REFERENCES
1. Ajoudani, A.; Zanchettin, A. M.; Ivaldi, S.; Albu-Schäffer, A.; Kosuge, K.; Khatib, O. Progress and prospects of the human-robot collaboration. Auton. Robot. 2018, 42, 957-75.
2. Yang, C.; Zhang, X.; Zhang, L.; et al. Coordinated energy-efficient walking assistance for paraplegic patients by using the exoskeleton-walker system. Intell. Robot. 2024, 4, 107-24.
3. Tong, L.; Cui, D.; Wang, C.; Peng, L. A novel zero-force control framework for post-stroke rehabilitation training based on fuzzy-PID method. Intell. Robot. 2024, 4, 125-45.
4. Wang, Z.; Chen, M.; Liu, Q. A review on multimodal communications for human-robot collaboration in 5G: from visual to tactile. Intell. Robot. 2025, 5, 579-606.
5. Gao, Z.; Liao, Z.; Li, C. Better interaction experience: human-machine interface for soft robotic systems. Intell. Robot. 2025, 5, 520-40.
6. Abbink, D. A.; Carlson, T.; Mulder, M.; et al. A topology of shared control systems - finding common ground in diversity. IEEE. Trans. Hum. Mach. Syst. 2018, 48, 509-25.
7. Parasuraman, R.; Sheridan, T. B.; Wickens, C. D. A model for types and levels of human interaction with automation. IEEE. Trans. Syst. Man. Cybern. A. Syst. Hum. 2000, 30, 286-97.
8. Dragan, A. D.; Srinivasa, S. S. A policy-blending formalism for shared control. Int. J. Robot. Res. 2013, 32, 790-805.
9. Abbink, D. A.; Mulder, M.; Boer, E. R. Haptic shared control: smoothly shifting control authority? Cogn. Technol. Work. 2012, 14, 19-28.
10. Flemisch, F.; Heesen, M.; Hesse, T.; Kelsch, J.; Schieben, A.; Beller, J. Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations. Cogn. Technol. Work. 2012, 14, 3-18.
11. Sheridan, T. B. Adaptive automation, level of automation, allocation authority, supervisory control, and adaptive control: distinctions and modes of adaptation. IEEE. Trans. Syst. Man. Cybern. A. Syst. Hum. 2011, 41, 662-7.
12. Jiang, H.; Yang, Y.; Hua, C.; Li, J. A novel ADP-based neurooptimal control methodology for teleoperation systems under interactive shared-control framework. IEEE. Trans. Autom. Sci. Eng. 2026, 23, 7036-48.
13. Wang, W.; Zhang, Y.; Yang, C.; et al. A human-machine shared dual fuzzy authority allocation control strategy for automatic driving vehicle considering driver intention judgement. Expert. Syst. Appl. 2025, 274, 126971.
14. Su, C.; Yang, H.; Li, J.; Wu, X. Steering authority allocation strategy for human-machine shared control based on driver take-over feasibility. Comput. Electr. Eng. 2024, 120, 109753.
15. Ma, Y.; Liu, H.; Yu, Z. Adaptive shared control for robot manipulator obstacle avoidance with dynamic authority allocation. In AI Enabled Robotic Loco-Manipulation. Lecture Notes in Networks and Systems. Springer Nature Switzerland; 2025. pp. 27–38.
16. Sarabia, J.; Marcano, M.; Diaz, S.; Rastelli, J. P.; Zubizarreta, A. Evaluating shared control in real-world conditions. IEEE. Open. J. Veh. Technol. 2026, 7, 418-31.
17. Matsumoto, S.; Riek, L. D. Shared control in human robot teaming: toward context-aware communication. arXiv 2022, arXiv:2203.10218. Available online: https://doi.org/10.48550/arXiv.2203.10218. (accessed on 22 Jul 2026).
18. Xiao, J.; Wang, B.; Huang, K.; Terzi, S.; Wang, W.; Macchi, M. Intelligent disassembly scenario understanding for human behavior and intention recognition towards self-perception human-robot collaboration system. J. Manuf. Syst. 2025, 79, 937-62.
19. Xing, X.; Burdet, E.; Si, W.; Yang, C.; Li, Y. Impedance learning for human-guided robots in contact with unknown environments. IEEE. Trans. Robot. 2023, 39, 3705-21.
