EdgeGrasp: Enhancing Edge Perception for 7-DoF Grasping Pose Estimation in Cluttered Scenes

Abstract

Estimating 7-DoF grasping poses (6-DoF with gripper width) in cluttered scenes is a critical challenge for robotic manipulation. In such environments, object edges often contain many promising grasp candidates, but relying solely on incomplete single-view point cloud to infer them is difficult. While neural networks excel at learning edge features from RGB images, simply combining these with point clouds often fails to generalize to novel scenes. To address these challenges, we propose EdgeGrasp, which enhances edge perception by allowing each modality to contribute to the most suitable edge information source for improving grasping performance. The internal edge features are extracted through voxel-based sparse 3D convolution on the aggregated point cloud from the edge interior, ensuring a rich geometric representation while mitigating incompleteness at the edge. For external edge and junction, vision foundation model is employed to extract local zero-shot semantic features, capturing fine-grained details and improving cross-object generalization. Finally, edge spatial attention fuses these features into edge-enhanced features by encoding edge distance for estimating 7-DoF grasping poses. Experimental results demonstrate our method's effectiveness, achieving state-of-the-art performance on the Graspnet-1Billion benchmark. Real-world robotic experiments further validate its practical applicability.