# Data-driven Methods Applied to Soft Robot Modeling and Control: A Review

arXiv: https://arxiv.org/abs/2305.12137 · HTML: https://mlsanity.com/paper/2305.12137/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2023.

## Built on by (most cited first, 13 of 13)

- [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)
- [Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions](https://mlsanity.com/paper/2504.15327/) (2025)
- [Learning Soft Robotic Dynamics with Active Exploration](https://mlsanity.com/paper/2510.27428/) (2025)
- [ICODE: Modeling Dynamical Systems with Extrinsic Input Information](https://mlsanity.com/paper/2411.13914/) (2024)
- [Hysteresis-Aware Neural Network Modeling and Whole-Body Reinforcement Learning Control of Soft Robots](https://mlsanity.com/paper/2504.13582/) (2025)
- [Toward Autonomous Soft Robotic Endovascular Navigation via Imitation Learning](https://mlsanity.com/paper/2510.09497/) (2025)
- [Efficient Model-Based Reinforcement Learning for Robot Control via Online Optimization](https://mlsanity.com/paper/2510.18518/) (2025)
- [A Flow Matching Framework for Soft-Robot Inverse Dynamics](https://mlsanity.com/paper/2604.03006/) (2026)
- [Thompson Sampling-Based Learning and Control for Unknown Dynamic Systems](https://mlsanity.com/paper/2506.22186/) (2025)
- [DiSA-IQL: Offline Reinforcement Learning for Robust Soft Robot Control under Distribution Shifts](https://mlsanity.com/paper/2510.00358/) (2025)
- [A Quantitative Comparison of Centralised and Distributed Reinforcement Learning-Based Control for Soft Robotic Arms](https://mlsanity.com/paper/2511.02192/) (2025)
- [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)

Soft robots show compliance and have infinite degrees of freedom. Thanks to these properties, such robots can be leveraged for surgery, rehabilitation, biomimetics, unstructured environment exploring, and industrial grippers. In this case, they attract scholars from a variety of areas. However, nonlinearity and hysteresis effects also bring a burden to robot modeling. Moreover, following their flexibility and adaptation, soft robot control is more challenging than rigid robot control. In order to model and control soft robots, a large number of data-driven methods are utilized in pairs or separately. This review first briefly introduces two foundations for data-driven approaches, which are physical models and the Jacobian matrix, then summarizes three kinds of data-driven approaches, which are statistical method, neural network, and reinforcement learning. This review compares the modeling and controller features, e.g., model dynamics, data requirement, and target task, within and among these categories. Finally, we summarize the features of each method. A discussion about the advantages and limitations of the existing modeling and control approaches is presented, and we forecast the future of data-driven approaches in soft robots. A website (https://sites.google.com/view/23zcb) is built for this review and will be updated frequently.