2021/07/19 by Tran Nguyen Le, Jens Lundell, Le, Tran Nguyen +6
Computer Science · Engineering · #FOS: Computer and information sciences #Robot Manipulation and Learning #Robotic Mechanisms and Dynamics #Robotics (cs.RO) #Teleoperation and Haptic Systems #cs.RO
paper · pdf · doi:10.48550/arxiv.2107.08898
4 pages, 5 figures. Published at Robotics: Science and Systems (RSS) 2021 Workshop on Deformable Object Simulation (DO-Sim)
arxiv created 2021/07/19 · openalex publication_date 2021/07/19 · arxiv updated 2021/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.