  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/ .
