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Secure grasping of objects in complex scenes is the foundation of many tasks. It is important for robots to autonomously determine the optimal grasp based on visual information, which requires reasoning about the stacking relationship of objects and detecting the grasp position. This paper proposes a multi-task secure grasping detection model, which consists of the grasping relationship network (GrRN) and the oriented rectangles detection network CSL-YOLO, which uses circular smooth label (CSL). GrRN uses DETR to solve set prediction problems in object detection, enabling end-to-end detection of grasping relationships. CSL-YOLO uses classification to predict the angle of oriented rectangles, and solves the angle distance problem caused by classification. Experiments on the Visual Manipulate Relationship Dataset (VMRD) and the grasping detection dataset Cornell demonstrate that our method outperforms existing methods and exhibits good applicability on robot platforms. [ABSTRACT FROM AUTHOR]

To grasp the target object stably and orderly in the object-stacking scenes, it is important for the robot to reason the relationships between objects and obtain intelligent manipulation order for more advanced interaction between the robot and the environment. This paper proposes a novel graph-based visual manipulation relationship reasoning network (GVMRN) that directly outputs object relationships and manipulation order. The GVMRN model first extracts features and detects objects from RGB images, and then adopts graph convolutional network (GCN) to collect contextual information between objects. To improve the efficiency of relation reasoning, a relationship filtering network is built to reduce object pairs before reasoning. The experiments on the Visual Manipulation Relationship Dataset (VMRD) show that our model significantly outperforms previous methods on reasoning object relationships in object-stacking scenes. The GVMRN model is also tested on the images we collected and applied on the robot grasping platform. The results demonstrated the generalization and applicability of our method in real environment.

To grasp the target object stably and orderly in the object-stacking scenes, it is important for the robot to reason the relationships between objects and obtain intelligent manipulation order for more advanced interaction between the robot and the environment. This paper proposes a novel graph-based visual manipulation relationship reasoning network (GVMRN) that directly outputs object relationships and manipulation order. The GVMRN model first extracts features and detects objects from RGB images, and then adopts graph convolutional network (GCN) to collect contextual information between objects. To improve the efficiency of relation reasoning, a relationship filtering network is built to reduce object pairs before reasoning. The experiments on the Visual Manipulation Relationship Dataset (VMRD) show that our model significantly outperforms previous methods on reasoning object relationships in object-stacking scenes. The GVMRN model is also tested on the images we collected and applied on the robot grasping platform. The results demonstrated the generalization and applicability of our method in real environment. [ABSTRACT FROM AUTHOR]