NVIDIA has released its Medical Physics Simulation framework as an open source tool, giving healthcare robotics developers a GPU-accelerated way to train surgical robots in virtual environments before they ever interact with patients.
The framework, announced today as part of NVIDIA Isaac for Healthcare, aims to solve a major bottleneck in medical robotics: obtaining enough varied data to train, test, and improve robot behavior. Anatomy varies between patients. Instruments bend, press, slip, and interact with tissue in complex ways. Imaging can be noisy or incomplete. And the rare edge cases developers most need to understand do not appear on schedule.
Medical Physics Simulation brings together anatomy modeling, medical device behavior, sensor simulation, and robot learning into a single reusable environment. Instead of rebuilding custom scenes for every workflow, teams can create simulation environments that work across multiple projects. This saves development time and helps bring innovations to market faster.
Because the framework is open source, developers can inspect the code, adapt it to their own devices and workflows, and build on a GPU-accelerated foundation that integrates with the broader NVIDIA stack. Open source is especially important in healthcare, NVIDIA says, because teams need transparency into the data, models, and weights that shape system behavior. Access to open models and model weights helps developers reproduce results, evaluate performance across different anatomies and scenarios, identify limitations, and build evidence for regulatory review.
A Virtual Training Ground for Medical Robots
For 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 interactions and environments to evaluate how robots perform across those changes. The framework is powered by NVIDIA CUDA and built on NVIDIA Warp, Newton, and Cosmos simulation and generative AI technologies. It 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 show that 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, 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.
An Ecosystem Building the Future of Medical Robotics
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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 is 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 is accelerating the evolution of medical technology with synthetic data, validating device mechanics, and producing in silico evidence to support regulatory pathways with NVIDIA Medical Physics Simulation.
Medtronic Structural Heart is exploring applying Medical Physics Simulation with simulated X-ray sensing to generate data for catheter navigation research.
A New Layer in the Isaac for Healthcare Stack
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, review available reference workflows, and start building simulation environments for their own devices, anatomies, and healthcare robotics applications.