## 2 Methods
### Slip Detection
#### 2.3.3 Optical Path Occlusion and Remaining Background Noise
This necessitated stabilizing the frames relative to the end effector movements, which could potentially have influenced the results. However, the fact that our algorithm performed well on artificially stabilized image data suggests that potential applications with a stationary or non-end-effector-attached camera, like the EXOTIC Exoskeleton [22], are feasible. The EXOTIC Exoskeleton is a 5-DoF upper limb exoskeleton, controlled by a tongue-computer interface, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Article** & **Proposed** & **[8]** & **[9]** & **[11]** & **[12]** \\ \hline Accuracy & 78.81\% & 80.92\% & 67.96\% & 88.03\% & 96.00\% \\ Precision & 74.69\% & N/A & N/A & N/A & 92.69\% \\ Recall & 87.14\% & N/A & N/A & N/A & 100.00\% \\ F1 Score & 80.44\% & N/A & N/A & N/A & 96.21\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the results of the proposed method with the current literature. These are the results achieved with only visual data, except for [11], which also includes tactile data, as no experiment with visual data only was conducted in their work. Ref. [13] achieves a grasping success rate of 85.20%, which is not directly comparable. where the camera is located on the user's shoulder, rather than the end effector or wrist. To make the slip detection algorithm applicable to this setup, the collected camera data would need to be stabilized. One approach could involve template matching a part of the exoskeleton close to the object in each recorded frame. Another option could be calculating the end effector position from the joint angles via forward kinematics and projecting it onto the two-dimensional camera plane to obtain its position in pixel coordinates. Combining both methods might lead to the most accurate estimation of the grasped object in the recorded frame. The method we employed to separate the object from the background is somewhat specific to our setup. With the continuous improvement of AI, segmentation models have become increasingly effective. Utilizing such segmentation models could improve the accuracy of extracting the object held by the robot arm and separating it from the background more precisely. However, some of the more sophisticated models may have longer calculation times, making them unsuitable for real-time applications. For our algorithm to work effectively, the segmentation accuracy needs to be extremely high to avoid false positives. Conversely, if the object extraction is overly accurate, comparing two cut-out subframes at the same coordinates becomes challenging. When estimating the motion of the grasped object with optical flow, perfectly extracted subframes around the object in the current frame would have to be applied to the last frame to calculate the optical flow between the frames. In the event of a slip, part of the background would appear in the past frame, which could potentially cause issues, although the voting part of the algorithm should minimize this effect. ### Limitations

Apart from the limitations related to untextured and transparent objects, the algorithm has several other constraints. Changes in lighting conditions could cause the algorithm to detect optical flow, even when no actual motion of the object is occurring, leading to false positives. However, this should not significantly impact the recall, which is considered more crucial in the application of our device. A fundamental drawback of our proposed method is that it solely relies on data from the RGBd camera and is based on frame-to-frame comparisons, lacking the ability to predict incipient slip events. Unfortunately, this is an inherent limitation of our approach. While the algorithm includes a mechanism to handle optical path occlusion, it is limited to approximately 50% occlusion of the object, and the frame itself can experience more occlusion. This limitation arises due to the nature of purely vision-based approaches and can only be addressed to some extent. Regarding the handling of compliant objects, our proposed method is simple but effective.