Foundation Models in Healthcare Robotics#

Foundation models are large-scale pretrained models, typically trained on diverse and massive datasets, that provide general-purpose representations which can be efficiently adapted or fine-tuned for a wide range of downstream tasks.

Today, multimodal foundation models have emerged as the core intelligence powering advanced systems, seamlessly integrating diverse data types—such as text, images, audio, and sensor streams—to address a broad spectrum of scientific and industrial challenges across virtually every field of application.

In this course, we will fine-tune NVIDIA Isaac GR00T, a foundation model designed for a wide range of humanoid robots, to perform the specific task of a liver ultrasound scan. To accomplish this task, we will first create digital human phantoms with realistic modeling of the liver, using NVIDIA MAISI, a foundational model for 3D CT data generation. Then, we will construct a physics-accurate simulated scene in Isaac Sim and perform synthetic data collection. Finally, we will leverage Isaac for Healthcare’s workflow to fine-tune and deploy the GR00T model.

GR00T and Cosmos#

Foundation models such as GR00T and Cosmos can revolutionize healthcare robotics applications. These models learn from vast amounts of multimodal data (text, images, videos, sensor data) and can be fine-tuned for specific medical applications. They can be taught to perform repetitve tasks autonomously. Some key points to remember about Vision Language Action models (VLA) like GR00T N1:

  • Language (Natural language understanding): Processes verbal or written instructions so the system can follow human directions.

  • Vision (Visual reasoning): Interprets camera images to provide context-aware perception.

  • Action (Adaptive behavior): Understands robot joint states and predicts the next actions of the robot to meet the instructed goal.

In this course we finetune GR00T N1, to teach a robot to perform an ultrasound scan of a liver model. Cosmos allows for advanced synthetic data generation. If you want to learn more you can check out our guide for Cosmos-transfer1 Integration.

Synthetic Data Generation (SDG)#

Synthetic Data Generation (SDG) plays a crucial role in healthcare robotics by creating diverse, realistic training data that would be impossible or unethical to collect from real patients. A few benefits specific to healthcare are:

  • Patient privacy protection by avoiding the need for real patient data

  • Rare case simulation for training on uncommon medical conditions

  • Controlled variability to ensure AI models can handle diverse patient populations

  • Cost-effective training without expensive clinical trials or data collection efforts

In this course, we will create a digital twin of our robotic utlrasound scanning setup. This twin allows for synthetic data collection. We vary our setup through scene randomization, enabling data collection of diverse robot trajectories. This is sufficient for model training and building out the infrastructure to deploy our first prototype.

Phantom Models#

Phantom models are synthetic representations of human anatomy which can be leveraged for training and testing medical robots in simulation and the real world. Phantoms are commonly used for the below purposes:

  • Mimic real tissue properties including elasticity, density, and response to contact

  • Provide consistent training environments for reproducible robot learning

  • Enable safe experimentation without risk to human subjects

  • Support validation of robotic systems before clinical deployment In this course, we will generate a digital patient with MAISI CT. Because we want to deploy the learnt policy on a real world phantom, we also use a digital twin of this phantom during our data collection phase.