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Artificial kinaesthesia in autonomous robotic surgery

Nature Reviews Bioengineering (2026)Cite this article

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Surgeons depend on a finely tuned multisensory system, in which vision and kinaesthesia work in synergy to manipulate tissue with precision. Translating this to robotic systems requires a hierarchical framework of artificial kinaesthesia, progressing from physical sensing to algorithmic understanding, and finally, to synergistic control.

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Fig. 1: Synergistic fusion of vision and kinaesthesia in tissue manipulation.

References

  1. Dupont, P. E. & Degirmenci, A. The grand challenges of learning medical robot autonomy. Sci. Robot. 10, eadz8279 (2025).

    Article  Google Scholar 

  2. Schmidgall, S., Opfermann, J. D., Kim, J. W. & Krieger, A. Will your next surgeon be a robot? Autonomy and AI in robotic surgery. Sci. Robot. 10, eadt0187 (2025).

    Article  Google Scholar 

  3. Ernst, M. O. & Banks, M. S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433 (2002).

    Article  Google Scholar 

  4. Abiri, A. et al. Visual–perceptual mismatch in robotic surgery. Surg. Endosc. 31, 3271–3278 (2017).

    Article  Google Scholar 

  5. Min, Z., Lai, J. & Ren, H. Innovating robot-assisted surgery through large vision models. Nat. Rev. Electr. Eng. 2, 350–363 (2025).

    Article  Google Scholar 

  6. Hao, Y., Zhang, H., Zhang, Z., Hu, C. & Shi, C. Development of force sensing techniques for robot-assisted laparoscopic surgery: a review. IEEE Trans. Med. Robot. Bionics 6, 868–887 (2024).

    Article  Google Scholar 

  7. Gao, A. et al. Laser-profiled continuum robot with integrated tension sensing for simultaneous shape and tip force estimation. Soft Robot. 7, 421–443 (2020).

    Article  Google Scholar 

  8. Boutry, C. M. et al. A hierarchically patterned, bioinspired e-skin able to detect the direction of applied pressure for robotics. Sci. Robot. 3, eaau6914 (2018).

    Article  Google Scholar 

  9. Ciuti, G., Webster, R. J. III, Kwok, K.-W. & Menciassi, A. Robotic surgery. Nat. Rev. Bioeng. 3, 565–578 (2025).

    Article  Google Scholar 

  10. Salehi, Y. & Giannacopoulos, D. PhysGNN: a physics-driven graph neural network based model for predicting soft tissue deformation in image-guided neurosurgery. In Proc. 36th International Conference on Neural Information Processing Systems (eds Koyejo, S. et al.) 37282–37296 (NeurIPS, 2022).

  11. Long, Y. et al. Surgical embodied intelligence for generalized task autonomy in laparoscopic robot-assisted surgery. Sci. Robot. 10, eadt3093 (2025).

    Article  Google Scholar 

  12. Han, T. et al. Bayesian calibration of a computational model of tissue expansion based on a porcine animal model. Acta Biomater. 137, 136–146 (2022).

    Article  Google Scholar 

  13. Zhang, E. Q., Shi, E. R. & Barceló-Coblijn, L. Categorical perception and language evolution: a comparative and neurological perspective. Front. Psychol. 14, 1110730 (2023).

    Article  Google Scholar 

  14. Liu, T., Wang, X., Katupitiya, J., Wang, J. & Wu, L. Automatic tissue traction using miniature force-sensing forceps for minimally invasive surgery. IEEE Trans. Robot. 40, 4690–4704 (2024).

    Article  Google Scholar 

  15. Chi, C. et al. Diffusion policy: visuomotor policy learning via action diffusion. Int. J. Robot. Res. 44, 1684–1704 (2024).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by NSFC Young Scientists Fund — Category A T252500134, Hong Kong Research Grants Council (RGC) Collaborative Research Fund (CRF C4026-21GF), General Research Fund (GRF 14216022, 14204524, 14203323, 14206125), Research Impact Fund (RIF R4020-22) and by the Chinese University of Hong Kong (CUHK) IdeaBooster fund (IDBF25ENG02).

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Authors and Affiliations

  1. Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China

    Tangyou Liu  (刘唐有), Sishen Yuan  (袁思申) & Hongliang Ren  (任洪亮)

Authors
  1. Tangyou Liu  (刘唐有)
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  2. Sishen Yuan  (袁思申)
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  3. Hongliang Ren  (任洪亮)
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Contributions

Conceptualization: T.L. and H.R. Researching data: T.L. Discussion: T.L., S.Y. and H.R. Visualization: T.L. Writing original draft: T.L. Writing review and editing: T.L., S.Y. and H.R. Funding acquisition and administration: H.R. Supervision: H.R.

Corresponding author

Correspondence to Hongliang Ren  (任洪亮).

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The authors declare no competing interests.

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Liu, T., Yuan, S. & Ren, H. Artificial kinaesthesia in autonomous robotic surgery. Nat Rev Bioeng (2026). https://doi.org/10.1038/s44222-026-00403-z

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