Issue
Mechanics & Industry
Volume 27, 2026
Artificial Intelligence in Mechanical Manufacturing: From Machine Learning to Generative Pre-trained Transformer
Article Number 9
Number of page(s) 15
DOI https://doi.org/10.1051/meca/2026002
Published online 05 March 2026
  1. P. Asgharian, A.M. Panchea, F. Ferland, A review on the use of mobile service robots in elderly care, J. Robotics, 11, 127–154 (2022) [Google Scholar]
  2. L. Guo, L. Gong, Z. Xu, W. Wang, M.H. Chen, The role of service robots in enhancing customer satisfaction in embarrassing contexts, J. Hosp. Tour. Manag. 59, 116–126 (2024) [Google Scholar]
  3. X. Zhu, B. Zhang, Y. Qiu, S.A. Chepinskiy, An interaction behavior decision-making model of service robots for the disabled based on human–robot empathy, IEEE Access, 12, 15778–15790 (2024) [Google Scholar]
  4. O. Nocentini, J. Kim, Z.M. Bashir, F. Cavallo, Learning-based control approaches for service robots on cloth manipulation and dressing assistance: a comprehensive review, J. NeuroEng. Rehabil. 19, 117–142 (2022) [Google Scholar]
  5. W. Jebrane, N. ElAkchioui, Advancing distributed distributional deterministic policy gradients for autonomous robotic control across diverse environments, J. Control, Autom. Electr. Syst. 5, 1059–1077 (2024) [Google Scholar]
  6. X. Gao, L. Yan, Z. Li, G. Wang, I.M. Chen, Improved deep deterministic policy gradient for dynamic obstacle avoidance of mobile robot, IEEE Trans. Syst. Man Cybern.: Syst. 53, 3675–3682 (2023) [Google Scholar]
  7. Z. Chen, Y. Nakamura, H. Ishiguro, Android as a receptionist in a shopping mall using inverse reinforcement learning, IEEE Robot. Autom. Lett. 7, 7091–7098 (2022) [Google Scholar]
  8. G. Abbate, A. Giusti, V. Schmuck, O. Celiktutan, A. Paolillo, Self-supervised prediction of the intention to interact with a service robot, Robot. Auton. Syst. 171, 104568–104577 (2024) [Google Scholar]
  9. A. Khan, J.P. Li, M.K. Hasan, N. Varish, Z. Mansor, S. Islam, R.A. Saeed, M. Alshammari, H. Alhumyani, PackerRobo: Model-based robot vision self supervised learning in CART, Alex. Eng. J. 61, 12549–12566 (2022) [Google Scholar]
  10. E. Kipnis, F. McLeay, A. Grimes, S. De Saille, S. Potter, Service robots in long-term care: a consumer-centric view, J. Serv. Res. 25, 667–685 (2022) [Google Scholar]
  11. S. Ozturkcan, E. Merdin-Uygur, Humanoid service robots: the future of healthcare?, J. Inf. Technol. Teach. Cases, 12, 163–169 (2022) [Google Scholar]
  12. O. Demir, A. Vatan, Robotisation in travel and tourism: tourist guides' perspectives on robot guides, Tour. Manag. Stud. 20, 13–23 (2024) [Google Scholar]
  13. S. Rosa, M. Randazzo, E. Landini, S. Bernagozzi, G. Sacco, M. Piccinino, L. Natale, Tour guide robot: a 5G-enabled robot museum guide, Front. Robot. AI, 10, 1323675–1323683 (2024) [Google Scholar]
  14. H. Abdollahi, M.H. Mahoor, R. Zandie, J. Siewierski, S.H. Qualls, Artificial emotional intelligence in socially assistive robots for older adults: a pilot study, IEEE Trans. Affect. Comput. 14, 2020–2032 (2022) [Google Scholar]
  15. J.D. Huang, I.A. Wong, Q.L. Lian, H. Huang, Robotic companionship for solo diners: the role of robotic service type, need to belong and restaurant type, Int. J. Contemp. Hosp. Manag. 37, 890–917 (2025) [Google Scholar]
