Start with one small, measurable robot task, build and test its software loop in simulation, then move to hardware only when you know what the robot must sense and do. Simulation can help you develop and test a prototype, but success in a virtual scene does not prove it will work safely or reliably on a physical robot.
What should your first physical AI prototype do?
Choose a single task with a clear pass condition—not a general goal such as “make a robot intelligent.” For example, the robot might detect a specified object and place it in a marked area, or follow a simple route while avoiding an obstacle. Keep the initial scene and behavior narrow enough that you can tell which part is failing.
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Write down the task loop
- Observation: What information does the robot need, such as an image, depth measurement, or position estimate?
- Decision: What must the software determine from those observations—for example, where an object is or which way to turn?
- Action: What movement or other command should the robot make?
- Success condition: What observable result means the task is complete?
- Failure cases: What should happen if the object is missed, an obstacle appears, or the robot cannot complete the action?
NVIDIA’s Physical AI learning module uses an obstacle-aware pick-and-place workflow as an example. It describes simulation as a way to develop, train, and test robotics applications in varied environments without needing physical robots: NVIDIA Physical AI learning module.
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How do you build the first version in simulation?
1. Model only what the task depends on
Choose a simulator and a robot model that can represent the important parts of your task. For a pick-and-place test, that may mean the arm’s movement, the target object, a camera, and the relevant surfaces. For route navigation, it may mean the mobile base, its sensors, and obstacles. Begin with a simple scene; add detail when it helps test a specific assumption.
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NVIDIA describes Isaac Sim as an open-source reference framework for robotics simulation, testing, and synthetic-data generation. Its listed workflow includes importing robot descriptions and CAD sources, assembling scenes, configuring physics and sensors, connecting robot software, and evaluating systems in software- or hardware-in-the-loop setups: NVIDIA Isaac Sim overview.
2. Connect the robot software through ROS 2
In the documented Isaac Sim workflow, the ROS 2 bridge connects the simulator with ROS 2 software. That gives you a way to exercise the same general perception-and-control workflow against a simulated robot before testing on hardware. Isaac Sim’s ROS 2 documentation recommends ROS 2 Humble and Jazzy; it describes support for other locally installed ROS 2 distributions on Ubuntu 22.04 or 24.04 as experimental: Isaac Sim ROS 2 documentation.
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3. Test a complete loop, not just a scene
Run the task from sensor input through software decisions to robot action. Check that the robot receives observations, responds to commands, and reports useful status or errors. Try variations that could affect the task, such as changing an object’s position or adding an obstacle. A scene that looks realistic is not enough if the software is not actually consuming the simulated sensor data and controlling the simulated robot.
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Which software versions and hardware should you use?
Check compatibility for the exact components you plan to install. Isaac Sim and Isaac ROS have separate documentation and version lines; a ROS 2 recommendation on one page should not be treated as a complete compatibility matrix for the other product.
| Component or guidance | What the cited NVIDIA page says | How to apply it |
|---|---|---|
| Isaac Sim ROS 2 bridge | The documentation recommends ROS 2 Humble and Jazzy. Other locally installed ROS 2 distributions on Ubuntu 22.04 or 24.04 are described as experimental. Source | Use the ROS 2 guidance for the Isaac Sim version you select, and treat the experimental qualification as a real compatibility risk. |
| Isaac ROS packages | The getting-started page says the packages are designed and tested for ROS 2 Lyrical. Source | Do not assume this is the same ROS 2 recommendation as the Isaac Sim page; verify that the exact components in your stack work together. |
| Isaac ROS x86_64 support-table entry | The page lists an Ampere-or-higher NVIDIA GPU architecture, at least 8 GB RAM, Ubuntu 24.04, CUDA 13.2 or later, NVIDIA driver 595 or later, and 32 GB or more available disk space. Source | This is the support-table entry reported on the page at the research timestamp, 2026-10-07—not a general requirement for every simulator or robotics project. |
| Isaac ROS Jetson support-table entry | The page lists Jetson Thor and Orin with JetPack 7.2 and an NVMe SSD of 128 GB or more. Source | Check the current page and the exact board configuration before buying; the entry is a changeable support snapshot, not a universal hardware recommendation. |
| NVIDIA Physical AI course setup | One course setup page specifies Ubuntu 22.04, ROS 2 Humble, and Isaac Sim 5.0 or 5.1. Source | Use these prerequisites for that course setup, not as a substitute for checking current product requirements. |
For a first prototype, you do not necessarily need to buy a robot immediately. NVIDIA’s Isaac ROS platform page lists Jetson Orin among its supported platforms, but it does not establish Jetson Orin as a requirement for every prototype. Choose an onboard computer, robot, and sensors only after the task and software interfaces give you a reason to select them.
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When should you move from simulation to a physical robot?
Move to hardware when the simulated task and its interfaces are clear enough to make physical testing informative. You should be able to say what sensor data the software needs, what commands it sends, and what behavior counts as success. Then select a robot and sensors suited to that task and confirm that the intended computer and software versions are compatible.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsUse simulation to find and fix software and task-design problems before involving the real machine. Then test on hardware to learn what the simulation could not establish about the physical setup. Differences in sensors, timing, control response, and the real environment may affect results; a successful simulated run does not guarantee sim-to-real performance.
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Make physical tests staged and observable
- Begin with a controlled setup and a clearly bounded task.
- Check that sensor observations and robot actions behave as expected on the real system.
- Change one important condition at a time so failures are easier to diagnose.
- Define what the robot should do when it cannot complete the task, and provide a way to stop or reset testing.
- Use software-in-the-loop and hardware-in-the-loop as evaluation stages where they fit your system; they are testing approaches, not safety or performance certifications.
How to choose a simulator without assuming one is best
Isaac Sim is one documented option for a simulation-first workflow, particularly when its robot models, ROS 2 integration, sensors, and synthetic-data capabilities fit the project. The available sources do not establish a fair head-to-head ranking against Gazebo or other simulators, so choose based on the demands of your task rather than an unsupported claim that one tool is best.
- Task fit: Can you represent the robot, scene, sensors, and physics that matter to your test?
- ROS 2 integration: Does the simulator connect to the ROS 2 distribution and packages your software needs?
- Compute needs: Can your available system meet the selected simulator and robotics stack’s requirements?
- Hardware path: Can you test the same task with the physical robot and sensors you intend to use?
- Data needs: Do you need synthetic data generation or large-scale robot-learning workflows, or is a simpler simulation sufficient?
A practical first-project plan
- Define one measurable task. Write the observation, decision, action, pass condition, and likely failure cases.
- Build the smallest useful simulated scene. Add the robot, task-relevant sensors, and only the environment details needed to test your assumptions.
- Connect and check the software loop. Use the simulator’s integration route, such as Isaac Sim’s ROS 2 bridge, and verify that data and commands flow end to end.
- Exercise variations and record assumptions. Test changes relevant to the task and note sensor, timing, control, or environment conditions that may differ on hardware.
- Check the exact compatibility matrix. Confirm the selected simulator, ROS 2 distribution, operating system, drivers, CUDA, packages, and compute platform before installation or purchase.
- Move to a controlled physical test. Choose hardware for the task, validate its real sensor and control behavior, and use staged testing to expose differences the simulation could not settle.
For a guided starting point, NVIDIA’s Physical AI learning resources include a simulation-based pick-and-place example and describe development and testing without a physical robot: learning module.
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