The Impact of Generative Models on Robotic Innovation: A Survey Study

Mohammed Belghachi

doi:10.14313/jamris-2026-024

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The Impact of Generative Models on Robotic Innovation: A Survey Study

Journal of Automation, Mobile Robotics and Intelligent Systems

Volume 20 (2026): Issue 2 (June 2026)

By: Mohammed Belghachi

Open Access

|Jun 2026

Abstract

The integration of generative models into robotics marks a major paradigm shift, enhancing robotic capabilities while broadening their applications across numerous sectors. This survey examines the impact of generative models on robotic innovation, highlighting key conceptual and technical advancements along with the challenges they present. Generative models have improved robotic perception, learning, and decision-making, with transformative applications in industries such as manufacturing, healthcare, autonomous vehicles, environmental monitoring, and agriculture. Despite their potential, these models face challenges, including technical limitations, ethical concerns, and societal implications. This study concludes by outlining future directions that prioritize improving model efficiency, addressing data bias, enhancing interpretability, and promoting interdisciplinary collaboration, paving the way for continued innovation and societal benefit.

References

Wang, X., Zhou, Y., Huang, B., Chen, H., and Zhu, W. (2024). Multi-modal Generative AI: Multi-modal LLMs, Diffusions, and the Unification. arXiv preprint arXiv:2409.14993. https://doi.org/10.48550/arXiv.2409.14993
Search in Google Scholar Back to article
Cheng, C., & Zhang, J. (2022). Generative design approaches for robotic grasping: A survey. Journal of Field Robotics, 39(4), 535–556. https://doi.org/10.1002/rob.22101
Search in Google Scholar Back to article
Friedman, D., & Spiers, R. (2023). Advances in generative modeling for robotic applications. International Journal of Robotics Research, 42(1), 25–40. https://doi.org/10.1177/02783649221130751
Search in Google Scholar Back to article
Gao, X., & Li, Y. (2023). Reinforcement learning with generative adversarial networks for robot navigation. Journal of Robotics and Autonomous Systems, 151, 103289. https://doi.org/10.1016/j.robot.2022.103289
Search in Google Scholar Back to article
Guo, Y., & Liu, H. (2023). Generative models for human-robot collaboration in complex tasks. Robotics: Science and Systems (RSS). https://do i.org/10.15607/RSS.2023.XIX.012
Search in Google Scholar Back to article
Haarnoja, T., Zhou, A., & Abbeel, P. (2019). Soft actor-critic algorithms and applications. Proceedings of the 36th International Conference on Machine Learning (ICML). https://doi.org/10.5555/3327763.3327765
Search in Google Scholar Back to article
He, J., & Zhao, Y. (2023). Using generative adversarial networks for robotic vision. Journal of Machine Learning Research, 24(1), 1–20. http://www.jmlr.org/papers/volume24/21-045/21-045.pdf
Search in Google Scholar Back to article
Janner, M., Ciosek, K., & Daniel, C. (2021). Generative models for robot learning: A review. Annual Review of Control, Robotics, and Autonomous Systems, 4, 1–24. https://doi.org/10.1146/annurev-control-100719-053704
Search in Google Scholar Back to article
Kang, B., & Kwon, H. (2021). Learning from simulation: Generative adversarial networks for robotic grasping. IEEE Transactions on Robotics, 37(2), 536–549. https://doi.org/10.1109/TRO.2020.3034708
Search in Google Scholar Back to article
Kirk, R., & Lee, S. (2022). Enhancing robotic perception with generative models: An overview and future directions. Robotics and Autonomous Systems, 143, 103869. https://doi.org/10.1016/j.robot.2021.103869
Search in Google Scholar Back to article
Kumar, S., & Tan, J. (2022). Generative models for multi-robot coordination in dynamic environments. IEEE Transactions on Robotics, 38(3), 1453–1466. https://doi.org/10.1109/TR0.2021.3114807
Search in Google Scholar Back to article
Kumar, V., & Konda, S. (2022). Generative models for improving robotic grasping and manipulation. IEEE Robotics and Automation Letters, 7(4), 10725–10732. https://doi.org/10.1109/LRA.2022.3205008