  16. Y. Cui, Y. Zhang, C.H. Zhang, S.X. Yang, Task cognition and planning for service robots, Intell. Robot. 5, 119–142 (2025) [Google Scholar]
  17. M. Jorda, M. Vulliez, O. Khatib, Local autonomy-based haptic-robot interaction with dual-proxy model, IEEE Trans. Robot. 38, 2943–2961 (2022) [Google Scholar]
  18. A. Hernandez, R.M. Ortega-Mendoza, E. Villatoro-Tello, C.J. Camacho-Bello, O. Perez-Cortes, Natural language understanding for navigation of service robots in low-resource domains and languages: scenarios in spanish and nahuatl, Mathematics, 12, 1136–1158 (2024) [Google Scholar]
  19. R. Filieri, Z. Lin, Y. Li, X. Lu, X. Yang, Customer emotions in service robot encounters: a hybrid machine-human intelligence approach, J. Serv. Res. 25, 614–629 (2022) [Google Scholar]
  20. S. Duan, Q. Shi, J. Wu, Multimodal sensors and ML‐based data fusion for advanced robots, Adv. Intell. Syst. 4, 2200213–2200222 (2022) [Google Scholar]
  21. T. Gong, D. Chen, G. Wang, W. Zhang, J. Zhang, Z. Ouyang, F. Zhan, R. Sun, J.C. Ji, W. Chen, Multimodal fusion and human-robot interaction control of an intelligent robot, Front. Bioeng. Biotechnol. 11, 1310247–1310257 (2024) [Google Scholar]
  22. K.J. Wang, C.J. Lin, A.A. Tadesse, B.H. Woldegiorgis, Modeling of human–robot collaboration for flexible assembly–A hidden semi-Markov-based simulation approach, Int. J. Adv. Manuf. Technol. 126, 5371–5389 (2023) [Google Scholar]
  23. G. Braglia, M. Tagliavini, F. Pini, L. Biagiotti, Online motion planning for safe human–robot cooperation using b-splines and hidden markov models, Robotics, 12, 118–143 (2023) [Google Scholar]
  24. P. Bachiller, D. Rodriguez-Criado, R.R. Jorvekar, P. Bustos, D.R. Faria, L.J. Manso, A graph neural network to model disruption in human-aware robot navigation, Multimed. Tools Appl. 81, 3277–3295 (2022) [Google Scholar]
  25. Y. Yang, R. Yang, Y. Li, K. Cui, Z. Yang, Y. Wang, J. Xu, H. Xie, Rosgas: adaptive social bot detection with reinforced self-supervised gnn architecture search, ACM Trans. Web, 17, 1–31 (2023) [Google Scholar]
  26. H. Lv, H. Yan, K. Liu, Z. Zhou, J. Jing, Yolov5-ac: attention mechanism-based lightweight yolov5 for track pedestrian detection, Sensors, 22, 5903–5928 (2022) [Google Scholar]
  27. B. Nikpour, D. Sinodinos, N. Armanfard, Deep reinforcement learning in human activity recognition: a survey and outlook, IEEE Trans. Neural Netw. Learn. Syst. 36, 4267–4278 (2024) [Google Scholar]
  28. Z. Lv, F. Poiesi, Q. Dong, J. Lloret, H. Song, Deep learning for intelligent human–computer interaction, Appl. Sci. 12, 11457–11485 (2022) [Google Scholar]
  29. B. Singh, R. Kumar, V.P. Singh, Reinforcement learning in robotic applications: a comprehensive survey, Artif. Intell. Rev. 55, 945–990 (2022) [Google Scholar]
  30. F. Munguia-Galeano, S. Veeramani, J.D. Hernandez, Q. Wen, Z. Ji, Affordance-based human–robot interaction with reinforcement learning, IEEE Access, 11, 31282–31292 (2023) [Google Scholar]
  31. S. Adams, T. Cody, P.A. Beling, A survey of inverse reinforcement learning, Artif. Intell. Rev. 55, 4307–4346 (2022) [Google Scholar]
  32. R. Devidze, P. Kamalaruban, A. Singla, Exploration-guided reward shaping for reinforcement learning under sparse rewards, Adv. Neural Inf. Process. Syst. 35, 5829–5842 (2022) [Google Scholar]

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