## 5 Discussion and Future Work  
This study integrated image masking into the DQN to overcome the problem of inefficient training due to sparse rewards. Sparse rewards are often caused by grasps or pushes performed at locations where there are no objects. When image masking was incorporated, the robotic arm always grasped the center of target objects and always pushed the edges of objects. In addition, each exploration was always performed near objects rather than in objectless areas and near the boundaries of the working area. The experimental results demonstrated that when masking was performed, the success rate reached 80% after 600 training steps, confirming that image masking improved the method developed in this study and accelerated the learning of effective grasping strategies; the mean completion rate and success rate in tests involving six scenarios improved. Regarding its current limitations, paper image masking is mainly effective for object centers and faces difficulties in accurately gripping complex shapes. Future research will use a semantic segmentation model to identify potential collision points during robotic arm manipulation to address this issue. These areas will be regarded as background to prevent the robotic arm from operating in these zones, thereby increasing the success rate of gripping complex shapes. This study demonstrates the efficacy of integrating image masking into the Deep Q-Network (DQN) framework to mitigate the challenges associated with sparse reward structures in robotic pushing and grasping tasks. Sparse rewards typically result from the robotic arm executing actions, such as grasps or pushes, in areas where no objects are present, leading to inefficient training and prolonged convergence times. By incorporating image masking, the proposed method ensures that the robotic arm focuses its actions on relevant areas--specifically, grasping the centers of target objects and pushing at their edges. This strategy enhances the likelihood of meaningful interactions, thereby accelerating the learning process. Empirical results indicate a substantial improvement in performance, with success rates reaching 80% after only 600 training steps, suggesting that the method effectively reduces the learning time needed for complex manipulation tasks. The performance evaluation across various scenarios reveals that the proposed approach effectively balances success rates and action efficiency, particularly in environments characterized by moderate levels of complexity. The experimental outcomes showed a notable enhancement in both mean completion rates and success rates across all six test scenarios, reflecting the robustness and adaptability of the method in handling different object types, whether static or dynamic. However, the performance decline observed inscenarios involving a higher number of dynamic objects or more complex arrangements indicates that the current method has limitations in adapting to highly dynamic and unpredictable environments. These findings suggest that while the proposed method is suitable for a range of tasks, further improvements are necessary to extend its applicability to more challenging scenarios. A key limitation of the current approach lies in its handling of objects with complex or irregular shapes. The image masking technique primarily targets the central areas of objects, which may not always be optimal for grasping objects with intricate geometries. This focus can lead to suboptimal grasping strategies and reduced success rates, particularly when multiple objects with varying shapes are present in the workspace. Future research should explore the integration of advanced object recognition techniques, such as semantic segmentation, which could enable the robotic arm to more accurately identify object boundaries and potential collision points. By doing so, the arm could avoid undesired interactions and improve its manipulation precision, thus enhancing the overall success rate and robustness in diverse environments.