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In vivo feasibility study of humanoid robots in surgery

  • Wensing, P. M. et al. Proprioceptive actuator design in the MIT cheetah: impact mitigation and high-bandwidth physical interaction for dynamic legged robots. IEEE Trans. Rob. 33, 509–522 (2017).

    Article 

    Google Scholar
     

  • Grimminger, F. et al. An open torque-controlled modular robot architecture for legged locomotion research. IEEE Robot. Autom. Lett. 5, 3650–3657 (2020).

    Article 

    Google Scholar
     

  • Chignoli, M., Kim, D., Stanger-Jones, E. & Kim, S. The MIT humanoid robot: design, motion planning, and control for acrobatic behaviors. In Proc. 2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids) 1–8 (IEEE, 2021).

  • Semasinghe, C., Taylor, D. & Rezazadeh, S. The design and manufacturing of Mithra: a humanoid robot with anthropomorphic attributes and high-performance actuators. Robotics 14, 28 (2025).

    Article 

    Google Scholar
     

  • Khazoom, C., Hong, S., Chignoli, M., Stanger-Jones, E. & Kim, S. Tailoring solution accuracy for fast whole-body model predictive control of legged robots. IEEE Robot. Autom. Lett. 9, 11074–11081 (2024).

    Article 

    Google Scholar
     

  • Zhang, J. Z. et al. Whole-body model-predictive control of legged robots with MuJoCo. Preprint at arxiv.org/abs/2503.04613 (2025).

  • Belvedere, T., Scianca, N., Lanari, L. & Oriolo, G. Joint-level IS-MPC: a whole-body MPC with centroidal feasibility for humanoid locomotion. In Proc. 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 11240–11247 (IEEE, 2024).

  • Ishihara, K., Gomi, H. & Morimoto, J. Hierarchical learning framework for whole-body model predictive control of a real humanoid robot. Preprint at arxiv.org/abs/2409.08488 (2024).

  • Porto, V. G. B. d. A., Melo, D. C., Maximo, M. R. O. A. & Afonso, R. J. M. Imitation learning of a model predictive controller for real-time humanoid robot walking. Eng. Appl. Artif. Intell. 143, 109919 (2025).

    Article 

    Google Scholar
     

  • Radosavovic, I. et al. Real-world humanoid locomotion with reinforcement learning. Sci. Robot. 9, eadi9579 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Radosavovic, I., Kamat, S., Darrell, T. & Malik, J. Learning humanoid locomotion over challenging terrain. Preprint at arxiv.org/abs/2410.03654 (2024).

  • He, T. et al. VIRAL: visual sim-to-real at scale for humanoid loco-manipulation. Preprint at arxiv.org/abs/2511.15200 (2025).

  • Kim, M. J. et al. Openvla: an open-source vision-language-action model. Preprint at arxiv.org/abs/2406.09246 (2024).

  • Belkhale, S. et al. RT-H: action hierarchies using language. Preprint at arxiv.org/abs/2403.01823 (2024).

  • Wen, J. et al. TinyVLA: toward fast, data-efficient vision-language-action models for robotic manipulation. IEEE Robot. Autom. Lett. 10, 3988–3995 (2025).

    Article 

    Google Scholar
     

  • Zheng, R. et al. Tracevla: visual trace prompting enhances spatial-temporal awareness for generalist robotic policies. Preprint at arxiv.org/abs/2412.10345 (2024).

  • O’Neill, A. et al. Open X-Embodiment: robotic learning datasets and RT-X models: Open X-Embodiment Collaboration0. In Proc. 2024 IEEE International Conference on Robotics and Automation (ICRA) 6892–6903 (IEEE, 2024).

  • Figure exceeds $1 billion in Series C funding at $39 billion post-money valuation. Figure AI www.figure.ai/news/series-c (2025).

  • Health workforce. World Health Organization www.who.int/health-topics/health-workforce#tab=tab_1 (2023).

  • Cypher, R. L. Burnout and patient safety: an occupational phenomenon. J. Perinat. Neonatal Nurs. 38, 128–130 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Ageing and health. World Health Organization www.who.int/news-room/fact-sheets/detail/ageing-and-health (2025).

