Neural Operators for Design-Space Surrogate Modeling of Tendon-Actuated Continuum Robots

2026-05-18

AI Summary

This study addresses the lack of an efficient, high-fidelity unified modeling framework for tendon-driven continuum robots across diverse designs by formulating surrogate modeling as an operator learning task. For the first time in this domain, neural operators are introduced, leveraging DeepONet and Fourier Neural Operator (FNO) architectures to develop four novel models that directly map design parameters and tendon inputs to robot configurations. Experimental results demonstrate that the proposed models achieve high accuracy and real-time prediction performance across unseen design configurations, significantly enhancing the efficiency of control, motion planning, and design optimization. These findings validate the effectiveness and generalization capability of operator learning for applications in both surgical and industrial continuum robotics.

Abstract

Continuum robots enable dexterous manipulation in constrained environments, but require accurate and efficient models for real-time manipulation and control. Traditional physics-based models can be computationally expensive and may suffer from inaccuracies due to unmodeled effects, while current learning-based methods often generalize poorly beyond the specific robot on which they are trained. We present a formulation of surrogate modeling for tendon-driven continuum robots as an operator learning problem that maps robot design parameters and tendon actuation inputs to resulting configurations. This formulation enables a single trained model to generalize across a large class of robot designs. We develop four novel neural operator architectures--two based on Deep Operator Networks (DeepONets) and two based on Fourier Neural Operators (FNOs)--and train them on simulation data to predict robot configurations. All architectures achieve good accuracy while allowing for fast and accurate generalization across designs. Our results demonstrate that operator learning provides an effective and generalizable surrogate for continuum robot mechanics in the design space, enabling fast modeling for control, planning, and design optimization in surgical and industrial applications.

Problem

• continuum robots
• surrogate modeling
• design-space generalization
• tendon-actuated
• real-time control

Innovation

• neural operators
• surrogate modeling
• continuum robots
• operator learning
• design-space generalization