## 3 Review of Papers Dealing with the Application of Robots in Waste Separation
### The Major Findings of the Selected Papers  
Unlike classic production facilities, recycling facilities operate without production deadlines, instead processing all waste according to a combination of additional financial and technical criteria. When recycling, you should take into account the waste that is in the recycling plant, the space it occupies, as well as the composition of the components from which it is made, because they directly affect the value of the recycled raw material and can dictate what will be recycled from the collected waste. This indicates that the recycling process, and therefore the separation process, is a non-uniform and complex task that is currently impossible to completely automate. A typical example of the automation of the waste separation process is based on the combination of a robot and a vision system in a recycling plant [23]. One of the possible approaches when it comes to the automation of recycling with the help of robots is the application of a hybrid approach, where humans can be included in the automated process and can add additional quality through their cognitive abilities [24]. There are already commercially available robotic recycling solutions on the market, which can sort different materials from waste, such as ZenRobotics, SamurAI robot, Max-AI AQC, and installed Liam robot systems developed by Apple to disassemble their devices [9; 25]. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Country of the First Author** & & **Insights** \\ \hline
6. [PERSON] et al., & & & Computer vision technology can significantly improve the management of electronic waste. \\
2023 [27] & India & & The technology aids in automating processes such as identification, classification, sorting, and monitoring of e-waste, reducing human intervention, processing time, and costs. \\ \hline
7. [PERSON] et al., & & & Optimizing image capture through predictive exposure control using deep learning techniques improves the visibility and accuracy of robotic operations in disassembly tasks. \\
2024 [28] & China & & The method leverages predictive learning to adapt exposure settings dynamically, resulting in better image quality and more reliable disassembly outcomes. \\ \hline
8. [PERSON] et al., & & & A remotely controlled system that aims to improve urban waste management through automation and deep learning, using deep convolutional neural networks (DCNNs) and image processing techniques to accurately classify different types of waste, was studied. \\
2023 [29] & Bangladesh & & The ConvoWaste deep learning model system achieved a high level of accuracy, with the deep learning model reaching a 98% accuracy rate in waste classification tasks. \\ \hline
9. [PERSON] et al., & & & Human involvement increases robot accuracy in recycling tasks. \\
2022 [30] & Chicago, USA & & Human assistance improves robot accuracy significantly in diverse material sorting. \\  & & & Different levels of human involvement in human–robot collaboration is suggested. \\ \hline
10. [PERSON] et al., & & & The introduction of collaborative robots (cobots) can significantly reduce the physical and cognitive workload of human workers during e-waste disassembly and improve ergonomic conditions for workers. \\
2022 [31] & Florida, USA & & Proper training for human workers on how to interact with cobots is crucial for safety and optimal task allocation between humans and robots. \\  & & & Robots are better suited for tasks that require precision and strength, while humans excel in tasks that require flexibility and problem-solving skills. \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._Robotic waste separation systems can be divided into two main subsystems that affect the separation process and its success [23]:  
* Vision systems and artificial intelligence algorithms. * Robotic systems with grippers. Vision systems and the application of machine learning and artificial intelligence aim to recognize an item to be recycled from the waste process [26; 27; 28; 29; 30].