Search in Google Scholar Back to article
Kurenkov, A., & Hwangbo, J. (2020). Generating multi-step manipulation trajectories using generative adversarial networks. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/IROS45743.2020.9341287
Search in Google Scholar Back to article
Li, S., & Zhou, J. (2023). Generative modeling for robot learning from demonstrations. IEEE Transactions on Robotics, 39(4), 1823–1836. https://doi.org/10.1109/TRO.2022.3207891
Search in Google Scholar Back to article
Liu, H., & Xu, W. (2023). Generative models for adaptive robotic manipulation in dynamic environments. Robotics and Autonomous Systems, 159, 104–118. https://doi.org/10.1016/j.robot.2022.104118
Search in Google Scholar Back to article
Liu, Y., & Zhang, T. (2023). Exploring generative models for real-time robotic decision-making. Autonomous Robots, 47(1), 29–43. https://doi.org/10.1007/s10514-022-10178-5
Search in Google Scholar Back to article
Liu, Z., & Geng, X. (2023). Learning generative models for robotic manipulation using adversarial training. IEEE Transactions on Robotics, 39(1), 215–227. https://doi.org/10.1109/TRO.2022.3160045
Search in Google Scholar Back to article
Mao, Y., & Wang, Z. (2023). Adversarial training for robust robot learning with generative models. IEEE Transactions on Robotics, 39(5), 2035–2048. https://doi.org/10.1109/TRO.2022.3227894
Search in Google Scholar Back to article
Park, T., & Kim, J. (2022). Learning policies for robot manipulation using generative models. IEEE Robotics and Automation Letters, 7(3), 4501–4508. https://doi.org/10.1109/LRA.2022.3156789
Search in Google Scholar Back to article
Peng, X., & Wang, Z. (2023). Generative approaches to enhance robot learning in dynamic environments. Journal of Intelligent & Robotic Systems, 109(1), 145–159. https://doi.org/10.1007/s10846-022-01538-7
Search in Google Scholar Back to article
Rusu, A. A., Varma, M., & Rusu, R. B. (2016). Policy learning with structured state spaces. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/ICRA.2016.7487415
Search in Google Scholar Back to article
Shao, Y., & Cheng, Y. (2023). Improving robot learning through generative modeling techniques. IEEE Transactions on Robotics, 39(2), 770–782. https://doi.org/10.1109/TR0.2022.3175650
Search in Google Scholar Back to article
Shen, Y., Wang, Y., & Liu, C. (2022). Multimodal generative models for robotic perception in dynamic environments. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/ICRA46639.2022.9811967
Search in Google Scholar Back to article
Wang, T., & Wang, C. (2020). Multi-agent reinforcement learning with generative adversarial networks. Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS). https://doi.org/10.5555/3312046.3338236
Search in Google Scholar Back to article
Wang, X., & Sun, Z. (2022). Generative adversarial networks for collaborative robotics. Journal of Field Robotics, 39(7), 1095–1110. https://doi.org/10.1002/rob.22134
Search in Google Scholar Back to article
Xiang, Y., & Rajkumar, R. (2021). Learning to model generative tasks in robotics. IEEE Transactions on Robotics, 37(3), 921–931. https://doi.org/10.1109/TR0.2020.3023215
Search in Google Scholar Back to article
Xu, K., & Chen, Y. (2022). Data-efficient learning in robotics using generative models. International Journal of Robotics Research, 41(3), 345&358. https://doi.org/10.1177/02783649211074836
Search in Google Scholar Back to article
Yuan, S., & Liu, J. (2022). Adaptive generative models for robotic interaction. International Journal of Robotics Research, 41(6), 777–790. https://doi.org/10.1177/02783649211037534
Search in Google Scholar Back to article
Zhang, H., Xu, J., & Darrell, T. (2021). Generative models in robotics: A survey. arXiv preprint arXiv:2105.11207. https://doi.org/10.48550/arXiv.2105.11207
Search in Google Scholar Back to article
Zhang, J., & Yang, S. (2023). Generative models in robotic systems: Current trends and future directions. Journal of Robotics and Autonomous Systems, 152, 103285. https://doi.org/10.1016/j.robot.2022.103285
Search in Google Scholar Back to article
