REFERENCES

1. Hogan, N. Impedance control: an approach to manipulation: Part I - theory. J. Dyn. Sys. Meas. Control. 1985, 107, 1-7.

2. Billard, A.; Kragic, D. Trends and challenges in robot manipulation. Science 2019, 364, eaat8414.

3. Abu-Dakka, F. J.; Saveriano, M. Variable impedance control and learning - a review. Front. Robot. AI. 2020, 7, 590681.

4. Johannink, T.; Bahl, S.; Nair, A.; et al. Residual reinforcement learning for robot control. In 2019 International Conference on Robotics and Automation (ICRA), Montreal, Canada. May 20-24, 2019. IEEE; 2019. pp. 60239.

5. Mir, B. A.; Nishwa, D. E.; Lee, S. W. Embodied artificial intelligence in healthcare: a systematic review of robotic perception, decision-making, and clinical impact. Healthcare 2026, 14, 572.

6. Yu, W.; Perrusquía, A. Human-robot interaction control using reinforcement learning. Hoboken, NJ, USA: Wiley-IEEE Press, 2021.

7. Perrusquia, A.; Yu, W. Neural H₂ control using continuous-time reinforcement learning. IEEE. Trans. Cybern. 2022, 52, 4485-94.

8. Angelopoulos, A. N.; Bates, S. Conformal prediction: a gentle introduction. Found. Trends. Mach. Learn. 2023, 16, 494-591.

9. Brohan, A.; Brown, N.; Carbajal, J.; et al. RT-2: vision-language-action models transfer web knowledge to robotic control. arXiv 2023, arXiv:2307.15818. Available online: https://doi.org/10.48550/arXiv.2307.15818. (accessed on 2026-08-21).

10. Ding, Y.; Zhang, X.; Paxton, C.; Zhang, S. Task and motion planning with large language models for object rearrangement. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, USA. Oct 01-05, 2023. IEEE; 2023. pp. 2086-92.

11. Silver, T.; Allen, K.; Tenenbaum, J.; Kaelbling, L. Residual policy learning. arXiv 2018, arXiv:1812.06298. Available online: https://doi.org/10.48550/arXiv.1812.06298. (accessed on 2026-08-21).

12. Keemink, A. Q. L.; van der Kooij, H.; Stienen, A. H. A. Admittance control for physical human–robot interaction. Int. J. Robot. Res. 2018, 37, 1421-44.

13. Ferraguti, F.; Talignani Landi, C.; Sabattini, L.; Bonfè, M.; Fantuzzi, C.; Secchi, C. A variable admittance control strategy for stable physical human–robot interaction. Int. J. Robot. Res. 2019, 38, 747-65.

14. Ficuciello, F.; Villani, L.; Siciliano, B. Variable impedance control of redundant manipulators for intuitive human-robot physical interaction. IEEE. Trans. Robot. 2015, 31, 850-63.

15. Yang, C.; Ganesh, G.; Haddadin, S.; Parusel, S.; Albu-Schaeffer, A.; Burdet, E. Human-like adaptation of force and impedance in stable and unstable interactions. IEEE. Trans. Robot. 2011, 27, 918-30.

16. Argall, B. D.; Chernova, S.; Veloso, M.; Browning, B. A survey of robot learning from demonstration. Robot. Auton. Syst. 2009, 57, 469-83.

17. Ravichandar, H.; Polydoros, A. S.; Chernova, S.; Billard, A. Recent advances in robot learning from demonstration. Annu. Rev. Control. Robot. Auton. Syst. 2020, 3, 297-330.

18. Kaufmann, T.; Weng, P.; Bengs, V.; Hüllermeier, E. A survey of reinforcement learning from human feedback. arXiv 2023, arXiv:2312.14925. Available online: https://doi.org/10.48550/arXiv.2312.14925. (accessed on 2026-08-21).

19. Javdani, S.; Admoni, H.; Pellegrinelli, S.; Srinivasa, S. S.; Bagnell, J. A. Shared autonomy via hindsight optimization for teleoperation and teaming. J. Robot. Res. 2018, 37, 717-42.

20. Dragan, A. D.; Srinivasa, S. S. A policy-blending formalism for shared control. Int. J. Robot. Res. 2013, 32, 790-805.

21. Reddy, S.; Dragan, A. D.; Levine, S. Shared autonomy via deep reinforcement learning. arXiv 2018, arXiv:1802.01744. Available online: https://doi.org/10.48550/arXiv.1802.01744. (accessed on 2026-08-21).

22. Losey, D. P.; Mcdonald, C. G.; Battaglia, E.; O’Malley, M. K. A review of intent detection, arbitration, and communication aspects of shared control for physical human–robot interaction. Appl. Mech. Rev. 2018, 70, 010804.

