# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 8 Discussion
### Interoperability Challenges and Open Robotic Architectures  
Interoperability requires transparent independence between the hardware (of the cobot and vision system) and its behavior on different software platforms. Current middleware frameworks interact with different platforms to react with similar control, manipulation, and vision-based scene understanding on different systems. Some examples of relevant middleware frameworks are ROS 2, OPC UA, RobMoSys, OPIL, SmartSoft/BRIDE, and ROS-Industrial. Standard middleware frameworks, such as ROS 2, have advanced control interfaces and AI-oriented perception extensions (such as ros2_control, MoveIt 2, and ROS 2 perception stacks). These extensions greatly improved basic interoperability for cobot systems, but they remain insufficient to achieve true interoperability in control, manipulation, and vision-based scene understanding. Middleware solves the problem of message exchange but does not address deeper semantic alignment across heterogeneous systems. For cobots to seamlessly share tasks such as collaborative assembly, dynamic object handover, or shared workspace navigation, they must agree not only on control interfaces but also on representations of objects, scenes, and human intents. This requires standardizing semantic models for scene understanding--including object taxonomies, affordances, human pose, and activity definitions--and shared ontologies for describing manipulation tasks and constraints. Without such semantic interoperability, perception modules (e.g., object detectors or 3D scene parsers) remain vendor- or task-specific, limiting reusability and coordination across platforms. Moreover, achieving safe, context-aware control in mixed-robot teams demands harmonized protocols for exchanging intent, negotiating plans, and enforcing safety constraints (e.g., force limits and dynamic collision avoidance) in real time. Therefore, advancing interoperability in cobotics will require not only robust middleware for communication but also standardized abstractions for perception, control policies, and collaborative planning--ultimately enabling the modular, plug-and-play integration of heterogeneous robots with a shared understanding of their environment and tasks.  
Table 7 summarizes the key differences between controlled laboratory demonstrations and real-world deployment environments for vision-AI-enabled collaborative robots, illustrating why solutions validated under ideal conditions often struggle to achieve robust, safe, and trustworthy performance in dynamic human-centered settings.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Aspect** & **Laboratory Demonstrations** & **Real-World Deployment** & **Representative References** \\ \hline Environment & Controlled, static, well-lit & Dynamic, cluttered, variable lighting and occlusion & [24,100,101] \\ \hline Data Distribution & Limited variation; curated datasets & Diverse, unpredictable, domain shifts & [24,77,99,101] \\ Task Definition & Predefined, repetitive, scripted & Variable tasks, unplanned changes & [16,19,75,124] \\ \hline \hline \end{tabular}
\end{table}
Table 7: Differences between laboratory demonstrations and real-world deployment for vision–AI-enabled cobots.