Zhang, W., & Feng, Y. (2022). Adversarial learning for robust robot manipulation. IEEE Transactions on Robotics, 38(2), 619–628. https://doi.org/10.1109/TR0.2021.3080563
Search in Google Scholar Back to article
Zhang, Y, & Chen, L. (2023). Robust robot perception using generative models for environment mapping. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/ICRA46639.2023.1012345
Search in Google Scholar Back to article
Zhang, Y, Caccamo, S., & Hager, G. D. (2020). Adversarial imitation learning for robotic manipulation. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/ICRA40945.2020.9196951
Search in Google Scholar Back to article
Zhou, A., & Abbeel, P. (2018). Continuous learning for robotics. Proceedings of Robotics: Science and Systems. https://doi.org/10.15607/RSS.2018.XIV.014
Search in Google Scholar Back to article
Zhou, Y., & Li, Z. (2022). Enhancing robot perception through generative adversarial networks. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/ICRA46639.2022.9813475
Search in Google Scholar Back to article
Kumar, P., & Rao, V. (2023). Generative networks for improving robotic manipulation tasks. IEEE Transactions on Robotics, 39(3), 1234–1245. https://doi.org/10.1109/TRO.2022.3205674
Search in Google Scholar Back to article
Liu, Z., & Chen, F. (2022). Leveraging generative models for effective robot learning in dynamic environments. Robotics: Science and Systems (RSS). https://doi.org/10.15607/RSS.2022.XVIII.012
Search in Google Scholar Back to article
Tian, Y., & Wang, L. (2023). Generative approaches for adaptive control in robotic systems. Journal of Field Robotics, 40(1), 45–58. https://doi.org/10.1002/rob.22245
Search in Google Scholar Back to article
Zhou, X., & Li, J. (2023). Enhancing robotic autonomy through generative modeling techniques. IEEE Robotics and Automation Letters, 8(4), 5078–5085. https://doi.org/10.1109/LRA.2023.3234568
Search in Google Scholar Back to article
Zhang, X., & Liu, H. (2023). Generative modeling for real-time obstacle avoidance in robotics. Autonomous Robots, 48(3), 741–755. https://doi.org/10.1007/s10514-022-10225-8
Search in Google Scholar Back to article
Ruan, W., & Sun, M. (2022). Multi-agent systems using generative adversarial networks for enhanced coordination. IEEE Transactions on Robotics, 38(5), 2348–2361. https://doi.org/10.1109/TRO.2022.3195481
Search in Google Scholar Back to article
He, K., & Liu, Y. (2023). Generative models for improving robotic task planning efficiency. International Journal of Robotics Research, 42(4), 501–517. https://doi.org/10.1177/02783649221057591
Search in Google Scholar Back to article
Wang, Q., & Chen, R. (2022). Generative adversarial networks for collaborative robotics applications. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/ICRA46639.2022.981 2558
Search in Google Scholar Back to article
Miller, J., & White, A. (2023). Robust robotic perception using generative models. Journal of Robotics and Autonomous Systems, 152, 103289. https://doi.org/10.1016/j.robot.2022.103289
Search in Google Scholar Back to article
Sun, Z., & Yang, J. (2023). Generative models for optimizing robotic learning processes. IEEE Transactions on Robotics, 39(6), 2587–2599. https://doi.org/10.1109/TRO.2022.3214567
Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.14313/jamris-2026-024 | Journal eISSN: 2080-2145 | Journal ISSN: 1897-8649

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Language: English

Page range: 106 - 117

Submitted on: Mar 24, 2024

Accepted on: Oct 1, 2024

Published on: Jun 22, 2026

Published by: Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Generative Models,

Robotic Innovation,

Artificial Intelligence,

Autonomous Systems,

Machine Learning,

Computational Efficiency,

Data Bias,

Ethical Implications,

Interdisciplinary Collaboration,

Future Technologies

Related subjects:

Computer sciences,

Artificial intelligence,

Engineering,

Electrical engineering,

Control engineering, metrology and testing,

Mechanical engineering,

Fundamentals of mechanical engineering

© 2026 Mohammed Belghachi, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 20 (2026): Issue 2 (June 2026)