Logo PTI Logo FedCSIS

Position Papers of the 21st Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 48

Toward Neurocognitive Intelligence for Assistive Humanoid Robots in Geriatric Care

, , ,

DOI: http://dx.doi.org/10.15439/2026F0447

Citation: Noorbakhsh Amiri Golilarz, , ,

Full text

Abstract. The increasing demand for elderly care assistance, combined with global caregiver shortages and the growing complexity of aging-related healthcare needs, has accelerated interest in assistive humanoid robots capable of supporting older adults in clinical and home environments. Despite recent advances in robotics, deep learning, multimodal perception, and embodied artificial intelligence, most current humanoid systems remain fundamentally reactive, task-driven, and fragmented in their cognitive design. Existing architectures typically separate perception, memory, reasoning, and action into isolated computational modules, limiting their ability to maintain contextual continuity, adapt behavior over long interaction horizons, and operate reliably under dynamic real world conditions. These limitations become particularly critical in geriatric care settings where safety, contextual awareness, emotional sensitivity, and long-term human interaction are essential. This position paper argues that the next generation of assistive humanoid robots requires a transition toward neurocognitive intelligence frameworks inspired by principles of biological cognition. The paper proposes a conceptual neurocognitive architecture centered around closed-loop perception-action coupling, attention-regulated perception, episodic memory integration, predictive reasoning, adaptive decision-making, and cognitive safety regulation. The proposed framework emphasizes continuous interaction among cognitive processes rather than isolated reactive modules, enabling more context-aware, adaptive, and human-centered robotic behavior. Furthermore, the paper discusses why geriatric care environments uniquely require neurocognitive capabilities, identifies key limitations of current humanoid systems, and outlines major open challenges related to lifelong adaptation, contextual memory, uncertainty-aware reasoning, and safe human-robot interaction. Finally, future research directions are discussed toward developing trustworthy and resilient neurocognitive humanoid systems capable of functioning effectively in elder care environments.

References

  1. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, pp. 436–444, 2015.
  2. I. Goodfellow, “Deep learning,” 2016.
  3. K. Friston, “The free-energy principle: a unified brain theory?” Nature reviews neuroscience, vol. 11, no. 2, pp. 127–138, 2010.
  4. D. Hassabis, D. Kumaran, C. Summerfield, and M. Botvinick, “Neuroscience-inspired artificial intelligence,” Neuron, vol. 95, no. 2, pp. 245–258, 2017.
  5. B. M. Lake, T. D. Ullman, J. B. Tenenbaum, and S. J. Gershman, “Building machines that learn and think like people,” Behavioral and brain sciences, vol. 40, p. e253, 2017.
  6. A. Baddeley, “Working memory,” Memory, pp. 71–111, 2020.
  7. D. Kahneman, Attention and Effort. Englewood Cliffs, NJ: Prentice-Hall, 1973.
  8. A. Clark, “Whatever next? predictive brains, situated agents, and the future of cognitive science,” Behavioral and brain sciences, vol. 36, no. 3, pp. 181–204, 2013.
  9. D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot et al., “Mastering the game of go with deep neural networks and tree search,” nature, vol. 529, no. 7587, pp. 484–489, 2016.
  10. S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” Journal of Machine Learning Research, vol. 17, no. 39, pp. 1–40, 2016.
  11. I. Akkaya, M. Andrychowicz, M. Chociej, M. Litwin, B. McGrew, A. Petron, A. Paino, M. Plappert, G. Powell, R. Ribas et al., “Solving rubik’s cube with a robot hand,” arXiv preprint https://arxiv.org/abs/1910.07113, 2019.
  12. R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill et al., “On the opportunities and risks of foundation models,” arXiv preprint https://arxiv.org/abs/2108.07258, 2021.
  13. B. Zitkovich, T. Yu, S. Xu, P. Xu, T. Xiao, F. Xia, J. Wu, P. Wohlhart, S. Welker, A. Wahid et al., “Rt-2: Vision-language-action models transfer web knowledge to robotic control,” in Conference on Robot Learning. PMLR, 2023, pp. 2165–2183.
  14. G. Marcus, “Deep learning: A critical appraisal,” arXiv preprint https://arxiv.org/abs/1801.00631, 2018.
  15. I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint https://arxiv.org/abs/1412.6572, 2014.
  16. D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mané, “Concrete problems in ai safety,” arXiv preprint https://arxiv.org/abs/1606.06565, 2016.
  17. G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter, “Continual lifelong learning with neural networks: A review,” Neural networks, vol. 113, pp. 54–71, 2019.
  18. M. McCloskey and N. J. Cohen, “Catastrophic interference in connectionist networks: The sequential learning problem,” in Psychology of learning and motivation. Elsevier, 1989, vol. 24, pp. 109–165.
  19. N. A. Golilarz, H. S. Al Khatib, and S. Rahimi, “Towards neurocognitive-inspired intelligence: From ai’s structural mimicry to human-like functional cognition,” IEEE Access, 2026.
  20. S. L. Greer, J. Lynch, A. Reeves, M. Falkenbach, J. Gingrich, J. Cylus, and C. Bambra, Ageing and health. Cambridge University Press, 2021.
  21. E. Marg, “Descartes’error: emotion, reason, and the human brain,” Optometry and Vision Science, vol. 72, no. 11, pp. 847–848, 1995.
  22. E. Tulving, Elements of Episodic Memory. Oxford: Oxford University Press, 1983.
  23. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
  24. A. Graves, G. Wayne, M. Reynolds, T. Harley, I. Danihelka, A. Grabska-Barwińska, S. G. Colmenarejo, E. Grefenstette, T. Ramalho, J. Agapiou et al., “Hybrid computing using a neural network with dynamic external memory,” Nature, vol. 538, no. 7626, pp. 471–476, 2016.
  25. M. I. Posner, “Orienting of attention,” Quarterly journal of experimental psychology, vol. 32, no. 1, pp. 3–25, 1980.
  26. T. A. Hemphill, “Human compatible: Artificial intelligence and the problem of control,” 2020.
  27. T. Baltrušaitis, C. Ahuja, and L.-P. Morency, “Multimodal machine learning: A survey and taxonomy,” IEEE transactions on pattern analysis and machine intelligence, vol. 41, no. 2, pp. 423–443, 2018.