Grasping is mainly divided into grasping detection

trajectory planning

and execution. Accurate grasping detection is the key to completing grasping tasks. In order to achieve more accurate grasping detection and improve the performance of robot grasping

this paper proposes a grasping detection algorithm that integrates attention and multi-task learning based on key point detection algorithm. Firstly

a coordinate attention (CA) attention module is introduced in the feature extraction process to explicitly learn channel and spatial features and make full use of feature information. Secondly

a multi-task weight learning algorithm is added to the loss function to learn the optimal weights of the grasp center coordinates

gripper opening width

and rotation angle information. Finally

experiments are conducted on the Cornell dataset and the larger-scale Jacquard dataset. The results show that the proposed method has a significant improvement in detection speed compared to classical methods such as sliding windows and anchor box types

and has higher accuracy compared to simple key point detection methods. The proposed model achieves accuracy rates of 98.8% and 95.7% on the two datasets

respectively. Grasping examples show that the proposed model also has good grasping results for unconventional objects

and the model has excellent performance in accurate grasping under different Jaccard coefficient conditions. Moreover

the experiments with different initial values of the weight learning algorithm show that the proposed model has good robustness. In addition

the impact of different modules on the performance of the model is analyzed through ablation experiments.