  • The Robot Report Staff. Diligent robotics hires 2 former Cruise execs to scale Moxi. The Robot Report http://www.therobotreport.com/diligent-robotics-hires-2-former-cruise-execs-to-scale-moxi/ (10 July 2025).

  • Lanfranco, A. R., Castellanos, A. E., Desai, J. P. & Meyers, W. C. Robotic surgery: a current perspective. Ann. Surg. 239, 14–21 (2004).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Taylor, R. H., Menciassi, A., Fichtinger, G., Fiorini, P. & Dario, P. in Springer Handbook of Robotics (eds Siciliano, B. & Khatib, O.) 1199–1222 (Springer, 2016).

  • Bruce, G. Elon Musk says Tesla robot will perform ‘superhuman’ surgeries. Becker’s Health IT www.beckershospitalreview.com/healthcare-information-technology/innovation/elon-musk-says-tesla-robot-will-perform-superhuman-surgeries/ (19 November 2025).

  • Marinho, M. M., Bernardes, M. C. & Bó, A. P. L. A programmable remote center-of-motion controller for minimally invasive surgery using the dual quaternion framework. In Proc. 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics 339–344 (IEEE, 2014).

  • Marinho, M. M., Adorno, B. V., Harada, K. & Mitsuishi, M. Dynamic active constraints for surgical robots using vector-field inequalities. IEEE Trans. Rob. 35, 1166–1185 (2019).

    Article 

    Google Scholar
     

  • Nasiri, E. & Wang, L. Admittance control for adaptive remote center of motion in robotic laparoscopic surgery. In Proc. 2024 21st International Conference on Ubiquitous Robots (UR) 51–57 (IEEE, 2024).

  • Davila, A., Colan, J. & Hasegawa, Y. Real-time inverse kinematics for robotic manipulation under remote center-of-motion constraint using memetic evolution. J. Comput. Des. Eng. 11, 248–264 (2024).


    Google Scholar
     

  • Hagn, U. et al. DLR MiroSurge: a versatile system for research in endoscopic telesurgery. Int. J. Comput. Assist. Radiol. Surg. 5, 183–193 (2010).

    Article 
    PubMed 

    Google Scholar
     

  • Hagn, U. et al. The DLR MIRO: a versatile lightweight robot for surgical applications. Ind. Rob. 35, 324–336 (2008).

    Article 

    Google Scholar
     

  • Cofran, L. et al. Barriers to safety and efficiency in robotic surgery docking. Surg. Endosc. 36, 206–215 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Kanji, F. et al. Room size influences flow in robotic-assisted surgery. Int. J. Environ. Res. Public Health 18, 7984 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ozturkcan, S. & Merdin-Uygur, E. Humanoid service robots: the future of healthcare? J. Info. Technol. Teaching Cases 12, 163–170 (2022).

    Article 

    Google Scholar
     

  • Mlakar, I. et al. Facilitating acceptance, trust, and ethical integration of socially assistive robots among nurses: a quasi-experimental study. Health Policy Technol. 14, 101034 (2025).

    Article 

    Google Scholar
     

  • Haddadin, S. & Croft, E. Physical human–robot interaction. in Springer Handbook of Robotics (eds Siciliano, B. & Khatib, O.) 1835–1874 (Springer, 2016).

  • Yang, G.-Z. et al. The grand challenges of science robotics. Sci. Robot. 3, eaar7650 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Atar, S. et al. Humanoids in hospitals: a technical study of humanoid robot surrogates for dexterous medical interventions. Preprint at arxiv.org/abs/2503.12725 (2025).

  • Battaglia, E. et al. Rethinking autonomous surgery: focusing on enhancement over autonomy. Eur. Urol. Focus 7, 696–705 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Unitree G1: humanoid Agent AI Avatar. Unitree Robotics https://www.unitree.com/g1/ (2026).

  • Wilson, J. T., Tsao, T.-C., Hubschman, J.-P. & Schwartz, S. Evaluating remote centers of motion for minimally invasive surgical robots by computer vision. In Proc. 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics 1413–1418 (IEEE, 2010).

