We introduce a Cable Grasping-Convolutional Neural Network designed to facilitate robust cable grasping in cluttered environments. Utilizing physics simulations, we generate an extensive dataset that mimics the intricacies of cable grasping, factoring in potential collisions between cables and robotic grippers. We employ the Approximate Convex Decomposition technique to dissect the non-convex cable model, with grasp quality autonomously labeled based on simulated grasping attempts. The CG-CNN is refined using this simulated dataset and enhanced through domain randomization techniques. Subsequently, the trained model predicts grasp quality, guiding the optimal grasp pose to the robot controller for execution. Grasping efficacy is assessed across both synthetic and real-world settings. Given our model implicit collision sensitivity, we achieved commendable success rates of 92.3% for known cables and 88.4% for unknown cables, surpassing contemporary state-of-the-art approaches. Supplementary materials can be found at https://leizhang-public.github.io/cg-cnn/ .

翻译：我们提出了一种线缆抓取卷积神经网络（CG-CNN），旨在实现在杂乱环境中稳健的线缆抓取。通过利用物理仿真，我们生成了一个大规模数据集，该数据模拟了线缆抓取的复杂性，并考虑了线缆与机器人夹爪之间的潜在碰撞。我们采用近似凸分解技术来分解非凸线缆模型，并根据仿真抓取尝试自动标注抓取质量。CG-CNN通过该仿真数据集进行训练，并结合域随机化技术增强性能。随后，训练完成的模型预测抓取质量，引导最优抓取姿态传递给机器人控制器执行。在合成场景和真实世界环境中的抓取效能评估显示：由于模型对碰撞的隐式敏感性，对于已知线缆和未知线缆的成功率分别达到92.3%和88.4%，超越了当前最先进的方法。补充材料详见 https://leizhang-public.github.io/cg-cnn/ 。