## 2 Methods
### Slip Detection
#### 2.3.3 Optical Path Occlusion and Remaining Background Noise
However, there is considerable room for improvement, as we currently make strong assumptions about the optical flow direction resulting from the deformation of the grasped object by the gripper. Instead of directly using the flow direction, we could consider the coherency of the flow direction. Slip can generally be categorized into two types: translational and rotational slip. For purely translational slip, the estimated motion field by the optical flow exhibits high coherence, with all pixels having the same flow direction. In rotational slip cases, the motion field's coherence depends on the distance of the pixels from the point of rotation, which is determined by the gripper's contact point, whose position is known. The motion field of compliant objects being deformed is less coherent, or at least it shows a different pattern. By excluding these patterns instead of solely relying on horizontal optical flow, we might be able to improve the handling of compliant objects. ### Future Work

Improving the algorithm would involve optimizing the parameters used in optical flow estimation, flow direction calculation, and temporal layering of the optical flow. A multidimensional grip search followed by gradient descent could be performed on these parameters, leading to a re-evaluation of the algorithm using the dataset presented above. Furthermore, evaluating the algorithm on similar datasets from comparable studies would provide a better understanding of its performance and enhance the comparison with those studies. Regarding the optical path occlusion strategy, merely eliminating pixels with zero optical flow might not be sufficient. Pixels with very small optical flow magnitudes could still skew the majority vote toward false negatives. This issue is particularly relevant for objects with high texture at the image borders but little texture inside, such as some packaging materials. To address this, an additional threshold on the lower end of the optical flow magnitude spectrum could be implemented to remove these pixels, allowing more \"relevant\" pixels to vote on slip cases. This enhancement would improve the algorithm's robustness in handling the optical path occlusion of such objects. To obtain a more accurate estimation of the grasping force, we could utilize not only the weight estimation of the grasped object but also the grasping force estimated from the current applied to the underactuated gripper during the grasping process. This additional information could be incorporated into a more sophisticated grasping strategy that uses force feedback to adjust the robot arm's grip, leading to improved grasping performance. ## 5 Conclusions

In this paper, we introduce a slip detection method for assistive robotic arms that offers several advantages, including minimal resource usage, reliance on classical and easy-to-implement methods, and the use of affordable and readily available materials. The approach leverages optical flow to estimate the motion field of grasped objects and addresses common challenges encountered in vision-based approaches. While the method is not as effective as tactile sensors, it achieves accuracy comparable to state-of-the-art learning-based methods. The proposed method effectively handles common issues associated with purely vision-based slip detection methods, such as optical path occlusion or compliant objects. By manipulating the optical flow estimation resulting from frame comparisons, a voting system of pixels enables successful slip detection, even with up to 50% occlusion of the grasped object's optical path. Additionally, the algorithm discriminates specific optical flow patterns to prevent false triggers caused by deformations of compliant objects resulting in optical flow changes. However, predictive or incipient slip detection is not feasible due to the nature of the method, which relies on comparing two frames.