NVIDIA 开源首个 GPU 加速医疗物理仿真框架

在医疗机器人能够在现实世界中发挥作用之前，它必须学习物理世界的反作用。解剖结构存在差异。器械会弯曲、按压、滑动并与组织互动。成像可能存在噪声或不完整。而开发者最需要理解的罕见极端场景不会按计划出现。

这就造成了医疗机器人领域最大的瓶颈之一：获取开发者训练、测试和改进机器人行为所需的巨大而多样化的数据。

NVIDIA 医学物理仿真框架——NVIDIA Isaac for Healthcare 中的一个新的开源 GPU 加速能力——今日宣布，帮助医疗机器人开发者建模解剖与器械的交互，生成难以捕捉的场景，进行虚拟测试，并在硬件密集型测试前训练或评估机器人策略。

该框架将解剖学与医疗器械行为与传感器仿真及机器人学习结合在一起，使团队能够创建可重复使用的仿真环境，而无需为每个工作流程重建自定义场景，从而节省开发者时间并更快将创新推向市场。

由于医学物理仿真是开源的，医疗机器人开发者可以检查框架，适配到自己的设备和工作流程，并在 GPU 加速的基础上构建，与更广泛的 NVIDIA 技术栈无缝协作。

开源在医疗领域尤为重要，因为团队需要透明地了解塑造系统行为的数据、模型和权重。访问开放模型和模型权重可以帮助开发者复现结果，在不同解剖结构和场景下评估性能，识别局限性，并为监管审查建立证据。

对于物理 AI：https://www.nvidia.com/en-us/glossary/generative-physical-ai/，经验就是动态数据。开发者需要训练机器人在解剖变化、器械行为不同、条件变化或策略意外失效时也能正常操作。

医学物理仿真帮助开发者模拟解剖结构、设备接触、摩擦和传感器输入，然后在交互和环境中进行测试，以评估机器人在这些变化下的表现。该框架由 NVIDIA CUDA 提供支持，并且是 Isaac for Healthcare 的一部分——构建于 NVIDIA Warp、Newton 和 Cosmos 仿真及生成式人工智能技术之上——可以运行数百个并行仿真环境，帮助团队探索更多场景并在开发早期识别失败模式。

对于机器人开发者来说，这将仿真从定制工程项目转变为可重复使用的基础设施。现在的区别在于规模：基准测试（https://arxiv.org/abs/2503.18616）显示，8,192 个机器人训练环境并行运行，使用 GPU 原生仿真将训练时间从五个多小时缩短至不到两分钟。

利用该框架，开发者可以连接血管解剖结构、柔性器械（如导管和导丝）、模拟 X 光成像和强化学习。该框架被设计为不仅限于这些示例，还可扩展到其他设备、解剖结构、传感器和医疗机器人领域。

医学物理仿真将经典物理仿真与生成式 AI 物理仿真结合在一起。经典仿真有助于建模已知的物理规则，例如设备接触、摩擦和运动。NVIDIA Cosmos-H Dreams（https://github.com/isaac-for-healthcare/Cosmos-H-Dreams），这是医学物理仿真中的实时生成式 AI 物理仿真能力，帮助从程序化数据中学习并建模视觉场景动态。

这些方法结合在一起，为开发者在转向物理原型和实验室测试之前，在虚拟环境中构建和测试医疗机器人系统提供了更丰富的方法。

医疗机器人领域的领导者已经在利用仿真驱动开发来解决具体的手术挑战。

CMR Surgical 和 Capgemini 旗下的 Cambridge Consultants 正在使用 Cosmos-H-Dreams 来隐式学习软组织外科手术的交互物理，并生成患者特定的模拟。CMR 向 Open-H Embodiment 开放数据集贡献了其 Versius 外科机器人系统的近 500 小时匿名临床数据，惠及包括胆囊切除术、前列腺切除术、疝修补术和子宫切除术在内的手术操作。

CMR Surgical 首席技术官 Chris Fryer 表示：“开源模型使我们能够建立在共享知识的基础上，加速负责任的创新，并最终有可能为全球患者提供更一致的护理和更好的结果。”

强生医疗技术正在利用 Isaac for Healthcare 的医学物理模拟和基于 Cosmos 的基础模型来构建其泌尿外科 ENDOLUMINAL MONARCH 平台的数字孪生，模拟复杂的解剖结构和肾结石场景。XCath 正在使用医学物理模拟进行血管内自主策略训练。Inner Logic 通过合成数据加速医疗技术的发展，验证设备力学并生成体外证据以支持监管路径，利用 NVIDIA 医学物理模拟。

美敦力结构心脏部门正在探索将医学物理模拟与模拟 X 光感应技术结合应用，以生成导管导航研究的数据。

作为 NVIDIA Isaac for Healthcare 的模块化功能，医学物理模拟既可以单独使用，也可以与数字孪生管道、医疗传感器模拟、NVIDIA Isaac Lab 开放机器人学习框架以及 NVIDIA 开放模型和策略一起使用。

开发者可以浏览开源医学物理模拟框架：https://isaac-for-healthcare.github.io/medical-physics-simulation/，查看可用的参考工作流程，并开始为自己的设备、解剖结构和医疗机器人应用构建模拟环境。

Before a healthcare robot can be useful in the real world, it has to learn how the physical world pushes back. Anatomy varies. Instruments bend, press, slip and interact with tissue. Imaging can be noisy or incomplete. And the rare, edge scenarios developers most need to understand don’t appear on schedule.

