  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.
