物理シミュレーションを用いた3D変形物体の把持生成
Towards synthesizing grasps for 3D deformable objects with physics-based simulation
物理ベースシミュレータで生成した合成データのみで学習し、物体の剛性に応じて把持を適応させるディープラーニング手法を提案した論文。
著者: Tran Nguyen Le, Jens Lundell, Fares J. Abu-Dakka, Ville Kyrki
分類: cs.RO
原文アブストラクト
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.
