ARCHIVES
Original Article
AI-Driven Intent Prediction and Adaptive Safety in Human–Robot Collaboration: A Review and Conceptual Framework for Smart Manufacturing
Dr Abhishek Kumar1
Tanisha2
1 Head, Department of Computer Science, S A Jain College, Ambala City, Haryana, India. 2 Department of Computer Science, S A Jain College, Ambala City, Haryana, India.
Published Online: March-April 2026
Pages: 125-131
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702018
References
1. G. Hoffman, T. Bhattacharjee, and S. Nikolaidis, “Inferring human intent and predicting human action in human–robot collaboration,” Annu. Rev. Control Robot. Auton. Syst., vol. 7, pp. 73–95, 2024.
2. Y. Su et al., “Intentions prediction for human–robot collaboration using hybrid deep learning,” Eng., Constr. Archit. Manag., 2025.
3. W. Liu et al., “Early prediction of human intention for human–robot collaboration using transformer networks,” J. Comput. Inf. Sci. Eng., 2024.
4. M. Mavsar et al., “Human intention recognition using LSTM and transformer-based models,” Sensors, 2025.
5. M. Kekana et al., “A review of human intention recognition frameworks in collaborative robotics,” Robotics, 2025.
6. Y. Shao et al., “Constraint-aware intent estimation for dynamic human–robot collaboration,” arXiv, 2024.
7. V. Hernandez-Cruz et al., “Bayesian intention framework for enhanced human–robot collaboration,” arXiv, 2024.
8. M. Cai et al., “Hierarchical deep learning for intention estimation in assembly tasks,” arXiv, 2024.
9. M. Zolotas and Y. Demiris, “Disentangled sequence clustering for human intention inference,” IEEE/ArXiv, 2021.
10. W. Wang et al., “Predicting human intentions in human–robot hand-over tasks through multimodal learning,” IEEE Trans. Autom. Sci. Eng., vol. 19, no. 3, pp. 2339–2353, 2022.
11. T. Zhou et al., “Human motion prediction using machine learning for human–robot collaboration,” ASCE, 2021.
12. J. Jiang et al., “Short-term human intention prediction using deep neural networks,” Neurocomputing, 2020.
A. Villani et al., “Survey on human–robot collaboration in industrial settings,” IEEE Access, vol. 8, pp. 12345–12367, 2020.
13. S. Haddadin and E. Croft, “Physical human–robot interaction,” IEEE Robotics & Automation Mag., 2020.
14. M. R. Pedersen et al., “Robot skills for manufacturing: From concept to industrial deployment,” Robot. Comput.-Integr. Manuf., 2021.
15. J. Krüger et al., “Human–robot collaboration in industrial assembly,” CIRP Annals, 2021.
A. Gupta et al., “Deep learning for human activity recognition: A survey,” Pattern Recognit. Lett., 2021.
16. H. Rahman et al., “Multimodal sensor fusion for human activity recognition,” IEEE Sensors J., 2022.
17. L. Chen et al., “Real-time human intention prediction using deep learning,” IEEE Access, 2022.
18. ISO 10218-1:2020, “Robots and robotic devices—Safety requirements for industrial robots,” 2020.
19. ISO/TS 15066:2021, “Collaborative robot safety requirements,” 2021.
20. P. Fiorini et al., “Adaptive safety in collaborative robotics,” IEEE Trans. Robot., 2023.
21. K. Zhou et al., “Industry 4.0: Towards future industrial opportunities,” IEEE Ind. Electron. Mag., 2020.
22. R. S. Sutton and A. G. Barto, “Reinforcement learning in robotics systems,” IEEE, 2020.
23. J. Redmon et al., “Real-time object detection for robotic perception,” IEEE, 2021.
24. B. Siciliano and O. Khatib, “Springer handbook of robotics (latest applications),” Springer, 2022.
2. Y. Su et al., “Intentions prediction for human–robot collaboration using hybrid deep learning,” Eng., Constr. Archit. Manag., 2025.
3. W. Liu et al., “Early prediction of human intention for human–robot collaboration using transformer networks,” J. Comput. Inf. Sci. Eng., 2024.
4. M. Mavsar et al., “Human intention recognition using LSTM and transformer-based models,” Sensors, 2025.
5. M. Kekana et al., “A review of human intention recognition frameworks in collaborative robotics,” Robotics, 2025.
6. Y. Shao et al., “Constraint-aware intent estimation for dynamic human–robot collaboration,” arXiv, 2024.
7. V. Hernandez-Cruz et al., “Bayesian intention framework for enhanced human–robot collaboration,” arXiv, 2024.
8. M. Cai et al., “Hierarchical deep learning for intention estimation in assembly tasks,” arXiv, 2024.
9. M. Zolotas and Y. Demiris, “Disentangled sequence clustering for human intention inference,” IEEE/ArXiv, 2021.
10. W. Wang et al., “Predicting human intentions in human–robot hand-over tasks through multimodal learning,” IEEE Trans. Autom. Sci. Eng., vol. 19, no. 3, pp. 2339–2353, 2022.
11. T. Zhou et al., “Human motion prediction using machine learning for human–robot collaboration,” ASCE, 2021.
12. J. Jiang et al., “Short-term human intention prediction using deep neural networks,” Neurocomputing, 2020.
A. Villani et al., “Survey on human–robot collaboration in industrial settings,” IEEE Access, vol. 8, pp. 12345–12367, 2020.
13. S. Haddadin and E. Croft, “Physical human–robot interaction,” IEEE Robotics & Automation Mag., 2020.
14. M. R. Pedersen et al., “Robot skills for manufacturing: From concept to industrial deployment,” Robot. Comput.-Integr. Manuf., 2021.
15. J. Krüger et al., “Human–robot collaboration in industrial assembly,” CIRP Annals, 2021.
A. Gupta et al., “Deep learning for human activity recognition: A survey,” Pattern Recognit. Lett., 2021.
16. H. Rahman et al., “Multimodal sensor fusion for human activity recognition,” IEEE Sensors J., 2022.
17. L. Chen et al., “Real-time human intention prediction using deep learning,” IEEE Access, 2022.
18. ISO 10218-1:2020, “Robots and robotic devices—Safety requirements for industrial robots,” 2020.
19. ISO/TS 15066:2021, “Collaborative robot safety requirements,” 2021.
20. P. Fiorini et al., “Adaptive safety in collaborative robotics,” IEEE Trans. Robot., 2023.
21. K. Zhou et al., “Industry 4.0: Towards future industrial opportunities,” IEEE Ind. Electron. Mag., 2020.
22. R. S. Sutton and A. G. Barto, “Reinforcement learning in robotics systems,” IEEE, 2020.
23. J. Redmon et al., “Real-time object detection for robotic perception,” IEEE, 2021.
24. B. Siciliano and O. Khatib, “Springer handbook of robotics (latest applications),” Springer, 2022.
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