# Agentic Workflows in Isaac for Healthcare[#](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-for-healthcare/latest/agentic-workflows/index.html.md#agentic-workflows-in-isaac-for-healthcare 'Link to this heading')

From prompt to policy

## Prompt Your Way to a Healthcare Robotics Simulation

Isaac for Healthcare supplies the simulation, data, and policy building blocks for robotic autonomy. In this course, you use agentic workflows to create a healthcare task, collect demonstrations, train a policy, and validate it in Isaac Sim.

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This hands-on course teaches you to build, simulate, and extend **healthcare-robotics workflows** with NVIDIA Isaac for Healthcare, the unified **IsaacLab-Arena** simulator, **GR00T**, and **openpi** policies. You’ll work entirely in simulation, following the same record → prepare → train → deploy loop the platform’s agentic skills automate.

The course is organized as **self-paced sections**. Work through them, then extend the platform with your own environments and skills.

## Compute Requirements[#](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-for-healthcare/latest/agentic-workflows/index.html.md#compute-requirements 'Link to this heading')

This is a GPU course. Before you begin, use a local or remote machine with:

- Ubuntu 22.04 or 24.04 on `x86_64` or `aarch64`.

- An NVIDIA GPU with Linux driver `580.65.06` or newer for Isaac Sim 5.1.0 and CUDA 13.

- About 30 GB of free disk space.

- `git` and `uv` available on `PATH`.

- Network access to GitHub, PyPI, NVIDIA PyPI, and PyTorch CUDA wheels.

Cosmos visual expansion is optional. It also needs Docker with GPU support and an `HF_TOKEN` or `HUGGING_FACE_HUB_TOKEN` after you accept the NVIDIA Cosmos model license.

## Stay in the CLI With the Tutor Skill[#](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-for-healthcare/latest/agentic-workflows/index.html.md#stay-in-the-cli-with-the-tutor-skill 'Link to this heading')

If you prefer to stay in your terminal the whole time, load the [Physical AI Tutor skill](https://developer.nvidia.com/downloads/Omniverse/learning/Courses/isaac-gr00t-e2e/Physical-AI-Tutor-skill.md) into your coding agent before you begin. It turns this course into an interactive CLI tutor: it adapts to your experience level, explains one step at a time, pauses before hands-on actions, and helps diagnose errors from the same shell session.

```
Tutor me through Agentic Workflows in Isaac for Healthcare from the beginning.
Use the course instructions at https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-for-healthcare/latest/agentic-workflows.
Keep me in the CLI, adapt to my level, and pause before each hands-on step.
```

[The Isaac for Healthcare agentic end-to-end pipeline](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-for-healthcare/latest/_images/i4h-workflow-e2e-system-diagram.svg)

The end-to-end agentic pipeline runs in four phases: prepare → build dataset → inspect & convert → train & verify.[#](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-for-healthcare/latest/agentic-workflows/index.html.md#id1 'Link to this image')

## What You’ll Do[#](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-for-healthcare/latest/agentic-workflows/index.html.md#what-you-ll-do 'Link to this heading')

Get Set Up

Install the workflow, list the supported environments, and run a zero-action smoke test in [Setup and Environments](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-for-healthcare/latest/agentic-workflows/02-setup-and-environments.html.md).

Build a Dataset

[Collect](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-for-healthcare/latest/agentic-workflows/03-data-collection.html.md) teleop demos and [prepare](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-for-healthcare/latest/agentic-workflows/04-data-preparation.html.md) them into a LeRobot dataset.

Train and Deploy

[Fine-tune a GR00T policy](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-for-healthcare/latest/agentic-workflows/05-imitation-learning-gr00t.html.md) and [deploy it in simulation](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-for-healthcare/latest/agentic-workflows/06-deployment-in-simulation.html).

Extend the Platform

[Build your own workflow](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-for-healthcare/latest/agentic-workflows/07-build-your-own-workflow.html.md) and [add your own skills](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-for-healthcare/latest/agentic-workflows/09-adding-your-own-skills.html.md).