# Data-driven Model Reduction for Soft Robots via Lagrangian Operator Inference

arXiv: https://arxiv.org/abs/2407.08840 · HTML: https://mlsanity.com/paper/2407.08840/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Dynamic mode decomposition with control](https://mlsanity.com/paper/1409.6358/) (2014)
- [Model Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges](https://mlsanity.com/paper/2110.01358/) (2021)
- [Lagrangian operator inference enhanced with structure-preserving machine learning for nonintrusive model reduction of mechanical systems](https://mlsanity.com/paper/2404.05040/) (2024)
- [Discrete Cosserat Approach for Multi-Section Soft Robots Dynamics](https://mlsanity.com/paper/1702.03660/) (2017)
- [Nonlinear System Identification of Soft Robot Dynamics Using Koopman Operator Theory](https://mlsanity.com/paper/1810.06637/) (2018)
- [Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems](https://mlsanity.com/paper/2312.06256/) (2023)
- [Data-Driven Spectral Submanifold Reduction for Nonlinear Optimal Control of High-Dimensional Robots](https://mlsanity.com/paper/2209.05712/) (2022)
- [Soft Robot Optimal Control Via Reduced Order Finite Element Models](https://mlsanity.com/paper/2011.02092/) (2020)

## Built on by (most cited first, 1 of 1)

- [Structure-preserving Lift & Learn: Scientific machine learning for nonlinear conservative partial differential equations](https://mlsanity.com/paper/2507.00301/) (2025)

## Abstract (arXiv)

Data-driven model reduction methods provide a nonintrusive way of constructing computationally efficient surrogates of high-fidelity models for real-time control of soft robots. This work leverages the Lagrangian nature of the model equations to derive structure-preserving linear reduced-order models via Lagrangian Operator Inference and compares their performance with prominent linear model reduction techniques through an anguilliform swimming soft robot model example with 231,336 degrees of freedom. The case studies demonstrate that preserving the underlying Lagrangian structure leads to learned models with higher predictive accuracy and robustness to unseen inputs.