## 2 Related Work
### Pregrasping Assistance  
Pregrasping tasks that facilitate the completion of grasping tasks are a current trend. In [PERSON] et al. (2012), nontarget objects were moved in a pregrasping process to increase the rate of successful grasping of a target object. Their system achieved a success rate of 98.4% but required data on approximately 250,000 grasps and 2500 shifting movements for training. [PERSON] et al. also applied their system to a real-life context, the picking up and emptying of bins.  
[PERSON] et al. (2020) introduced QT-Opt, an expandable self-supervised visual reinforcement learning framework. They used data on 580,000 actual grasps to train networks for controlling seven robotic arms and, for the grasping of unknown objects, achieved a success rate of 96%.  
The aforementioned methods achieved high success rates, but they had the disadvantage of requiring a large volume of real-life training data. [PERSON] et al. (2013) proposed the DQN-FCN combination for training a model on pushing and grasping tasks within simulated and real environments. In their system, an FCN is employed to estimate the maximum Q-value of a grasp or push. Only 2500 grasps and pushes were required for training before a success rate of 80% could be achieved for grasping tasks involving obstructed objects. Many researchers have since combined the DQN and an FCN. [PERSON] et al. (2018) proposed a target-oriented attention module for training. [PERSON] et al. (2020) enhance the efficiency of object recognition and grasping by analyzing the colors of the objects. This module can effectively find hidden objects and then instruct a robot to push and grasp the objects as well as be effectively trained in the pushing and scattering of objects. [PERSON] et al. (2020) proposed the use of Split DQN to perform actions on target objects in cluttered scenarios, reducing sample complexity and effectively accelerating model convergence. However, goal-oriented methods target only specified objects for grasping and cannot directly identify the best objects for grasping in the workspace. [PERSON] et al. (2020) employed a traditional rule-based grasp detection algorithm for grasping, with only the pushing action using deep reinforcement learning algorithms to effectively reduce system reasoning time and increase execution speed. However, the mentioned literature typically requires long training times or may not achieve a satisfactory grasping success rate, limiting their practical applicability.  
Some papers use imitation learning to train various action tasks ([PERSON] et al., 2019; [PERSON] et al., 2020). Imitation learning allows for rapid skill acquisition by observing expert demonstration data, thereby eliminating the need for model convergence through extensive trial and error. However, training each new action requires new demonstration data, which poses challenges for real-world industrial applications.  
In summary, the methods discussed herein can achieve a high success rate, but most of them require numerous iterations or multiple neural networks, meaning that they are unsuitable for use in industry, where they would have to be deployed within a short period. By contrast, the method proposed in this study achieves a success rate of 80% for grasping tasks when training is conducted for only 600 iterations, making it suitable for rapid deployment in industry.