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.

翻译：自主医疗机器人有望改善患者预后、减轻医护工作负担、实现医疗资源普及化并达到超人级精度。然而，自主医疗机器人技术长期受困于基础数据问题：现有医疗机器人数据集规模小、形态单一且极少公开共享，严重制约了该领域亟需的基础模型发展。为此，我们提出Open-H-Embodiment——目前规模最大的开源医疗机器人视频与同步运动学数据集，覆盖全球50余家机构及多种机器人平台（包括CMR Versius、直觉外科达芬奇系统、达芬奇研究套件dVRK、Rob Surgical BiTrack、虚拟切口MIRA、Moon Surgical Maestro及多种定制系统），涉及手术操作、机器人超声及内窥镜等诊疗流程。通过两个基础模型，我们验证了本数据集的研究价值：GR00T-H作为首个面向医疗机器人的开源视觉-语言-动作基础模型，在结构化缝合基准测试中实现全端到端任务完成（成功率25%，其余模型均为0%），并在29步离体缝合序列中达到64%平均成功率；我们同时训练了Cosmos-H-手术模拟器——首个基于单检查点实现跨九种机器人平台的多形态手术仿真模型，支持医学领域的虚拟策略评估与合成数据生成。实验表明，开放大规模医疗机器人数据采集可为研究社区构建关键基础设施，推动机器人学习、世界建模等领域的突破性发展。