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.
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我们介绍了一种用于在混乱环境中实现可靠电缆抓取的 CG-CNN 模型，该网络利用物理仿真生成了模拟电缆抓取的广泛数据集，通过利用近似凸分解技术解剖非凸电缆模型，并基于模拟抓取尝试自动标记抓取质量。通过对这个仿真数据集进行训练和领域随机化技术的改善，我们的模型可以预测抓取质量并指导机器人控制器执行最优的抓取姿势，该模型的抓取效果在合成和真实环境下得到了评估，成功率分别为 92.3% 和 88.4%，优于目前的技术水平。