# DefGraspSim: Physics-based simulation of grasp outcomes for 3D deformable objects

arXiv: https://arxiv.org/abs/2203.11274 · HTML: https://mlsanity.com/paper/2203.11274/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2022.

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

- [Factory: Fast Contact for Robotic Assembly](https://mlsanity.com/paper/2205.03532/) (2022)
- [GIC: Gaussian-Informed Continuum for Physical Property Identification and Simulation](https://mlsanity.com/paper/2406.14927/) (2024)
- [Meta-Learning Regrasping Strategies for Physical-Agnostic Objects](https://mlsanity.com/paper/2205.11110/) (2022)
- [PokeFlex: A Real-World Dataset of Volumetric Deformable Objects for Robotics](https://mlsanity.com/paper/2410.07688/) (2024)
- [DOFS: A Real-world 3D Deformable Object Dataset with Full Spatial Information for Dynamics Model Learning](https://mlsanity.com/paper/2410.21758/) (2024)
- [SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable Objects (Early Version)](https://mlsanity.com/paper/2607.04234/) (2026)
- [Reinforcement Learning-Based Bionic Reflex Control for Anthropomorphic Robotic Grasping exploiting Domain Randomization](https://mlsanity.com/paper/2312.05023/) (2023)
- [Advances in Compliance Detection: Novel Models Using Vision-Based Tactile Sensors](https://mlsanity.com/paper/2506.14980/) (2025)
- [Prolonging Tool Life: Learning Skillful Use of General-purpose Tools through Lifespan-guided Reinforcement Learning](https://mlsanity.com/paper/2507.17275/) (2025)
- [Efficiently Linking Real Scenes with Synthetic Data Generation for AI-based Cognitive Robotics and Computer Vision Applications](https://mlsanity.com/paper/2606.20272/) (2026)

## Abstract (arXiv)

Robotic grasping of 3D deformable objects (e.g., fruits/vegetables, internal organs, bottles/boxes) is critical for real-world applications such as food processing, robotic surgery, and household automation. However, developing grasp strategies for such objects is uniquely challenging. Unlike rigid objects, deformable objects have infinite degrees of freedom and require field quantities (e.g., deformation, stress) to fully define their state. As these quantities are not easily accessible in the real world, we propose studying interaction with deformable objects through physics-based simulation. As such, we simulate grasps on a wide range of 3D deformable objects using a GPU-based implementation of the corotational finite element method (FEM). To facilitate future research, we open-source our simulated dataset (34 objects, 1e5 Pa elasticity range, 6800 grasp evaluations, 1.1M grasp measurements), as well as a code repository that allows researchers to run our full FEM-based grasp evaluation pipeline on arbitrary 3D object models of their choice. Finally, we demonstrate good correspondence between grasp outcomes on simulated objects and their real counterparts.