## 6 Results and Analysis
### Robotic Grasping  
#### 6.4.1 Single-Object and Multi-Object Scenes  
In the robotic grasping task, we first conducted a single-object grasping test on 16 unknown household objects, varying in shape, size, and color. Each object was randomly positioned in different locations within the robot's workspace. In total, we performed 160 grasping attempts, out of which 152 were successful, resulting in a grasping success rate of 95.0%. The experimental results demonstrate the effectiveness of the proposed FAGD-Net in grasping unknown household objects. We compared the success rates of our method with those of classical and the state-of-the-art algorithms, as shown in Table 4. The comparison indicates that our proposed method also achieves advanced success rates in single-object grasping tasks.  
Furthermore, we conducted grasping tests on adversarial objects with abstract shapes and irregular surfaces. We attempted 90 grasps, out of which 82 were successful, resulting in a success rate of 91.1%. These tests demonstrate that our grasping detection model performs well even on objects with peculiar shapes. Figure 15 depicts a schematic illustration of grasping in a single-object scenario, showcasing the results of grasping detection for both household objects and adversarial objects, followed accurate grasping by the robot. Additionally, we visualize the grasp configurations as heatmaps to intuitively present the feature maps. The grasp heatmap demonstrates the model's effective perception of grasp-pable regions in single-object scenes, with varying confidence levels of grasp configurations distributed across each pixel of the single object. Finally, the robot executes the grasp configuration with the highest confidence level. These results show that FAGD-Net is able to effectively grasp previously unseen objects in single-object scenarios, also showcasing its robust grasping performance.  
To assess the effectiveness of our grasp detection model in multi-object scenarios, we randomly selected objects to construct a scene with multiple targets, in which each object was randomly placed on a tabletop. During each trial, the robot identified and executed the grasp with the highest confidence level. A total of 120 grasping attempts were conducted, resulting in 112 successful grasps and achieving an average success rate of 93.3%. The robot demonstrated high-performance grasping even in multi-object scenarios, facilitated by the model's pixel-level learning, which effectively extracts relevant grasp features and enables the model to generalize well even in the presence of multiple objects. The grasp detection results in a multi-object scenario are depicted in Figure 16, where different colors represent varying confidence levels. We visualize the feature maps of the multi-object scenario to observe the distribution of grasp configurations at each pixel. In the grasp heatmap, it can be observed that the model effectively generalizes to multi-object scenarios, perceiving grasp configurations for each pixel. Finally, the robot selects the grasp configuration with the highest confidence for execution.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Work**} & \multicolumn{2}{c}{**Household Objects**} \\ \cline{2-3}  & **Grasping Success Rates (\%)** \\ \hline [PERSON] et al. [4] & 92.0 \\ [PERSON] et al. [9] & 89.0 \\ [PERSON] [16] & 89.0 \\ [PERSON] et al. [19] & 94.6 \\ [PERSON] et al. [23] & 94.0 \\ [PERSON] et al. [37] & 93.5 \\ [PERSON] et al. [38] & 92.0 \\ [PERSON] et al. [39] & 91.5 \\ [PERSON] & 95.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results on single objects.