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arXiv · 2604.21017

Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics

Open-H-Embodiment Consortium·:·Nigel Nelson·Juo-Tung Chen·Jesse Haworth·Xinhao Chen·Lukas Zbinden·Dianye Huang·Alaa Eldin Abdelaal·Alberto Arezzo·Ayberk Acar·Farshid Alambeigi·Carlo Alberto Ammirati·Yunke Ao·Pablo David Aranda Rodriguez·Soofiyan Atar·Mattia Ballo·Noah Barnes·Federica Barontini·Filip Binkiewicz·Peter Black·Sebastian Bodenstedt·Leonardo Borgioli·Nikola Budjak·Benjamin Calmé·Fabio Carrillo·Nicola Cavalcanti·Changwei Chen·Haoxin Chen·Sihang Chen·Qihan Chen·Zhongyu Chen·Ziyang Chen·Shing Shin Cheng·Meiqing Cheng·Min Cheng·Zih-Yun Sarah Chiu·Xiangyu Chu·Camilo Correa-Gallego·Giulio Dagnino·Anton Deguet·Jacob Delgado·Jonathan C. DeLong·Kaizhong Deng·Alexander Dimitrakakis·Qingpeng Ding·Hao Ding·Giovanni Distefano·Daniel Donoho·Anqing Duan·Marco Esposito·Shane Farritor·Jad Fayad·Zahi Fayad·Mario Ferradosa·Filippo Filicori·Chelsea Finn·Philipp Fürnstahl·Jiawei Ge·Stamatia Giannarou·Xavier Giralt Ludevid·Frederic Giraud·Aditya Amit Godbole·Ken Goldberg·Antony Goldenberg·Diego Granero Marana·Xiaoqing Guo·Tamás Haidegger·Evan Hailey·Pascal Hansen·Ziyi Hao·Kush Hari·Kengo Hayashi·Jonathon Hawkins·Shelby Haworth·Ortrun Hellig·S. Duke Herrell·Zhouyang Hong·Andrew Howe·Junlei Hu·Zhaoyang Jacopo Hu·Ria Jain·Mohammad Rafiee Javazm·Howard Ji·Rui Ji·Jianmin Ji·Zhongliang Jiang·Dominic Jones·Jeffrey Jopling·Britton Jordan·Ran Ju·Michael Kam·Luoyao Kang·Fausto Kang·Siddhartha Kapuria·Peter Kazanzides·Sonika Kiehler·Ethan Kilmer·Ji Woong Kim·Przemysław Korzeniowski
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Abstract

Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision. However, autonomous medical robotics has been limited by a fundamental data problem: existing medical robotic datasets are small, single-embodiment, and rarely shared openly, restricting the development of foundation models that the field needs to advance. We introduce Open-H-Embodiment, the largest open dataset of medical robotic video with synchronized kinematics to date, spanning more than 50 institutions and multiple robotic platforms including the CMR Versius, Intuitive Surgical's da Vinci, da Vinci Research Kit (dVRK), Rob Surgical BiTrack, Virtual Incision's MIRA, Moon Surgical Maestro, and a variety of custom systems, spanning surgical manipulation, robotic ultrasound, and endoscopy procedures. We demonstrate the research enabled by this dataset through two foundation models. GR00T-H is the first open foundation vision-language-action model for medical robotics, which is the only evaluated model to achieve full end-to-end task completion on a structured suturing benchmark (25% of trials vs. 0% for all others) and achieves 64% average success across a 29-step ex vivo suturing sequence. We also train Cosmos-H-Surgical-Simulator, the first action-conditioned world model to enable multi-embodiment surgical simulation from a single checkpoint, spanning nine robotic platforms and supporting in silico policy evaluation and synthetic data generation for the medical domain. These results suggest that open, large-scale medical robot data collection can serve as critical infrastructure for the research community, enabling advances in robot learning, world modeling, and beyond.

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Open-H-Embodiment Consortium, :, Nigel Nelson, Juo-Tung Chen, Jesse Haworth, Xinhao Chen, Lukas Zbinden, Dianye Huang, Alaa Eldin Abdelaal, Alberto Arezzo, Ayberk Acar, Farshid Alambeigi, Carlo Alberto Ammirati, Yunke Ao, Pablo David Aranda Rodriguez, Soofiyan Atar, Mattia Ballo, Noah Barnes, Federica Barontini, Filip Binkiewicz, Peter Black, Sebastian Bodenstedt, Leonardo Borgioli, Nikola Budjak, Benjamin Calmé, Fabio Carrillo, Nicola Cavalcanti, Changwei Chen, Haoxin Chen, Sihang Chen, Qihan Chen, Zhongyu Chen, Ziyang Chen, Shing Shin Cheng, Meiqing Cheng, Min Cheng, Zih-Yun Sarah Chiu, Xiangyu Chu, Camilo Correa-Gallego, Giulio Dagnino, Anton Deguet, Jacob Delgado, Jonathan C. DeLong, Kaizhong Deng, Alexander Dimitrakakis, Qingpeng Ding, Hao Ding, Giovanni Distefano, Daniel Donoho, Anqing Duan, Marco Esposito, Shane Farritor, Jad Fayad, Zahi Fayad, Mario Ferradosa, Filippo Filicori, Chelsea Finn, Philipp Fürnstahl, Jiawei Ge, Stamatia Giannarou, Xavier Giralt Ludevid, Frederic Giraud, Aditya Amit Godbole, Ken Goldberg, Antony Goldenberg, Diego Granero Marana, Xiaoqing Guo, Tamás Haidegger, Evan Hailey, Pascal Hansen, Ziyi Hao, Kush Hari, Kengo Hayashi, Jonathon Hawkins, Shelby Haworth, Ortrun Hellig, S. Duke Herrell, Zhouyang Hong, Andrew Howe, Junlei Hu, Zhaoyang Jacopo Hu, Ria Jain, Mohammad Rafiee Javazm, Howard Ji, Rui Ji, Jianmin Ji, Zhongliang Jiang, Dominic Jones, Jeffrey Jopling, Britton Jordan, Ran Ju, Michael Kam, Luoyao Kang, Fausto Kang, Siddhartha Kapuria, Peter Kazanzides, Sonika Kiehler, Ethan Kilmer, Ji Woong Kim, Przemysław Korzeniowski. 2026-04-22. Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics. https://arxiv.org/abs/2604.21017

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