Neural Operators for Design-Space Surrogate Modeling of Tendon-Actuated Continuum Robots
Branden Frieden, James Ferguson, Alan Kuntz, Varun Shankar
AI summary
Problem
Traditional physics-based models for tendon-driven continuum robots are computationally expensive and struggle with unmodeled effects, while current learning-based methods are typically design-specific and fail to generalize across different robot geometries or material properties.
Approach
The authors formulate surrogate modeling as an operator learning problem and train four novel neural operator architectures (two DeepONets and two FNOs) on Cosserat rod simulation data to map robot design parameters and tendon tensions directly to equilibrium configurations.
Key results
- Four novel neural operator architectures (DeepONet and FNO variants) for mapping design parameters to configurations
- High accuracy and fast inference on unseen robot designs without retraining
- Robust generalization across varying tendon routing, backbone stiffness, and geometry
- Mathematical formulation bridging Cosserat rod mechanics and operator learning
Why it matters
Enables real-time control, rapid design optimization, and efficient simulation for surgical and industrial continuum robots by replacing costly physics-based solvers with a single, generalizable learned model.
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.