# Integration of Deep Learning and Collaborative Robot for Assembly Tasks
## 7 Conclusions  
In this study, three CNNs were successfully integrated to enable human-robot interaction in a predefined assembly task. The system utilized voice commands for executing routines, a pre-trained hand-tracking model for part handover and storage, and image classification to ensure the accurate placement of the parts.  
The results demonstrated the effectiveness of each CNN, with a success rate of 91.25%. It was observed that previous experience in the manipulation of cobots did not change the results of the tests, pointing to the seamless adaptation and use of the system. These findings highlight the significant contribution of the implemented neural networks in enhancing the capabilities of the cobot in assembly tasks, reducing the need for manual intervention, and moving towards a more human-like human-machine interaction.  
The success can be attributed to the accurate interpretation of user commands through the audio command classification system and the object-type identification system, which eliminates the requirement for user intervention in identifying object types. In addition, the use of the hand-tracking system enables flexible human-robot collaboration.  
Further work could focus on exploring different Deep Learning architectures or implementing different collaborative human-robot activities. The models performed adequately in the environment used for experimentation; nonetheless, by using more robust models, the system could be used in environments with more variations in lighting or movement for the object classification models, or one could implement a bigger set of voice commands.  
The use of Deep Learning has the potential to push collaborative human-robot systems to new frontiers. The integration of several CNNs working together in a collaborative task allows one to broaden the range of capabilities. For instance, with the use of voice commands, users are able to perform activities while waiting for an object to be brought or stored. The use of flexible routines and tasks, as seen in [52; 53], would enable adaptable routines according to specific application needs.  
The evaluation methodology proposed for individual subsystems and their integration can be further utilized in similar projects. The integration of Deep Learning and cobots is widely employed across various domains, ranging from manufacturing to medical assistance. Furthermore, this work serves as a proof of concept for seamlessly integrating multiple CNNs in a collaborative system.  
Conceptualization, E.M., O.O., V.H.S.-H. and D.O.-G.; methodology, E.M. and O.O.; software, E.M., O.O., J.A.L.-S., V.H.S.-H. and D.O.-G.; validation, O.O., J.A.L.-S. and V.H.S.-H.; formal analysis, E.M., O.O., C.L.-D. and I.N.A.-G.; investigation, E.M. and O.O.; writing--original draft preparation, E.M. and O.O.; writing--review and editing, E.M., O.O., M.A.P., I.N.A.-G. and A.G.; data curation, E.M., O.O., C.L.-D. and J.A.L.-S.; visualization, E.M., O.O., C.L.-D. and D.O.-G.; supervision and project administration, E.M., O.O., C.L.-D., E.L.-d.-R. and A.G. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by Tecnologico de Monterrey, Vicerrectory of Research and Technology Transfer.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to their large volume.  
The authors declare no conflict of interest.  
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