## 4 Experimental Section
### Real-World Experiments  
Real-life experiments were conducted to determine whether the proposed system could be applied in practical situations. This study used a Hiwin Ra605-710-GC robotic arm and a Xeg_32 electric gripper; an Intel RealSense D415i depth camera (Intel Corporation, Santa Clara, California, United States) was installed to obtain RGB-D images. In the training, ten objects were randomly placed in the working space, and the robotic arm grasped them until the working space was empty. This was repeated until the training was complete. This study used the model trained in the simulator as the pretrained model for use in real-life training. Figure 10 shows that when the model of this study was employed, the probability of an object being successfully grasped was 77%. Factors such as light reflection in the environment may have negatively affected the success rate. The real-world success rate for the proposed method and the VPG system is shown in Figure 11.  
This study tested the trained model in real life. Ten objects were randomly placed in the working space for the robotic arm to grasp, and 20 rounds of test were performed. Similar to in the simulated experiment, the mean completion rate, mean success rate, and mean action efficiency were employed as indices to evaluate the outcomes. The results are presented in Table 4. The mean success and completion rates achieved using the proposed model were both approximately 10% greater than those achieved using the VPG system [13]. The real-world results of our proposed method show significant improvements over the Variable Policy Gradient (VPG) method [13] across all key performance metrics. Our \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Method** & **Completion** & **Grasp Success** & **Action Efficiency** \\ \hline VPG & 78.3 & 72.1 & 65.9 \\ OURS & 88.0 & 81.4 & 68.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Real-world performance of proposed method and VPG (mean %). Figure 11: The real-world success rate for the proposed method and the VPG system. The graph illustrates the comparison of grasping performance (success rate) between two methods: VPG_real_world (blue) and OURS_real_world (red) over 1500 training steps. The solid lines represent the average performance throughout training, while the dashed lines indicate the standard deviation bounds, reflecting the variability in performance at each training step. The OURS_real_world method demonstrates higher and faster success rates compared to VPG_real_world, as seen by the success rate reaching 80% after approximately 600 training steps. approach achieves a higher completion ratio of 88.0% compared to VPG's 78.3%, indicating a more consistent execution of the pushing and grasping tasks. The grasping success ratio also improves, with our method achieving 81.4%, outperforming VPG by 72.1%. This indicates a more reliable grasping phase after pushing the object. In terms of action efficiency, our method shows a slight advantage (68.2% compared to 65.9% for VPG), reflecting the ability to perform the task with fewer and more effective actions. Overall, these results indicate that our method is more effective and efficient for real-world applications. Furthermore, studies cited in references [40; 41] are in line with our findings, showing that the use of Deep Reinforcement Learning (DRL) models improves the optimization of both grasping and pushing actions. These studies confirm that DRL-based methods, as used in our study, improve the performance of robotic arms in complex manipulation tasks by learning efficient strategies through environmental interactions. This validation further supports the conclusion that DRL integration improves grasping success and action efficiency, making it a powerful approach for robot control in dynamic environments. Pushing and grasping objects are shown in Figure 12.  
Pushing and grasping objects in the real world is an important part of optimizing the grasping process.