# Sim-to-Real for Soft Robots using Differentiable FEM: Recipes for Meshing, Damping, and Actuation

arXiv: https://arxiv.org/abs/2201.12560 · HTML: https://mlsanity.com/paper/2201.12560/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2022.

## Built on by (most cited first, 10 of 10)

- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics](https://mlsanity.com/paper/2410.08257/) (2024)
- [Differentiable Visual Computing for Inverse Problems and Machine Learning](https://mlsanity.com/paper/2312.04574/) (2023)
- [Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots](https://mlsanity.com/paper/2303.04136/) (2023)
- [Learning Soft Robotic Dynamics with Active Exploration](https://mlsanity.com/paper/2510.27428/) (2025)
- [Zero-shot Whole-Body Manipulation with a Large-Scale Soft Robotic Torso via Guided Reinforcement Learning](https://mlsanity.com/paper/2509.23556/) (2025)
- [Vid2Sid: Videos Can Help Close the Sim2Real Gap](https://mlsanity.com/paper/2602.19359/) (2026)
- [RAFL: Generalizable Sim-to-Real of Soft Robots with Residual Acceleration Field Learning](https://mlsanity.com/paper/2603.22039/) (2026)
- [Learning Implicit Constitutive Laws for Dynamic 3D Gaussian Splatting from Monocular Videos](https://mlsanity.com/paper/2608.22102/) (2026)
- [Shaping the Evolutionary Dynamics of Robot Morphology via Adaptive Control Learning](https://mlsanity.com/paper/2608.23100/) (2026)

## Abstract (arXiv)

An accurate, physically-based, and differentiable model of soft robots can unlock downstream applications in optimal control. The Finite Element Method (FEM) is an expressive approach for modeling highly deformable structures such as dynamic, elastomeric soft robots. In this paper, we compare virtual robot models simulated using differentiable FEM with measurements from their physical counterparts. In particular, we examine several soft structures with different morphologies: a clamped soft beam under external force, a pneumatically actuated soft robotic arm, and a soft robotic fish tail. We benchmark and analyze different meshing resolutions and elements (tetrahedra and hexahedra), numerical damping, and the efficacy of differentiability for parameter calibration using a simulator based on the fast Differentiable Projective Dynamics (DiffPD). We also advance FEM modeling in application to soft robotics by proposing a predictive model for pneumatic soft robotic actuation. Through our recipes and case studies, we provide strategies and algorithms for matching real-world physics in simulation, making FEM useful for soft robots