  Grasping deformable objects is not well researched due to the complexity in
modelling and simulating the dynamic behavior of such objects. However, with
the rapid development of physics-based simulators that support soft bodies, the
research gap between rigid and deformable objects is getting smaller. To
leverage the capability of such simulators and to challenge the assumption that
has guided robotic grasping research so far, i.e., object rigidity, we proposed
a deep-learning based approach that generates stiffness-dependent grasps. Our
network is trained on purely synthetic data generated from a physics-based
simulator. The same simulator is also used to evaluate the trained network. The
results show improvement in terms of grasp ranking and grasp success rate.
Furthermore, our network can adapt the grasps based on the stiffness. We are
currently validating the proposed approach on a larger test dataset in
simulation and on a physical robot.
