EXPLORING DEEP LEARNING FOR AUTONOMOUS KINETIC ART: FROM ALGORITHMS TO MECHANICAL EXPRESSION
DOI:
https://doi.org/10.29121/shodhkosh.v7.i1.2026.6995Keywords:
Autonomous Kinetic Art, Deep Learning, Computational Creativity, Expressive Motion Synthesis, Robotic Art, Cyber-Physical SystemsAbstract [English]
Autonomous kinetic art is a new interdisciplinary field at the edge of artificial intelligence, robotics, and practice of art, where the movement has become a primary form of artistic expression. Conventional kinetic art objects depend mainly on deterministic processes or a rule-based approach, restricting them to adapting, learning and reacting in any meaningful way to dynamic conditions. The paper discusses how deep learning can be used to empower autonomous kinetic art systems with adaptive, expressive and context-aware mechanical behavior. It suggests a detailed conceptualization of kinetic art as a cyber-physical system that involves multimodal perception and learning-based artistic intelligence, generation of movement and actuation of machinery in a closed-loop structure. Different types of neural network models, convolutional, recurrent and transformer-based are studied in terms of their functions in spatial perception, temporal coherence, and long-range expressive consistency. Motion planning and adaptive control systems are based on learning to convert the abstract neural representations into realizable and expressive motion in the real world taking mechanical and safety limits into account. Experimental assessment uses both quantitative performance metrics and qualitative artistic evaluation using quantitative data (table and plot based) to confirm the smoothness of motion, responsiveness, stability, and energy efficiency of various interaction cases. The case studies of autonomous kinetic installations also illustrate that the system can support its continuous functioning, the development of behaviors and the increase in the level of audience interest. The conclusions state that deep learning has allowed transforming the paradigm of kinetic art of pre-programmed motion into adaptive, learning-driven mechanic expression, which has altered the definition of artistic authorship and pushed the limits of computational creativity and embodied artificial intelligence.
References
Al-Khazraji, L. R., Abbas, A. R., Jamil, A. S., and Hussain, A. J. (2023). A Hybrid Artistic Model Using Deepy-Dream Model and Multiple Convolutional Neural Networks Architectures. IEEE Access, 11, 101443–101459. https://doi.org/10.1109/ACCESS.2023.3315615 DOI: https://doi.org/10.1109/ACCESS.2023.3309419
Cheng, M. (2022). The Creativity of Artificial Intelligence in Art. Proceedings, 81(1), 110. DOI: https://doi.org/10.3390/proceedings2022081110
Elgammal, A., Liu, B., Elhoseiny, M., and Mazzone, M. (2017). CAN: Creative Adversarial Networks, Generating “art” by Learning about Styles and Deviating from Style Norms. arXiv. https://arxiv.org/abs/1706.07068
Gatys, L. A., Ecker, A. S., and Bethge, M. (2015). A Neural Algorithm of Artistic Style. arXiv. https://arxiv.org/abs/1508.06576
Guo, D. H., Chen, H. X., Wu, R. L., and Wang, Y. G. (2023). AIGC Challenges and Opportunities Related to Public Safety: A Case Study of ChatGPT. Journal of Safety Science and Resilience, 4, 329–339. DOI: https://doi.org/10.1016/j.jnlssr.2023.08.001
Karnati, A., and Mehta, D. (2022). Artificial Intelligence in Self-Driving Cars: Applications, Implications and Challenges. Ushus Journal of Business Management, 21, 1–28. https://doi.org/10.12724/ujbm.60.1
Kisačanin, B. (2017). Deep Learning for Autonomous Vehicles. In Proceedings of the 2017 IEEE 47th International Symposium on Multiple-Valued Logic (ISMVL) (142). DOI: https://doi.org/10.1109/ISMVL.2017.49
Leong, W. Y. (2025). Ai-Generated Artwork as a Modern Interpretation of Historical Paintings. International Journal of Social Science and Artistic Innovation, 5, 15–19.
Leong, W. Y. (2025). Ai-Powered Color Restoration of Faded Historical Painting. In Proceedings of the 10th International Conference on Digital Arts, Media and Technology (DAMT) and 8th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (NCON) (623–627). DOI: https://doi.org/10.1109/ECTIDAMTNCON64748.2025.10961986
Leong, W. Y., and Zhang, J. B. (2025). AI on Academic Integrity and Plagiarism Detection. ASM Science Journal, 20, 75. DOI: https://doi.org/10.32802/asmscj.2025.1918
Leong, W. Y., and Zhang, J. B. (2025). Ethical Design of AI for Education and Learning Systems. ASM Science Journal, 20, 1–9. DOI: https://doi.org/10.32802/asmscj.2025.1917
Lou, Y. Q. (2023). Human Creativity in the AIGC Era. Journal of Design, Economics and Innovation, 9, 541–552. DOI: https://doi.org/10.1016/j.sheji.2024.02.002
McCormack, J., Gifford, T., and Hutchings, P. (2019). Autonomy, Authenticity, Authorship and Intention in Computer Generated Art. In Proceedings of the International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART) (35–50). Springer. DOI: https://doi.org/10.1007/978-3-030-16667-0_3
Miglani, A., and Kumar, N. (2019). Deep Learning Models for Traffic Flow Prediction in Autonomous Vehicles: A Review, Solutions, and Challenges. Vehicular Communications, 20, 100184. https://doi.org/10.1016/j.vehcom.2019.100184 DOI: https://doi.org/10.1016/j.vehcom.2019.100184
Oksanen, A., et al. (2023). Artificial Intelligence in Fine Arts: A Systematic Review of Empirical Research. Computers in Human Behavior: Artificial Humans, 1, 100004. DOI: https://doi.org/10.1016/j.chbah.2023.100004
Shao, L. J., Chen, B. S., Zhang, Z. Q., Zhang, Z., and Chen, X. R. (2024). Artificial Intelligence Generated Content (AIGC) in Medicine: A Narrative Review. Mathematical Biosciences and Engineering, 2, 1672–1711. DOI: https://doi.org/10.3934/mbe.2024073
Tikito, I., and Souissi, N. (2019). Meta-analysis of Systematic Literature Review Methods. International Journal of Modern Education and Computer Science, 12, 17–25. DOI: https://doi.org/10.5815/ijmecs.2019.02.03
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Dr. Shirish Jaysing Navale, Dr. Ashish Suresh Patel, Dr. Minal Prashant Nerkar, Dr. Girish Jaysing Navale
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
