  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.