That creates one of the biggest bottlenecks in healthcare robotics: obtaining the enormous amount of varied data developers need to train, test and improve robot behavior.

NVIDIA Medical Physics Simulation framework — a new open source, GPU-accelerated capability within NVIDIA Isaac for Healthcare — announced today, helps medical robotics developers model anatomy-device interaction, generate hard-to-capture scenarios, test in silico, and train or evaluate robot policies before hardware-heavy testing.

The framework brings together anatomy and medical device behavior with sensor simulation and robot learning so teams can create reusable simulation environments instead of rebuilding custom scenes for every workflow, saving developers time and bringing innovations to market faster.

Because Medical Physics Simulation is open source, healthcare robotics developers can inspect the framework, adapt it to their own devices and workflows, and build on a GPU-accelerated foundation that works seamlessly with the broader NVIDIA stack.

Open source is especially important in healthcare because teams need transparency into the data, models and weights that shape system behavior. Access to open models and model weights can help developers reproduce results, evaluate performance across different anatomies and scenarios, identify limitations and build evidence for regulatory review.

For physical AI ：https://www.nvidia.com/en-us/glossary/generative-physical-ai/ , experience is data in motion. Developers need to train robots to operate properly even when anatomy changes, devices behave differently, conditions shift or a policy fails unexpectedly.

Medical Physics Simulation helps developers simulate anatomy, device contact, friction and sensor inputs, then test in interactions and environments to evaluate how robots perform across those changes. Powered by NVIDIA CUDA and part of Isaac for Healthcare — built on the NVIDIA Warp, Newton and Cosmos simulation and generative AI technologies — the framework can run hundreds of parallel simulation environments, helping teams explore more scenarios and identify failure modes earlier in development.

For robot builders, this turns simulation from a bespoke engineering project into reusable infrastructure. The difference now is scale: benchmarks ：https://arxiv.org/abs/2503.18616 show 8,192 robot-training environments running in parallel with GPU-native simulation cut training from over five hours to under two minutes.

With this framework, developers can connect vascular anatomy, flexible instruments such as catheters and guidewires, simulated X-ray imaging and reinforcement learning. The framework is designed to extend beyond that example to additional devices, anatomies, sensors and healthcare robotics domains.

Medical Physics Simulation brings together classical physics simulation and generative AI physics simulation. Classical simulation helps model known physical rules, such as device contact, friction and motion. NVIDIA Cosmos-H Dreams：https://github.com/isaac-for-healthcare/Cosmos-H-Dreams, the real-time generative AI physics simulation capability within Medical Physics Simulation, helps model visual scene dynamics learned from procedural data.

Together, these approaches give developers a richer way to build and test healthcare robotics systems in virtual environments before moving to physical prototypes and lab testing.

Medical robotics leaders are already applying simulation-driven development to solve specific surgical challenges .

CMR Surgical and Cambridge Consultants, part of Capgemini, are using Cosmos-H-Dreams to implicitly learn interaction physics for soft-tissue surgical procedures and generate patient-specific simulations. CMR contributed nearly 500 hours of anonymized clinical data from its Versius Surgical Robotic System to the Open-H Embodiment open dataset, benefiting procedures including cholecystectomy, prostatectomy, hernia repair and hysterectomy.

“Open source models allow us to build on shared knowledge, accelerating responsible innovation and, ultimately, gives us the potential to deliver more consistent care and better outcomes for patients worldwide,” said Chris Fryer, chief technology officer at CMR Surgical.

Johnson & Johnson MedTech i s using Isaac for Healthcare’s Medical Physics Simulation and a Cosmos-based foundation model to build digital twins of its endoluminal MONARCH platform for urology, modeling complex anatomy and kidney-stone scenarios. XCath is using the Medical Physics Simulation for endovascular autonomy policy training. Inner Logic i s accelerating the evolution of medical technology with synthetic data, validating device mechanics and producing in silico evidence to support regulatory pathways with NVIDIA Medical Physical Simulation.

Medtronic Structural Heart is exploring applying Medical Physics Simulation with simulated X-ray sensing to generate data for catheter navigation research.

As a modular capability within NVIDIA Isaac for Healthcare, Medical Physics Simulation can be used on its own or alongside digital twin pipelines, medical sensor simulation, the NVIDIA Isaac Lab open robot-learning framework and NVIDIA open models and policies.

Developers can explore the open source Medical Physics Simulation framework ：https://isaac-for-healthcare.github.io/medical-physics-simulation/ , review available reference workflows and start building simulation environments for their own devices, anatomies and healthcare robotics applications.