Neural Operators for Continuum Robot Surrogate Modeling
A recent preprint on arXiv (2605.19104) presents neural operator architectures aimed at surrogate modeling for tendon-driven continuum robots. This method conceptualizes the challenge as operator learning, linking design parameters and tendon actuation inputs to the configurations of the robot. Four innovative architectures have been created: two derived from Deep Operator Networks (DeepONets) and two from Fourier Neural Operators (FNOs). Trained using simulation data, these models demonstrate strong predictive accuracy. A significant benefit is that a single trained model can effectively generalize across a wide range of robot designs, addressing the shortcomings of conventional physics-based models (which are costly and often inaccurate) and existing learning-based approaches (which struggle with generalization beyond specific robots).
Key facts
- arXiv:2605.19104v1
- Announce Type: cross
- Abstract: continuum robots enable dexterous manipulation in constrained environments
- Traditional physics-based models can be computationally expensive and suffer from inaccuracies
- Current learning-based methods often generalize poorly beyond the specific robot on which they are trained
- Presents a formulation of surrogate modeling for tendon-driven continuum robots as an operator learning problem
- Maps robot design parameters and tendon actuation inputs to resulting configurations
- Develops four novel neural operator architectures: two DeepONets and two FNOs
Entities
Institutions
- arXiv