A Binary Grasp Pose Discriminator with Attention-Bridging Based on Local Grasp Units

Investigating the methods of handling real-world objects with robotic arms is a crucial aspect of artificial intelligence research. By inputting images from cameras or other devices, neural networks extract suitable end-effector poses to achieve precise object manipulation. Current methods face challenges of inadequate regression precision and excessive classification, which negatively impact network performance. Thus, we propose a new method based on the local grasp unit. This approach transforms grasp prediction into evaluating whether the features of local grasp units are suitable for grasping. Stable local grasp units are selected by introducing a binary grasp pose discriminator with local point clouds as inputs. In addition, to tackle the issue of distant regions in point clouds of grasp pose discriminator, we propose the innovative Attention-Bridging Layer. The Attention-Bridging Layer selects representative points from different regions and enables information exchange between them, facilitating information flow between distant areas and enhancing the network’s performance.