An Artificial Sensory Component in a Man-Machine System with Combined Feedback

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Abstract

This paper proposes a conceptual approach to constructing combined feedback in a human–machine interaction system through introducing an artificial sensory feedback component controlled by a technical subsystem. The approach is intended to systematize the role of combined feedback in the control of multi-agent systems with additional elements, humans, and artificial agents. This approach is studied for human vertical posture control and in synthetic experiments (within the CartPole model) considered using reinforcement learning as an example. The efficiency of the control problem solution is investigated by varying the characteristics of information transmission channels and the properties of the artificial sensory feedback component. According to the results, natural experiment observations are conceptually similar to those of the artificial numerical experiment in terms of additional feedback channel operation: there are a similar overshoot effect and prospects for improving control performance by tuning the artificial sensory component.

About the authors

O. V Kubryak

National Research University Moscow Power Engineering Institute

Email: kubriakov@mpei.ru
Moscow Russia

S. V Kovalchuk

ITMO University

Email: kovalchuk@itmo.ru
Saint Petersburg, Russia

References

  1. Su, H., Qi, W., Chen, J., et al. Recent Advancements in Multimodal Human–Robot Interaction // Frontiers in Neurorobotics. – 2023. – Vol. 17. – Art. no. 1084000.
  2. Belhassein, K., Fernandez Castro, V., Mayima, A., et al. Addressing Joint Action Challenges in HRI: Insights from Psychology and Philosophy // Acta Psychologica. – 2022. – Vol. 222, no. 1. – Art. no. 103476.
  3. Leichtmann, B., Nitsch, V., Mara, M. Crisis Ahead? Why Human-Robot Interaction User Studies May Have Replicability Problems and Directions for Improvement // Frontiers in Robotics and AI. – 2022. – Vol. 11, no. 9. – Art. no. 838116.
  4. Al-Hamadani, M., Fadhel, M.A., Alzubaidi, L., Harabgi, B. Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review // Sensors. – 2024. – No. Vol. 24, no. 8. – Art. no. 2461.
  5. Knudsen, J.E., Ghaffar., U., Ma, R., Hung, A.J. Clinical Applications of Artificial Intelligence in Robotic Surgery // Journal of Robotic Surgery. – 2024. – Vol. 18, no. 1. – Art. no. 102.
  6. Кубряк О.В., Панова Е.Н., Крикленко Е.А. Влияние глубины биологической обратной связи на результат выполнения инструкции здоровыми добровольцами // Человек. Спорт. Медицина. – 2018. – Т. 18, № S. – C. 19–26.
  7. Foundations and Fundamentals in Human-Computer Interaction / Ed. by C. Stephanidis, G. Salvendy. – Boca Raton: CRC Press, 2024. – 474 p.
  8. Соколов В.В. Философия Ренэ Декарта Москва: Госполитиздат, 1950. – C. 5–76.
  9. Tustin, A. The Nature of the Operator’s Response in Manual Control, and Its Implications for Controller Design // Journal of the Institution of Electrical Engineers – Part IIA: Automatic Regulators and Servo Mechanisms. – 1947. – Vol. 94, no. 2. – P. 190–206.
  10. Анохин П.К. Биология и нейрофизиология условного рефлекса. – М.: Медицина, 1968. – 547 c.
  11. Anticipation: Learning from the Past: The Russian/Soviet Contributions to the Science of Anticipation / Ed. by M. Nadin. – Cham: Springer International Publishing, 2015.
  12. Новиков Д.А. Кибернетика: Навигатор. История кибернетики, современное состояние, перспективы развития. – Москва: ЛЕНАНД, 2016. – 160 c.
  13. Бернштейн Н.А. Новые линии развития в физиологии и их соотношение с кибернетикой. – Москва: Ин-т философии АН СССР, 1962. – 45 c.
  14. Gunes, H., Broz, F., Crawford, C., et al. Reproducibility in Human-Robot Interaction: Furthering the Science of HRI // Current Robotics Reports. – 2022. – Vol. 3, no. 3. – P. 281–292.
  15. Scibilia, A., Pedrocchi, N., Fortuna, L. Human Control Model Estimation in Physical Human–Machine Interaction: A Survey // Sensors. – 2022. – Vol. 22, no. 5. – Art. no. 1732.
  16. Kubryak, O.V., Kovalchuk, S.V., Bagdasaryan, N.G. Assessment of Cognitive Behavioral Characteristics in Intelligent Systems with Predictive Ability and Computing Power // Philosophies. – 2023. – Vol. 8, no. 5. – Art. no. 75.
  17. Ivanenko, Y., Gurfinkel, V.S. Human Postural Control // Frontiers in Neuroscience. – 2018. – Vol. 20, no. 12. – Art. no. 171.
  18. Левик Ю.С. Исследования в космосе и новые концепции в физиологии движений // Авиакосмическая и экологическая медицина. – 2020. – № 6 (54). – C. 80–91.
  19. Missen, K. J., Carpenter, M.G., Assländer, L. Velocity Dependence of Sensory Reweighting in Human Balance Control // Journal of Neurophysiology. – 2024. – Vol. 232, no. 2. – P. 454–460.
  20. Karmali, F., Goodworth, A.D., Valko, Y., et al. The Role of Vestibular Cues in Postural Sway // Journal of Neurophysiology. – 2021. – Vol. 125, no. 2. – P. 672–686.
  21. Assländer, L., Peterka, R.J. Sensory Reweighting Dynamics Following Removal and Addition of Visual and Oroprioceptive Cues // Journal of Neurophysiology. – 2016. – Vol. 116, no. 2. – P. 272–285.
  22. Zheng, N., Lui, Z., Ren, P., et al. Hybrid-augmented Intelligence: Collaboration and Cognition // Frontiers of Information Technology & Electronic Engineering. – 2017. – Vol. 18, no. 2. – P. 153–179.
  23. Guleva, V., Shikov, E., Bochenina, K., et al. Emerging Complexity in Distributed Intelligent Systems // Entropy. – 2020. – Vol. 22, no. 12. – Art. no. 1437.
  24. Barto, A.G., Sutton, R.S., Anderson, C.W. Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems // IEEE Transactions on Systems, Man, and Cybernetics. – 1983. – Vol. SMC-13, no. 5. – P. 834–846.
  25. Botvinick, M., Cohen, J. Rubber Hands ‘Feel’ Touch That Eyes See // Nature. – 1998. – Vol. 391, p. 756.
  26. Chancel, M., Ehrsson, H.H. Proprioceptive Uncertainty Promotes the Rubber Hand Illusion // Cortex. – 2023. – Vol. 165. – P. 70–85.

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