  • de Graaf, P. Optimal positioning of robotic arms inside the abdominal cavity in pediatric surgery. Surgical Robotics Laboratory https://surgicalroboticslab.nl/wp-content/uploads/2020/08/degraaf20-masters.pdf (2020).

  • Afshar, M. et al. Optimal design of a novel spherical scissor linkage remote center of motion mechanism for medical robotics. In Proc. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 6459–6465 (IEEE, 2020).

  • Freschi, C. et al. Technical review of the da Vinci surgical telemanipulator. Int. J. Med. Robotics Comput. Assist. Surg. 9, 396–406 (2013).

    Article 
    CAS 

    Google Scholar
     

  • Da Vinci Xi Surgical System in-Service Guide: OR Staff (Da Vinci OS4 v.9) (Intuitive Surgical, 2021).

  • Kazanzides, P. et al. An open-source research kit for the da Vinci surgical system. In Proc. 2014 IEEE International Conference on Robotics and Automation (ICRA) 6434–6439 (IEEE, 2014).

  • Richter, F., Orosco, R. K. & Yip, M. C. Motion scaling solutions for improved performance in high delay surgical teleoperation. In Proc. 2019 IEEE International Conference on Robotics and Automation (ICRA) 1590–1595 (IEEE, 2019).

  • Lu, S. et al. Adaptive control of time delay teleoperation system with uncertain dynamics. Front. Neurorobot. 16, 928863 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xiong, Z. et al. ExtremControl: low-latency humanoid teleoperation with direct extremity control. Preprint at arxiv.org/abs/2602.11321 (2026).

  • Ichihara, J. & Miura, S. Quantifying the effects of delays on telerobotic surgical operability via brain activity measurements. Int. J. Comput. Assist. Radiol. Surg. 20, 2371–2379 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ferguson, J. M. et al. Comparing the accuracy of the da Vinci Xi and da Vinci Si for image guidance and automation. Int. J. Med. Robot. 16, 1–10 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fryar, C. D., Carroll, M. D., Gu, Q., Afful, J. & Ogden, C. L. Anthropometric reference data for children and adults: United States, 2015–2018. Vital Health Stat. 3, 1–44 (2021).


    Google Scholar
     

  • Nabeel, A. et al. Assessing and evaluating the impact of operative vision compromise (OViC) on surgeons’ practice: a qualitative study. Int. J. Surg. 110, 6972–6981 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • von Strauss und Torney, M. et al. Microcomplications in laparoscopic cholecystectomy: impact on duration of surgery and costs. Surg. Endosc. 30, 2512–2522 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Rojas Burbano, J. C. et al. Robot-assisted surgery: current applications and future trends in general surgery. Cureus 17, e82318 (2024).


    Google Scholar
     

  • Kim, V. B. et al. Early experience with telemanipulative robot-assisted laparoscopic cholecystectomy using da vinci. Surg. Laparosc. Endosc. Percutan. Tech. 12, 33–40 (2002).

    Article 
    PubMed 

    Google Scholar
     

  • Rojas, A., Gachabayov, M., Abouezzi, Z. E., Bergamaschi, R. & Latifi, R. Current robotic platforms in surgery and the road ahead. Surg. Technol. Int. 38, 39–46 (2021).

    PubMed 

    Google Scholar
     

  • Surgical tools. LivsMed https://livsmed.us/products/ (accessed 26 May 2026).

  • Macenski, S., Foote, T., Gerkey, B., Lalancette, C. & Woodall, W. Robot operating system 2: design, architecture, and uses in the wild. Sci. Robot. 7, eabm6074 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Society of American Gastrointestinal and Endoscopic Surgeons (SAGES). FLS Manual Skills Written Instructions and Performance Guidelines. https://cpd.partners.org/sites/default/files/manual_skills_guidelines[1].pdf (2012).

  • Liang, Z. & Yip, M. User performance data sheets. Zenodo https://doi.org/10.5281/zenodo.20434260 (2026).

  • Liang, Z. Laparoscopic humanoid code. Zenodo https://doi.org/10.5281/zenodo.18023650 (2025).

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