Robotic grasping is a fundamental skill for robots, but it is quite challenging in cluttered scenes. In cluttered scenes, the precise prediction of high-quality grasp configurations such as rotation and grasping width while avoiding collisions is essential. To accomplish this, the grasp detection models require the capabilities of stronger fine-grained information extracted around the grasp points. However, due to the computational resource restriction, point clouds are usually downsampled in existing networks, which inevitably make some potentially important points discarded. To overcome this problem, we propose a Grasp Region Exploration module to explore the area covered by high-quality grasps. Based on the grasp region, we enhance the point density around the grasp points to mitigate the loss of information caused by downsampling. Furthermore, we devise the Grasp Region Attention module to dynamically aggregate features of various points within the grasp region, such as the grasp point and contact points. The proposed method achieves state-of-the-art performance on the large-scale GraspNet-1Billion dataset. We also conduct real-world experiments on a Franka Emika Panda robot and show that the robot can grasp objects in cluttered scenes with a high success rate.

Robotic grasping pose detection that predicts the configuration of the robotic gripper for object grasping is fundamental in robot manipulation. Based on point clouds, most of the existing methods predict grasp pose with the hierarchical PointNet++ backbone, while the non-local geometric information is underexplored. In this work, we address the 7-DoF (6- DoF with the grasp width) grasp detection by introducing a one- stage Transformer-based hierarchical multi-scale model dubbed TransGrasp. Empowered by TransGrasp, the point features are enhanced via acquiring multi-scale shape awareness in the whole scene. By directly modeling the long-range relevance, our pipeline is aware of object contour to avoid collisions and able to apply analogy reasoning for long-distance geometric structures. The evaluation results on the large scale GraspNet- 1Billion dataset demonstrate the effectiveness of the proposed TransGrasp. The real robot experiments on an ABB YUMI robot with an Azure Kinect DK camera and an ABB Smart two-finger gripper show high success rates in both single object and cluttered scenes.