# Soft Robots Modeling: a Structured Overview

arXiv: https://arxiv.org/abs/2112.03645 · HTML: https://mlsanity.com/paper/2112.03645/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2021.

## Built on by (most cited first, 19 of 19)

- [Controlling diverse robots by inferring Jacobian fields with deep networks](https://mlsanity.com/paper/2407.08722/) (2024)
- [Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems](https://mlsanity.com/paper/2312.06256/) (2023)
- [Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space](https://mlsanity.com/paper/2409.08439/) (2024)
- [Learning Low-Dimensional Strain Models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control](https://mlsanity.com/paper/2411.00138/) (2024)
- [Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networks](https://mlsanity.com/paper/2502.01916/) (2025)
- [Adaptive Model-Predictive Control of a Soft Continuum Robot Using a Physics-Informed Neural Network Based on Cosserat Rod Theory](https://mlsanity.com/paper/2508.12681/) (2025)
- [Multi-modal perception for soft robotic interactions using generative models](https://mlsanity.com/paper/2404.04220/) (2024)
- [Domain Translation of a Soft Robotic Arm using Conditional Cycle Generative Adversarial Network](https://mlsanity.com/paper/2508.14100/) (2025)
- [Bridging Embodiment Gaps: Deploying Vision-Language-Action Models on Soft Robots](https://mlsanity.com/paper/2510.17369/) (2025)
- [ManiSoft: Towards Vision-Language Manipulation for Soft Continuum Robotics](https://mlsanity.com/paper/2605.18617/) (2026)
- [Soft Robotics for Search and Rescue: Advancements, Challenges, and Future Directions](https://mlsanity.com/paper/2502.12373/) (2025)
- [Deformable Linear Object Surface Placement Using Elastica Planning and Local Shape Control](https://mlsanity.com/paper/2503.08545/) (2025)
- [Data-driven Kinematic Modeling in Soft Robots: System Identification and Uncertainty Quantification](https://mlsanity.com/paper/2507.07370/) (2025)
- [Zero-shot Whole-Body Manipulation with a Large-Scale Soft Robotic Torso via Guided Reinforcement Learning](https://mlsanity.com/paper/2509.23556/) (2025)
- [Frequency-aware decomposition learning for sensorless wrench estimation in vibration-rich robotic contact](https://mlsanity.com/paper/2604.12905/) (2026)
- [Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations](https://mlsanity.com/paper/2606.08104/) (2026)
- [SoRoMoX: Fast, Differentiable, and Parallelizable Soft Robot Models](https://mlsanity.com/paper/2608.06650/) (2026)
- [Constraint-Aware Physics-Informed Neural Networks for Static Shape Estimation of Co-Manipulative Continuum Robots](https://mlsanity.com/paper/2608.26273/) (2026)
- [History-Conditioned Flow Matching for Probabilistic Dynamics of Tendon-Driven Continuum Robots](https://mlsanity.com/paper/2609.25658/) (2026)

## Abstract (arXiv)

The robotics community has seen an exponential growth in the level of complexity of the theoretical tools presented for the modeling of soft robotics devices. Different solutions have been presented to overcome the difficulties related to the modeling of soft robots, often leveraging on other scientific disciplines, such as continuum mechanics, computational mechanics and computer graphics. These theoretical and computational foundations are often taken for granted and this leads to an intricate literature that, consequently, has rarely been the subject of a complete review. For the first time, we present here a structured overview of all the approaches proposed so far to model soft robots. The chosen classification, which is based on their theoretical and numerical grounds, allows us to provide a critical analysis about their uses and applicability. This will enable robotics researchers to learn the basics of these modeling techniques and their associated numerical methods, but also to have a critical perspective on their uses.