# Integration of Deep Learning and Collaborative Robot for Assembly Tasks
## 6 Discussion  
CNN models, such as the ones implemented here, offer a significant advantage for human-robot collaborative tasks due to their adaptability based on training. Compared to traditional vision-based models, they require relatively shorter training times while swiftly adapting to new sets of parts. While other architectures, such as ResNet or MobileNet,  
Figure 12: Confusion matrix showing the results from the voice recognition system tests.  
could have been implemented, the model selection was influenced by its shorter training duration and controlled experimental conditions. The CNN models implemented here exhibited high accuracy within a short training period, making them suitable for the current application. However, different conditions, such as varying lighting or movement scenarios, might warrant the utilization of more robust models. A similar discussion could be presented for voice recognition models such as Recurrent Neural Networks, which have shown excellent performance [49]. Once again, a CNN proved sufficient for discerning 11 commands. More robust models could be advantageous for applications involving larger command sets.  
The proposed integrated system reached an accuracy of 91.25%. Individually, the object classification model achieved 98.42% accuracy, voice command recognition averaged 94.5%, and hand tracking reached 99% accuracy. The high accuracy of the object classification system is attributed to the robustness of the model in classifying the objects in the lighting conditions used. In the case of the integrated system, the accuracy was affected by errors from the different systems, mostly by voice command misclassification. These results are in line with other previously reported works for both the integrated system and each independent model.  
For instance, [PERSON] et al. [15] developed a human-robot collaboration system based on AND/OR graphs, achieving an 80% success rate in 30 tests. On the other hand, [PERSON] et al. [50] proposed a behavioral tree-based task planning system that reported 100% accuracy in 10 tests across different tasks. However, this system lacked active human interaction beyond a graphical interface. Both aforementioned systems represent human-robot collaborations without the integration of machine learning algorithms.  
In contrast, [PERSON] et al. [33] implemented various neural networks in a human-robot environment, achieving 70% accuracy for body posture estimation, 96% for hand motion recognition, and 93% for voice command recognition. Yet, these models were not deployed for human-robot task completion. Further developments include the work by [PERSON] et al. [32], which presents a CNN model for detecting collisions between users and cobots with an accuracy of 89%, and by [PERSON] et al. [51], which develops a generative grasping CNN-based model achieving between 88% and 94% accuracy for grasping static and moving objects. Additionally, [PERSON] et al. [29] utilized a reinforcement learning model for grasping tasks with a 5-DOF robot, reporting 95.5% accuracy, albeit without human interaction. In summary, current accuracy benchmarks for human-robot collaboration tasks surpass 80%. CNN models in collaborative robot applications exhibit between 70% and 95% accuracy.  
Furthermore, the conducted experimentation surpassed the existing literature by encompassing 560 full routines for testing. The experimentation involved distinct evaluation procedures for the models (outlined in Section 4) and an assessment of integrating various models in a practical use-case scenario, yielding comprehensive results. Supplementing quantitative measurements, qualitative evaluations via questionnaires could enhance the findings derived from the experimentation.