-to-end manner.

Findings

Achieved 2.1 mm position error and 4.9° orientation error in experiments.

Outperformed traditional model-based and Koopman variants in accuracy.

Validated in both position control and simulated cardiac ablation scenarios.

Abstract

Catheter-based interventions are widely used for the diagnosis and treatment of cardiac diseases. Recently, robotic catheters have attracted attention for their ability to improve precision and stability over conventional manual approaches. However, accurate modeling and control of soft robotic catheters remain challenging due to their complex, nonlinear behavior. The Koopman operator enables lifting the original system data into a linear "lifted space", offering a data-driven framework for predictive control; however, manually chosen basis functions in the lifted space often oversimplify system behaviors and degrade control performance. To address this, we propose a neural network-enhanced Koopman operator framework that jointly learns the lifted space representation and Koopman operator in an end-to-end manner. Moreover, motivated by the need to minimize radiation exposure during X-ray fluoroscopy in cardiac ablation, we investigate open-loop control strategies using neural Koopman operators to reliably reach target poses without continuous imaging feedback. The proposed method is validated in two experimental scenarios: interactive position control and a simulated cardiac ablation task using an atrium-like cavity. Our approach achieves average errors of 2.1 +- 0.4 mm in position and 4.9 +- 0.6 degrees in orientation, outperforming not only model-based baselines but also other Koopman variants in targeting accuracy and efficiency. These results highlight the potential of the proposed framework for advancing soft robotic catheter systems and improving catheter-based interventions.