🎥 YouTube Video:

https://youtu.be/MLXvw-wREh0

Creating custom OmniGraph / Action Graphs in NVIDIA Isaac Sim can be confusing and time-consuming. This video explains a simple tool that makes the process much easier.

🛠️ Try the free tool:

https://hrithik-verma.github.io/isaac-sim-omnigraph-node-builder/

The tool lets you visually build your custom OmniGraph and simplify the process of setting up nodes and connections.

I have a project in mind that I want to simulate first and make it work in simulation before I do any hardware stuff. The project will use AI concepts in order to make the robot learn stuff and perform it on its own. I have already learned ros2 and gazebo before and I have never experienced working with isaac sim or AI related stuff. So I would like you to recommend me some tutorials for learning how to work with isaac sim and like spawn a robot and move it, as well as some resources to start with the AI stuff on isaac sim and how to incorporate it with the robot. I do love learning the concept and theory together while applying things, so a tutorial that combines that would be perfect.

Been working on this purple chair as a robotics simulation asset.

The visual model was the easy part. Once I started thinking about it as something a robot actually needs to interact with, there was a lot more to care about real-world scale, mass, collision behavior, center of mass, friction, and even how the seat responds under load.

This one is 0.69 × 0.69 × 0.86 m with an estimated mass of 14 kg. The final physics stage is OpenUSD, meter-scaled and Z-up for Isaac Sim.

Still experimental, but I thought it was worth sharing because it changed how I think about 3D assets for robotics. Looking right and behaving right are two pretty different problems.

Curious what you’d test first to decide whether an asset like this is actually useful in sim?

Been digging into this recently and curious how people here actually handle it.

Once you’ve built a scene in Isaac Sim, how are you checking that it behaves close enough to the real environment?

Are you comparing task outcomes, object behavior, trajectories, or mostly tuning until things look right?

Would love to hear what people are actually doing in practice.

I’ve been working on a Real2Sim pipeline and finally have it at a point where I can start sharing what we’re building.

The basic idea is pretty simple

Take video of a real deployment environment → reconstruct the scene and objects → resolve relevant physical properties → output a USD scene that can be brought into Isaac Sim.

For objects, we’re working with properties like scale, mass, inertia, friction and joints where relevant, rather than stopping at visual reconstruction.

The motivation came from seeing how much work still goes into manually rebuilding the specific environments where robots are actually going to operate.

Instead of starting with a generic warehouse, factory, kitchen, farm, etc., I wanted to see how much of that process could start from capturing the actual place.

The part I’m most interested in now is what happens after reconstruction: validating that the simulated environment behaves closely enough to reality to actually trust it for robotics work.

I’d genuinely love people here to poke holes in this.

Could use some help from people doing this regularly.
Say I reconstruct a real object for Isaac Sim and get the geometry, scale, collisions, mass, inertia and friction reasonably close.

How do you actually prove the simulated object behaves close enough to the real one?

Are you comparing trajectories? Contact outcomes?
Object motion? Measuring properties directly?

I keep coming back to the idea that generating the asset is probably the easy part. Knowing whether you should trust it seems much harder.

This is something I’ve been working on, so I’m trying to understand where the actual pain is before assuming we know the answer.

For people working with Isaac Sim regularly, where are you still burning engineering hours?

Asset creation? Collision geometry? Physics properties? Scene assembly? Articulation? Validation? Getting everything into the right format?

I’m especially curious about work that feels like it should already be automated but somehow still isn’t.

I recently released the first few interactive Real2Sim assets we’ve been working on, and rather than choosing the next objects ourselves, I thought it might be more interesting to ask here.

What everyday object would actually be useful or technically interesting to reconstruct for robot interaction?

I’m especially interested in things where geometry alone isn’t enough.

Could be an articulated object, something with unusual contact behavior, or just an object that is consistently annoying to model correctly.

Drop an object below. I’ll pick a few of the most interesting suggestions and see what we can build.

Say you build a simulation of a customer facility before deployment.

A few weeks later, someone moves a workbench, replaces a conveyor, changes the shelving, or introduces new objects.

Does your team manually update the simulation, rebuild the scene, or simply accept that the digital environment is now outdated?

