DefGraspSim: Physics-based simulation of grasp outcomes for 3D deformable objects
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.
Cite
@article{arxiv.2203.11274,
title = {DefGraspSim: Physics-based simulation of grasp outcomes for 3D deformable objects},
author = {Isabella Huang and Yashraj Narang and Clemens Eppner and Balakumar Sundaralingam and Miles Macklin and Ruzena Bajcsy and Tucker Hermans and Dieter Fox},
journal= {arXiv preprint arXiv:2203.11274},
year = {2022}
}
Comments
For associated web page, see \url{https://sites.google.com/nvidia.com/defgraspsim}. To be published in the IEEE Robotics and Automation Letters (RA-L) special issue on Robotic Handling of Deformable Objects, 2022. arXiv admin note: substantial text overlap with arXiv:2107.05778