DefGraspSim: Physics-Based Simulation of Grasp Outcomes for 3D Deformable Objects

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...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 7; no. 3; pp. 6274 - 6281
Main Authors: Huang, Isabella, Narang, Yashraj, Eppner, Clemens, Sundaralingam, Balakumar, Macklin, Miles, Bajcsy, Ruzena, Hermans, Tucker, Fox, Dieter
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Deformable models
Deformation
Evaluation
Finite element analysis
Finite element method
Food processing
Formability
Grasping
Grasping (robotics)
Measurement
Reproducibility
Robotic surgery
Simulation
simulation and animation
software tools for benchmarking and reproducibility
Strain
Stress
Three dimensional models
Three-dimensional displays
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary: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, 1.1 M 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.
Bibliography:ObjectType-Article-1
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3158725