Distributed Adaptive Control: An Ideal Cognitive Architecture Candidate
163
7 Discussion
In this article, we proposed the Distributed Adaptive Control (DAC) cognitive architecture as a candidate for robot and plant control within the context of Industry 4.0.
The implementation of this architecture has been supported by previous works on different robotic areas such as Navigation, Human-Robot Interaction and Motor Control.
However, the following issues have not been discussed yet.
The implementation of DAC has been addressed within the context of a hybrid
human-robot recycling plant. This kind industrial plant can be perceived as a simplified
instance since in comparison with the overall idea of Industry 4.0 it less dependent on
other technologies such as IoT or cloud computing. However, although we are convinced
that our recursive implementation of DAC could take great advantage of such technology,
the Fourth Industrial Revolution will be achieved thanks to discreet but firm steps.
How much must be learned and how much must be prewired by the agents is another
important question to solve, in order to maintain both adaptability and efficiency. We
propose that basic abilities such as grabbing a tool or creating and navigating a map of
the environment are preferably achieved in previous training sessions, so the agent can
adapt a behaviour already learned. Hence, the robots just should adapt these abilities
already learned to the position of the tool or the trajectory of other mobile robots. Other
significant information such as the referred to the worker’s abilities and preferences
could be integrated directly in the central control system by using questionnaires.
Acknowledgements. This material is based upon work funded by the European Commission’s
Horizon 2020 HR-Recycler project (HR-Recycler-820742H2020-NMBP-FOF-2018).
References
1. Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T.: Intelligent manufacturing in the context of
industry 4.0: a review. Engineering 3(5), 616–630 (2017)
2. Tan, Y., Goddard, S., Pérez, L.C.: A prototype architecture for cyber-physical systems. ACM
SIGBED Rev. 5(1), 1–2 (2008)
3. Van Kranenburg, R.: The Internet of Things: a critique of ambient technology and the allseeing network of RFID. Institute of Network Cultures (2008)
4. Beer, S.: Brain of the Firm: The Managerial Cybernetics of Organisation. Wiley (1972)
5. Medina, E.: Cybernetic Revolutionaries: Technology and Politics in Allende’s Chile. MIT
Press (2011)
6. NSF: Cyber-physical Systems (CPS) (2017)
7. Botvinick, M., Ritter, S., Wang, J.X., Kurth-Nelson, Z., Blundell, C., Hassabis, D.:
Reinforcement learning, fast and slow. Trends Cogn. Sci. 23(5), 408–422 (2019)
8. Vahrenkamp, N., Wächter, M., Kröhnert, M., Kaiser, P., Welke, K., Asfour, T.: High-level
robot control with ArmarX. Informatik 2014 (2014)
9. Kotseruba, I., Tsotsos, J.K.: 40 years of cognitive architectures: core cognitive abilities and
practical applications. Artif. Intell. Rev. 53(1), 17–94 (2018). https://doi.org/10.1007/s10462018-9646-y
10. EPA. https://www.epa.gov/facts-and-figures-about-materials-waste-and-recycling/nationaloverview-facts-and-figures-materials
163
7 Discussion
In this article, we proposed the Distributed Adaptive Control (DAC) cognitive architecture as a candidate for robot and plant control within the context of Industry 4.0.
The implementation of this architecture has been supported by previous works on different robotic areas such as Navigation, Human-Robot Interaction and Motor Control.
However, the following issues have not been discussed yet.
The implementation of DAC has been addressed within the context of a hybrid
human-robot recycling plant. This kind industrial plant can be perceived as a simplified
instance since in comparison with the overall idea of Industry 4.0 it less dependent on
other technologies such as IoT or cloud computing. However, although we are convinced
that our recursive implementation of DAC could take great advantage of such technology,
the Fourth Industrial Revolution will be achieved thanks to discreet but firm steps.
How much must be learned and how much must be prewired by the agents is another
important question to solve, in order to maintain both adaptability and efficiency. We
propose that basic abilities such as grabbing a tool or creating and navigating a map of
the environment are preferably achieved in previous training sessions, so the agent can
adapt a behaviour already learned. Hence, the robots just should adapt these abilities
already learned to the position of the tool or the trajectory of other mobile robots. Other
significant information such as the referred to the worker’s abilities and preferences
could be integrated directly in the central control system by using questionnaires.
Acknowledgements. This material is based upon work funded by the European Commission’s
Horizon 2020 HR-Recycler project (HR-Recycler-820742H2020-NMBP-FOF-2018).
References
1. Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T.: Intelligent manufacturing in the context of
industry 4.0: a review. Engineering 3(5), 616–630 (2017)
2. Tan, Y., Goddard, S., Pérez, L.C.: A prototype architecture for cyber-physical systems. ACM
SIGBED Rev. 5(1), 1–2 (2008)
3. Van Kranenburg, R.: The Internet of Things: a critique of ambient technology and the allseeing network of RFID. Institute of Network Cultures (2008)
4. Beer, S.: Brain of the Firm: The Managerial Cybernetics of Organisation. Wiley (1972)
5. Medina, E.: Cybernetic Revolutionaries: Technology and Politics in Allende’s Chile. MIT
Press (2011)
6. NSF: Cyber-physical Systems (CPS) (2017)
7. Botvinick, M., Ritter, S., Wang, J.X., Kurth-Nelson, Z., Blundell, C., Hassabis, D.:
Reinforcement learning, fast and slow. Trends Cogn. Sci. 23(5), 408–422 (2019)
8. Vahrenkamp, N., Wächter, M., Kröhnert, M., Kaiser, P., Welke, K., Asfour, T.: High-level
robot control with ArmarX. Informatik 2014 (2014)
9. Kotseruba, I., Tsotsos, J.K.: 40 years of cognitive architectures: core cognitive abilities and
practical applications. Artif. Intell. Rev. 53(1), 17–94 (2018). https://doi.org/10.1007/s10462018-9646-y
10. EPA. https://www.epa.gov/facts-and-figures-about-materials-waste-and-recycling/nationaloverview-facts-and-figures-materials