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

TL;DR

This paper introduces a physics-based simulation framework for evaluating robotic grasps on 3D deformable objects, providing a large dataset and open-source tools to advance research in deformable object manipulation.

Contribution

It presents a GPU-accelerated FEM simulation method for deformable objects, along with a comprehensive dataset and open-source code for grasp evaluation.

Findings

Good correspondence between simulated and real grasp outcomes

Large dataset with 1.1 million grasp measurements available

Open-source tools facilitate future research in deformable object grasping

Abstract

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.