20. Johnson, A. W.; Duda, K. R.; Sheridan, T. B.; Oman, C. M. A closed-loop model of operator visual attention, situation awareness, and performance across automation mode transitions. Hum. Factors. 2017, 59, 229-41.
21. Lee, J. D.; See, K. A. Trust in automation: designing for appropriate reliance. Hum. Factors. 2004, 46, 50-80.
22. Kraus, J.; Scholz, D.; Stiegemeier, D.; Baumann, M. The more you know: trust dynamics and calibration in highly automated driving and the effects of take-overs, system malfunction, and system transparency. Hum. Factors. 2020, 62, 718-36.
23. Elgohr, A. T.; Rashad, M.; El-Gendy, E. M.; Shaaban, W.; Saafan, M. M. Trust as a design principle in human-robot collaboration: a review of explainable and adaptive control. Artif. Intell. Rev. 2026, 59, 134.
24. Hergeth, S.; Lorenz, L.; Krems, J. F. Prior familiarization with takeover requests affects drivers' takeover performance and automation trust. Hum. Factors. 2017, 59, 457-70.
25. Muir, B. M. Trust in automation: Part Ⅰ. Theoretical issues in the study of trust and human intervention in automated systems. Ergonomics 1994, 37, 1905-22.
26. Mackworth, N. H. The breakdown of vigilance during prolonged visual search. Q. J. Exp. Psychol. 1948, 1, 6-21.
27. Warm, J,. S.; Parasuraman, R.; Matthews, G. Vigilance requires hard mental work and is stressful. Hum. Factors. 2008, 50, 433-41.
28. Hua, C.; Li, Y.; Guan, X. Finite/fixed-time stabilization for nonlinear interconnected systems with dead-zone input. IEEE. Trans. Autom. Control. 2017, 62, 2554-60.
29. Hua, C.; Ning, P.; Li, K. Adaptive prescribed-time control for a class of uncertain nonlinear systems. IEEE. Trans. Autom. Control. 2022, 67, 6159-66.
30. Ames, A. D.; Xu, X.; Grizzle, J. W.; Tabuada, P. Control barrier function based quadratic programs for safety critical systems. IEEE. Trans. Autom. Control. 2017, 62, 3861-76.
31. Jankovic, M. Robust control barrier functions for constrained stabilization of nonlinear systems. Automatica 2018, 96, 359-67.
32. Salehi, I.; Rotithor, G.; Dani, A. Safe adaptive trajectory tracking control of robot for human-robot interaction using barrier function transformation. In Collaborative and Humanoid Robots. London: IntechOpen; 2021.
33. Maithani, P.; Arab, A.; Khorrami, F.; Krishnamurthy, P. Proactive hierarchical control barrier function-based safety prioritization in close human-robot interaction scenarios. arXiv 2025, arXiv:2505.16055. Available online: https://doi.org/10.48550/arXiv.2505.16055. (accessed on 22 Jul 2026).
34. Janwani, N. C.; Daş, E.; Touma, T.; Wei, S. X.; Molnar, T. G.; Burdick, J. W. A learning-based framework for safe human-robot collaboration with multiple backup control barrier functions. arXiv 2023, arXiv:2310.05865. Available online: https://doi.org/10.48550/arXiv.2310.05865. (accessed on 22 Jul 2026).
35. Xiong, Y.; Zhai, D. H.; Xia, Y. Robust safety-critical control design for robotic systems under input disturbances with multiple time-varying constraints. Int. J. Syst. Sci. 2026.
36. Zhang, Y.; Xie, W.; Ma, R. Disturbance observer-based terminal sliding mode control for the training safety improvement in robot-assisted rehabilitation. Intell. Robot. 2025, 5, 333-54.
37. He, L.; Peng, Z.; Zhang, X.; et al. Reinforcement learning-based fixed-time optimal impedance control for human-robot collaboration with input disturbances. IEEE. Internet. Things. J. 2025, 12, 54638-51.
38. Busellato, L.; Cunico, F.; Dall’Alba, D.; et al. Uncertainty aware-predictive control barrier functions: safer human-robot interaction through probabilistic motion forecasting. Robot. Auton. Syst. 2026, 197, 105291.
39. Sato, E.; Wada, T. Human-centered cooperative control coupling autonomous and haptic shared control via control barrier function. IFAC-Pap. 2025, 58, 280-5.
