  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. In this work, we
efficiently 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. We
also provide a detailed analysis on 6 object primitives. For each primitive, we
methodically describe the effects of different grasp strategies, compute a set
of performance metrics (e.g., deformation, stress) that fully capture the
object response, and identify simple grasp features (e.g., gripper
displacement, contact area) measurable by robots prior to pickup and predictive
of these performance metrics. Finally, we demonstrate good correspondence
between grasps on simulated objects and their real-world counterparts.
