  Grasp synthesis for 3D deformable objects remains a little-explored topic,
most works aiming to minimize deformations. However, deformations are not
necessarily harmful -- humans are, for example, able to exploit deformations to
generate new potential grasps. How to achieve that on a robot is though an open
question. This paper proposes an approach that uses object stiffness
information in addition to depth images for synthesizing high-quality grasps.
We achieve this by incorporating object stiffness as an additional input to a
state-of-the-art deep grasp planning network. We also curate a new synthetic
dataset of grasps on objects of varying stiffness using the Isaac Gym simulator
for training the network. We experimentally validate and compare our proposed
approach against the case where we do not incorporate object stiffness on a
total of 2800 grasps in simulation and 420 grasps on a real Franka Emika Panda.
The experimental results show significant improvement in grasp success rate
using the proposed approach on a wide range of objects with varying shapes,
sizes, and stiffness. Furthermore, we demonstrate that the approach can
generate different grasping strategies for different stiffness values, such as
pinching for soft objects and caging for hard objects. Together, the results
clearly show the value of incorporating stiffness information when grasping
objects of varying stiffness.
