Sim-to-Real for Soft Robots using Differentiable FEM: Recipes for Meshing, Damping, and Actuation
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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
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2022.
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Citing papers per year: 2023: 3; 2024: 1; 2025: 1; 2026: 4.
9 papers.
- 2023Orbit: A Unified Simulation Framework for Interactive Robot Learning Environmentscited by 239
- 2024Neural Material Adaptor for Visual Grounding of Intrinsic Dynamicscited by 30
- 2023Differentiable Visual Computing for Inverse Problems and Machine Learningcited by 7
- 2023Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robotscited by 2
- 2025Learning Soft Robotic Dynamics with Active Explorationcited by 2
- 2026Vid2Sid: Videos Can Help Close the Sim2Real Gapcited by 0
- 2026RAFL: Generalizable Sim-to-Real of Soft Robots with Residual Acceleration Field Learningcited by 0
- 2026Learning Implicit Constitutive Laws for Dynamic 3D Gaussian Splatting from Monocular Videoscited by 0
- 2026Shaping the Evolutionary Dynamics of Robot Morphology via Adaptive Control Learningcited by 0
- 2026Shaping the Evolutionary Dynamics of Robot Morphology via Adaptive Control Learning24 Aug 2026
- 2026Learning Implicit Constitutive Laws for Dynamic 3D Gaussian Splatting from Monocular Videos22 Aug 2026
- 2026RAFL: Generalizable Sim-to-Real of Soft Robots with Residual Acceleration Field Learning23 Mar 2026
- 2026Vid2Sid: Videos Can Help Close the Sim2Real Gap22 Feb 2026
- 2025Learning Soft Robotic Dynamics with Active Exploration31 Oct 2025
- 2024Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics10 Oct 2024
- 2023Differentiable Visual Computing for Inverse Problems and Machine Learning21 Nov 2023
- 2023Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots7 Mar 2023
- 2023Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments10 Jan 2023