Towards synthesizing grasps for 3D deformable objects with physics-based simulation
This work addresses a gap in robotic grasping research for deformable objects, which is important for applications like handling soft materials, though it appears incremental as it builds on existing simulation capabilities.
The paper tackles the problem of robotic grasping for deformable objects by proposing a deep-learning approach that generates stiffness-dependent grasps, showing improvements in grasp ranking and success rate using synthetic data from a physics-based simulator.
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.