## 4 Experimental Section
### Findings and Limitations in This Experiment
#### 4.4.1 Mask Optimization in This Model  
Image masks play a crucial role in improving the robot's ability to precisely push and grasp objects. Using image segmentation techniques, image masks can separate objects from the background and identify important parts of the object, such as edges or handles. This allows deep reinforcement learning (DRL) systems to receive cleaner and more focused input, thereby increasing the accuracy of decisions taken by the robot. In this context,  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Num. of Objects**} & \multicolumn{1}{c}{**Method**} & \multicolumn{1}{c}{**Achieve 80\% Grasp Success Rate Training Steps**} \\ \hline \multirow{3}{*}{8} & VPG & 1350 \\ \cline{2-3}  & Binary Mask & 1000 \\ \cline{2-3}  & **Our Mask** & **550** \\ \hline \hline \multirow{3}{*}{10} & VPG & 1500 \\ \cline{2-3}  & Binary Mask & 950 \\ \cline{2-3}  & **Our Mask** & **600** \\ \hline \hline \multirow{3}{*}{12} & VPG & 1800 \\ \cline{2-3}  & Binary Mask & 1400 \\ \cline{2-3}  & **Our Mask** & **1100** \\ \hline \hline \multirow{3}{*}{16} & VPG & 2300 \\ \cline{2-3}  & Binary Mask & 2100 \\ \cline{2-3}  & **Our Mask** & **1600** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of 16 objects using this model.  
image masks help reduce visual confusion and ensure that DRL algorithms can quickly and efficiently determine the best action for pushing or grasping an object [10].  
The use of image masks brings several significant advantages in robotic manipulation. First, it improves accuracy in detecting objects and important object features, which is especially important in unstructured environments or with objects that have irregular shapes. Second, by filtering out irrelevant information, image masks can speed up the robot's decision-making process, because the system only focuses on relevant information. Third, image masks can help in situations where objects overlap or are close to each other, clearly defining the boundaries of each object so that the robot can perform manipulation tasks more effectively and safely [28].  
Although image masks are very useful for robotic manipulation tasks, there are some limitations and challenges that need to be overcome. One of the main limitations is that the detection accuracy is highly dependent on the quality and resolution of the images used. In poor lighting conditions or when dealing with objects with complex textures, image masks may not perform optimally, which can lead to errors in object segmentation [46]. Furthermore, the use of image masks is usually computationally intensive, especially when applied to very large and high-resolution models, which can be a significant bottleneck in real-time applications [47]. Another challenge is related to adaptability; masks generated by models trained under certain conditions may not perform well when applied to other conditions, which calls for the development of more robust and flexible segmentation models to accommodate different environmental and object variations [48]. Therefore, new, more efficient and adaptive approaches are needed to overcome these limitations to improve the performance and applicability of image mask technology in dynamic real-world scenarios.