# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 2 Related Work  
### Deep Learning Methods for Robotic Grasp Detection  
With the increasing number of potential applications of robots, robot grasp detection methods are also evolving. Grasp detection methods are divided into analytical and data-driven methods [2]. The analytical method is to analyze the physical models of the robot and the object, and calculate the appropriate grasping position according to the kinematics and dynamics equations. And the analytical method usually has to satisfy the principles of force closure and form closure [14]. In real scenarios, accurate physical models are difficult to obtain, which makes the method difficult to implement. A data-driven method refers to the use of a priori knowledge of object grasping to predict the grasping postures of an object [15]. Traditional data-driven methods usually require manually building a knowledge library for grasping, which contains the correspondence between the object features and the grasping postures. Traditional data-driven methods are good at predicting the grasping postures of known and similar objects, but cannot predict the grasping postures of unknown objects. The data-driven method based on deep learning can automatically extract the features of the grasping region and establish the mapping relationship between the features of the grasping regions and the grasping postures through deep neural networks. The deep learning-based grasp detection method is able to predict the grasping posture of the unknown objects. The deep learning-based grasp detection method was first proposedin [4], which obtains the optimal grasping posture from many candidates through two cascaded deep networks. [PERSON] et al. [16] used AlexNet [17] to sample and evaluate a large number of grasping candidates. [PERSON] et al. [18] proposed the Grasp Quality Convolutional Neural Network, which iteratively performs multiple rounds of sampling and evaluation for grasping candidates. The above methods are slow to detect because a large number of candidates need to be evaluated. In [8], [PERSON] et al. proposed a lightweight fully convolutional network, GGCNN, which takes a depth image as the input and outputs three grasping parameter maps, including a grasping quality map, a grasping angle map, and a grasping width map. This method can generate grasping configurations end-to-end, and the grasping parameter map representation method has been used in subsequent studies. After that, [PERSON] et al. [19] introduced the Multi-View Pickup Controller to improve the grasp detection accuracy. [PERSON] et al. [20] introduced a multilayer residual module into the GGCNN, constituting the Generative Residual Convolutional Neural Network (GR-ConvNet), which enhances the depth of the network, resulting in enhanced network generalization, and the detection accuracy on the Cornell dataset reached 97.7%. In [21], [PERSON] et al. introduced the SE-ResNet module into the UNet architecture to constitute SE-ResUNet, which further improved the detection accuracy. In [9], [PERSON] et al. used Selective Kernel Convolution to enhance the usage of multi-scale features. In [22], [PERSON] et al. designed a hierarchical multi-scale feature fusion module and an Inverted Shuffle Residual module to further improve the detection accuracy. In addition to using CNNs to design grasp detection networks, [PERSON] et al. [23] were the first to introduce the Transformer architecture into grasp detection networks, obtaining TF-Grasp. Due to the powerful self-attention mechanism, TF-Grasp can acquire both global and local information, and obtains a 97.99% grasp detection accuracy on the Cornell dataset. [PERSON] et al. [24] used a hierarchical Transformer as an encoder and a CNN based on the channel attention mechanism as a decoder, and also achieved good detection results.