# Soft Robot Optimal Control Via Reduced Order Finite Element Models

arXiv: https://arxiv.org/abs/2011.02092 · HTML: https://mlsanity.com/paper/2011.02092/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Learning Soft Robotic Dynamics with Active Exploration](https://mlsanity.com/paper/2510.27428/) (2025)
- [Data-driven Model Reduction for Soft Robots via Lagrangian Operator Inference](https://mlsanity.com/paper/2407.08840/) (2024)
- [Soft Robotics for Search and Rescue: Advancements, Challenges, and Future Directions](https://mlsanity.com/paper/2502.12373/) (2025)
- [Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perception](https://mlsanity.com/paper/2605.05053/) (2026)

## Abstract (arXiv)

Finite element methods have been successfully used to develop physics-based models of soft robots that capture the nonlinear dynamic behavior induced by continuous deformation. These high-fidelity models are therefore ideal for designing controllers for complex dynamic tasks such as trajectory optimization and trajectory tracking. However, finite element models are also typically very high-dimensional, which makes real-time control challenging. In this work we propose an approach for finite element model-based control of soft robots that leverages model order reduction techniques to significantly increase computational efficiency. In particular, a constrained optimal control problem is formulated based on a nonlinear reduced order finite element model and is solved via sequential convex programming. This approach is demonstrated through simulation of a cable-driven soft robot for a constrained trajectory tracking task, where a 9768-dimensional finite element model is used for controller design.