Shimin Wei

Mingshuai Dong, Shimin Wei, Jianqin Yin et al.

Recognizing the category of the object and using the features of the object itself to predict grasp configuration is of great significance to improve the accuracy of the grasp detection model and expand its application. Researchers have been trying to combine these capabilities in an end-to-end network to grasping specific objects in a cluttered scene efficiently. In this paper, we propose an end-to-end semantic grasp detection model, which can accomplish both semantic recognition and grasp detection. And we also design a target feature attention mechanism to guide the model focus on the features of target object ontology for grasp prediction according to the semantic information. This method effectively reduces the background features that are weakly correlated to the target object, thus making the features more unique and guaranteeing the accuracy and efficiency of grasp detection. Experimental results show that the proposed method can achieve 98.38% accuracy in Cornell Grasp Dataset. Furthermore, our results on complex multi-object scenarios or more rigorous evaluation metrics show the domain adaptability of our method over the state-of-the-art.

Mingshuai Dong, Shimin Wei, Xiuli Yu et al.

The reliability of grasp detection for target objects in complex scenes is a challenging task and a critical problem that needs to be solved urgently in practical application. At present, the grasp detection location comes from searching the feature space of the whole image. However, the cluttered background information in the image impairs the accuracy of grasping detection. In this paper, a robotic grasp detection algorithm named MASK-GD is proposed, which provides a feasible solution to this problem. MASK is a segmented image that only contains the pixels of the target object. MASK-GD for grasp detection only uses MASK features rather than the features of the entire image in the scene. It has two stages: the first stage is to provide the MASK of the target object as the input image, and the second stage is a grasp detector based on the MASK feature. Experimental results demonstrate that MASK-GD's performance is comparable with state-of-the-art grasp detection algorithms on Cornell Datasets and Jacquard Dataset. In the meantime, MASK-GD performs much better in complex scenes.