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- W4313332380 abstract "Purpose The grasping task of robots in dense cluttered scenes from a single-view has not been solved perfectly, and there is still a problem of low grasping success rate. This study aims to propose an end-to-end grasp generation method to solve this problem. Design/methodology/approach A new grasp representation method is proposed, which cleverly uses the normal vector of the table surface to derive the grasp baseline vectors, and maps the grasps to the pointed points (PP), so that there is no need to add orthogonal constraints between vectors when using a neural network to predict rotation matrixes of grasps. Findings Experimental results show that the proposed method is beneficial to the training of the neural network, and the model trained on synthetic data set can also have high grasping success rate and completion rate in real-world tasks. Originality/value The main contribution of this paper is that the authors propose a new grasp representation method, which maps the 6-DoF grasps to a PP and an angle related to the tabletop normal vector, thereby eliminating the need to add orthogonal constraints between vectors when directly predicting grasps using neural networks. The proposed method can generate hundreds of grasps covering the whole surface in about 0.3 s. The experimental results show that the proposed method has obvious superiority compared with other methods." @default.
- W4313332380 created "2023-01-06" @default.
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- W4313332380 date "2023-01-02" @default.
- W4313332380 modified "2023-09-29" @default.
- W4313332380 title "PP-GraspNet: 6-DoF grasp generation in clutter using a new grasp representation method" @default.
- W4313332380 cites W1970875798 @default.
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- W4313332380 doi "https://doi.org/10.1108/ir-08-2022-0196" @default.
- W4313332380 hasPublicationYear "2023" @default.
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