Fig. 1 Grasping cables from cluttered scenes exploiting the grasping samples approach. The grasp candidates are obtained considering the force closure principle. a) CG-CNN estimates grasp quality of grasp candidates. Positive grasp candidates are in green: quality > 0.5; Negative grasp candidates are in red : quality < 0.5. b) Optimal grasp candidate. c) Physics simulation for grasping.

Fig. 2 illustrates the architectural framework of the cable grasping method integrating CG-CNN. CG-CNN is designed to acquire the capability to assess grasp quality based on synthetic depth data generated within a simulated environment. The determination of grasp quality is accomplished through the execution of physics-based grasping simulations conducted within complex and cluttered settings. To assess the performance of CG-CNN during its training phase, a total of 100 grasping simulation experiments are conducted within the simulation environment every 10 epochs, and the success rate of grasping within this simulated context is chosen as the evaluation metric. Ultimately, the trained model is deployed for real-world cable grasping within cluttered environments.