23. Brunke, L.; Greeff, M.; Hall, A. W.; et al. Safe learning in robotics: from learning-based control to safe reinforcement learning. Annu. Rev. Control. Robot. Auton. Syst. 2022, 5, 411-44.

24. Hewing, L.; Wabersich, K. P.; Menner, M.; Zeilinger, M. N. Learning-based model predictive control: toward safe learning in control. Annu. Rev. Control. Robot. Auton. Syst. 2020, 3, 269-96.

25. Cheng, R.; Orosz, G.; Murray, R. M.; Burdick, J. W. End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks. arXiv 2019, arXiv:1903.08792. Available online: https://doi.org/10.48550/arXiv.1903.08792. (accessed on 2026-08-21).

26. Thumm, J.; Frei, M.; Ni, T.; Althoff, M.; Pavone, M. Vision-based safe human-robot collaboration with uncertainty guarantees. arXiv 2026, arXiv:2604.15221. Available online: https://doi.org/10.48550/arXiv.2604.15221. (accessed on 2026-08-21).

27. Zhou, H.; Zhang, Y.; Luo, W. Safety-critical control with uncertainty quantification using adaptive conformal prediction. In 2024 American Control Conference (ACC), Toronto, Canada. Jul 10-12, 2024. IEEE; 2024. pp. 574-80.

28. Gonzales, J.; Mizuta, K.; Leung, K.; Ratliff, L. J. Safe probabilistic planning for human-robot interaction using conformal risk control. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hangzhou, China. Oct 19-25, 2025. IEEE; 2025. pp. 18676-83.

29. 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.

30. Zhang, D.; Van, M.; McIlvanna, S.; Sun, Y.; McLoone, S. Adaptive safety-critical control with uncertainty estimation for human-robot collaboration. IEEE. Trans. Autom. Sci. Eng. 2024, 21, 5983-96.

31. Kober, J.; Bagnell, J. A.; Peters, J. Reinforcement learning in robotics: a survey. Int. J. Robot. Res. 2013, 32, 1238-74.

32. Levine, S.; Finn, C.; Darrell, T.; Abbeel, P. End-to-end training of deep visuomotor policies. arXiv 2015, arXiv:1504.00702. Available online: https://doi.org/10.48550/arXiv.1504.00702. (accessed on 2026-08-21).

33. Tang, C.; Abbatematteo, B.; Hu, J.; Chandra, R.; Martín-Martín, R.; Stone, P. Deep reinforcement learning for robotics: a survey of real-world successes. Annu. Rev. Control. Robot. Auton. Syst. 2025, 8, 153-88.

34. Eskue, N.; Baptista, M. L. Deep reinforcement learning for facilitating human-robot interaction in manufacturing. In: Islam, M. M. M.; Baptista, M. L.; Tariq, F.; editors. Artificial intelligence for smart manufacturing and industry X.0. Cham: Springer Nature Switzerland; 2025. pp. 69-95.

35. Habibian, S.; Alvarez Valdivia, A.; Blumenschein, L. H.; Losey, D. P. A survey of communicating robot learning during human-robot interaction. Int. J. Robot. Res. 2025, 44, 665-98.

36. Lindemann, L.; Cleaveland, M.; Shim, G.; Pappas, G. J. Safe planning in dynamic environments using conformal prediction. IEEE. Robot. Autom. Lett. 2023, 8, 5116-23.

37. Zhao, W.; Peña Queralta, J.; Westerlund, T. Sim-to-real transfer in deep reinforcement learning for robotics: a survey. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia. Dec 01-04, 2020. IEEE; 2020. pp. 737-44.

38. Ali, M. U.; Zafar, A.; Kim, S.; Kim, K. S.; Lee, S. W. From task-specific to foundation models: a paradigm shift in medical vision-language analysis. Comput. Sci. Rev. 2026, 59, 100831.

39. Levine, S.; Kumar, A.; Tucker, G.; Fu, J. Offline reinforcement learning: tutorial, review, and perspectives on open problems. arXiv 2020, arXiv:2005.01643. Available online: https://doi.org/10.48550/arXiv.2005.01643. (accessed on 2026-08-21).

40. Figueiredo Prudencio, R.; Maximo, M. R. O. A.; Colombini, E. L. A survey on offline reinforcement learning: taxonomy, review, and open problems. IEEE. Trans. Neural. Netw. Learn. Syst. 2024, 35, 10237-57.

41. Gürtler, N.; Blaes, S.; Kolev, P.; et al. Benchmarking offline reinforcement learning on real-robot hardware. arXiv 2023, arXiv:2307.15690. Available online: https://doi.org/10.48550/arXiv.2307.15690. (accessed on 2026-08-21).

42. Open X-Embodiment Collaboration. Open X-embodiment: robotic learning datasets and RT-X models. arXiv 2023, arXiv:2310.08864. Available online: https://doi.org/10.48550/arXiv.2310.08864. (accessed on 2026-08-21).