GFLA: A Grasping Framework With Learning-Based Perception and Analytical Modeling for Single-View Scenes

Abstract

Antipodal grasping from single-view red-green-blue and depth (RGB-D) images is challenged by occlusion and partial observability, making purely analytical inference ill-posed. We present the Grasping Framework with Learning-Based Perception and Analytical Modeling (GFLA), which fuses learning-based perception with analytical modeling. GFLA projects antipodal contacts to the image plane, samples grasp candidates via inverse projection, and ranks them with a force-closure metric. To compensate for the information loss inherent in single-view observations, we introduce two grasping hypothesis-guided modules: 1) a contact projection detection network that localizes graspable regions and predicts antipodal projections on visible surfaces, and 2) a 3-D U-Net-based scene completion network that completes geometry and provides explicit collision cues. On GraspNet-1Billion, GFLA achieves its largest improvement on the novel object set (average precision (AP) 35.88%, an improvement of 7.59%), demonstrating superior generalization to previously unseen object categories while also attaining a competitive overall AP of 57.84% (an improvement of 1.33%). Real-robot experiments in cluttered environments, without domain adaptation or fine-tuning, achieve grasp success rates of 95.42% for single-object scenes and 90.12% for multiobject scenes, demonstrating strong practical robustness.