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In this letter, a novel grasp detection neural network Squeeze-and-Excitation ResUNet (SE-ResUNet) is developed, where the residual block with the channel attention is integrated. The proposed framework can not only generate the grasp pose from the RGB-D images, but also predict the quality score of each grasp pose. The experimental results show that the accuracy on the Cornell dataset and the Jacquard dataset is 98.2% and 95.7%, respectively. And the processing speed for the RGB-D images can reach 30fps, which shows the good real-time performance. In the comparison study, better performance is also obtained by the proposed method, which improves the accuracy and time efficiency. Finally, it is also demonstrated by physical grasping on the Baxter robot, where the average grasp success rate is 96.3%.
Yu et al. (Tue,) studied this question.
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