## 5 Conclusions
The last triplet (D), formed by EEG, RL, and robotics, introduces brain-guided automation, which integrates EEG feedback and reinforcement learning into robotic systems, enabling adaptive behavior and decision making based on real-time cognitive signals, thus enhancing efficiency and adaptability in automated tasks. This integration not only optimizes the performance of the system but also promotes a higher level of interaction between human operators and robotic systems, making the technology more intuitive and responsive to human states and intentions. In the previous paragraphs, the intersections of different technologies were grouped into triplets, examining how their combined capabilities can lead to more innovative and efficient systems. Although there is no existing research that clearly integrates all four technologies together, the potential benefits of such a comprehensive integration are significant. Given the rapid growth in each of these fields, combining them could pave the way for the development of robotic systems that are not only efficient but also acutely aware of their human operators and surroundings. Proposing robotic systems that can be operated  
Figure 7: Conceptual diagram. Intersection between topics displayed in triplets for future research. through cognitive signals from humans to conduct training and exhibit behaviors generated through reinforcement learning in a virtual world seems like a promising direction. Such systems could revolutionize various sectors, including industrial, environmental, and even domestic settings, by introducing robots that are more efficient and more attuned to the nuances of human and environmental interactions. However, it is important to acknowledge the limitations and potential points of criticism of these existing methods. Technically, integrating multiple advanced technologies presents several challenges. Issues such as data synchronization, real-time processing, and the development of robust and adaptive algorithms are significant obstacles. For instance, ensuring that EEG signals are accurately interpreted and translated into meaningful commands in real time requires sophisticated signal processing techniques and powerful computational resources. Similarly, combining VR and RL necessitates creating highly responsive virtual environments that can adapt to both the robot's learning processes and the user's interactions without noticeable latency. The use of cognitive signals and bio-sensors raises several ethical concerns. Privacy is a major issue, as EEG data can potentially reveal sensitive information about a person's mental state, health, and emotions. There is a risk of misuse of or unauthorized access to these data, which could lead to ethical and legal complications. Ensuring informed consent and implementing stringent data protection measures are essential to address these concerns. Moreover, the psychological impact of long-term interaction with robots and immersive virtual environments on users should be carefully studied to prevent any adverse effects. From an implementation perspective, deploying such sophisticated systems in real-world environments demands substantial financial investment, extensive training for users, and rigorous testing to ensure safety and reliability. The initial costs of setting up advanced robotic systems integrated with EEG, VR, and RL can be prohibitive for many organizations. Additionally, users need to be adequately trained to interact with these systems effectively, which requires time and resources. Furthermore, rigorous testing under various conditions is necessary to ensure that these systems can operate safely and reliably in different environments, which can be a time-consuming and complex process. Addressing these challenges is essential for the successful adoption and integration of these innovative technologies in collaborative robotics.