## 1 Introduction  
Reacting to slippage or adjusting unstable grasps of handheld objects is a subconscious task that happens automatically for most individuals [1]. Nondisabled individuals can use their sense of touch and vision to react to slippage or even predict when the held object is likely to slip. Making adjustments stabilizes the grip, and the grasp can be re-evaluated [2]. For individuals with severe sensory-motor disabilities, grasping tasks can be achieved with the help of assistive robot arms, which can be controlled via remaining bodily functions, such as tongue movements [3]. Developing such an interface for these robot manipulators is a complex task [4], and therefore, supporting the control with autonomy of the robot to reduce the complexity is of great importance. While grasping points for grasping tasks of robotic arms can be synthesized well with modern computer vision methods [5], the grasp might still be unstable, causing objects to slip out of the gripper during the pickup or midair during an executed task. Due to the nature of the setup and the user group, there is little to no chance for them to react in time to a slip event. This can be detrimental to the confidence and trust the user develops in the rehabilitative device and ultimately the desire to use the system for everyday aid [6]. Detecting these slip events and having the gripper react fully autonomously to those events can improve the robustness of--and increase the trust of the user in--the system. In the last two decades, research on slip detection has primarily focused on industrial applications [7]. With the continuous improvement of machine learning methods, these strategies remain a current area of research [8; 9]. However, it is essential to note that these methods have mainly been developed and trained on controlled environments for industrial settings. In contrast, in everyday life, assistive devices must cope with various outside influences and noises. As a result, assistive robot arms, although more lightweight and affordable, are often equipped with very few sensors. The general research on slip detection mostly focuses on imitating the sense of touch of the human hand [7; 10]. This sense of touch is replicated through sensors like multiaxial force sensors, sensors detecting microvibrations, thermal microflux sensors, and optical sensors [7; 10]. With only visual data and a machine learning approach, [PERSON] et al. [8] achieve up to an 80.92% slip detection accuracy when providing the image difference sequence as input to their deep neural network (DNN) and 53.68% when giving the raw image sequence as input. In their work, [PERSON] et al. [8] equip a WSG-50 parallel gripper with a GelSight tactile sensor. They propose a method based on a DNN to classify and detect slip. When including the tactile data, the DNN achieves an accuracy of up to 88.03% using the pretrained Inception-V3 model. With the utilization of a CNN-LSTM (convolutional neural network-long short-term memory), [PERSON] et al. [9] achieve up to 67.96% accuracy using only visual data. When including tactile data, they achieve a classification accuracy of up to 88.75%. [PERSON] et al. [11] reported an 88.03% detection accuracy with combined tactile and visual data. By utilizing interpolation of deformation fields detected by their custom-built sensors, they achieved an accuracy of 97.62%. In a recent study [12] by [PERSON] et al., an accuracy of up to 96.96% was claimed. They performed visuo-tactile fusion using a multiscale temporal convolutional network (MS-TCN) to extract both visual and tactile data. It is noteworthy that all four of these studies used controlled environments with minimal background noise to train and evaluate their models for slip detection. [PERSON] et al. [13] developed a method for computer vision-guided robot arms to detect and grasp transparent objects. In their work, they also made use of the GelSight sensor and employed a poking strategy to help identify the transparent objects. With their strategy, [PERSON] et al.