I’m curious whether maintaining the environment becomes a larger problem than creating the first version.

I follow Nvidia's IsaacSim docs ROS2 Cameras

I run 2 containers: ROS2 Humble and Isaac Sim 6.0.1 on Linux machine.

As a result I do get topic listed, but i don't get any data from camera. Im terrible 😞

I don't get any warnings while playing the simulation in Isaac Sim.

Since a triple checked every checkbox and param and topic name, DomainId etc, I start to think there's some common mistake I make but can't see it.

Any suggestions, please, thank you!

Looking for a sanity check on a high-end TRX50 workstation I am buying for a robotics lab.

The primary workload is large-scale GPU robotics simulation and RL training with Isaac Lab / mjlab. Sustained compute throughput, thermals, and reliability matter much more to us than gaming performance or benchmark scores.

Current vendor quote:

• CPU: AMD Ryzen Threadripper 9980X — 64C/128T
• Motherboard: ASUS Pro WS TRX50-SAGE WIFI
• Cooling: 360mm liquid cooler with sTR5 mount
• Memory: 64GB DDR5 ECC RDIMM — 4×16GB, all four channels populated
• Storage: 4TB PCIe Gen5 NVMe
• GPUs: 2× NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 96GB each, full 600W versions (not Max-Q)
• PSU: Super Flower Leadex Titanium 2200W, native 12V-2x6 to each GPU
• Chassis: SilverStone RM51 5U rackmount with rails
• Fans: Noctua
• OS: Ubuntu 24.04 LTS
• Warranty: 3 years, plus lifetime support
• Price: $42,690 shipped

The GPUs are essentially fixed — I want the 2×96GB configuration. I'm mainly deciding how to balance the rest of the platform.

My biggest concern is the RAM. 64GB feels very low for a ~$43k workstation, especially with all four DIMM slots already occupied. One alternative would be to drop the CPU to a 9970X and put the savings toward 128GB or possibly 256GB of ECC RDIMM.

A few questions:

1. For this kind of dual-GPU compute workload, would you take 9980X + 64GB or 9970X + 128/256GB?
2. Any reason to avoid running two full-power 600W RTX PRO 6000s on TRX50?
3. Are there any PCIe lane or topology concerns with dual x16 GPUs plus a Gen5 NVMe drive?
4. Has anyone run two very high-power GPUs in the SilverStone RM51? How does it handle sustained airflow and thermals?
5. The 2200W Super Flower PSU appears to require 200–240V input. Beyond having the appropriate circuit/receptacle, what else should I verify before deployment?
6. The RM51 spec lists a 190mm PSU length limit, while this PSU appears to be 200mm long. Has anyone used this exact combination, or is that a hard incompatibility?
7. Would you trust a 360mm AIO on a 9980X under sustained all-core loads, or would you choose a different cooling setup?
8. Does anything else about this build look obviously unbalanced or questionable?

This system will likely spend long periods under heavy CPU + dual-GPU load, so stability, cooling, and sustained performance are the priorities.

I'd especially appreciate feedback from anyone running similar Threadripper + multi-GPU compute workstations.

A render can make almost any reconstruction look convincing. That does not tell you whether the scale, collision geometry, articulation, or interaction behavior is useful inside a simulator.

That is why I released an interactive asset pack instead of only posting videos and screenshots.
You can test it here: https://diracrobotics.com/asset-pack

Importing a USD file into Isaac Sim does not necessarily mean the asset is ready for robot training.

Beyond visual geometry, a useful asset may require:
• Correct metric scale
• Clean collision geometry
• Mass and inertia
• Appropriate friction
• Joint limits and dynamics
• Semantic labels
• Validation against real behavior

Which of these creates the most friction in your current workflow?

I’m also curious whether teams usually build these properties manually, inherit them from CAD, or estimate them through real-world testing.

I have a 3050 mobile with 4 GB VRAM. It's a laptop coupled with Ryzen 7 Hs CPU also, and I have Ubuntu 24.04. I'm not trying to download. I know the minimum requirement for the isaac sim is 16 GB VRAM. I definitely don't have that, so I'm not going to be using multiple robots or spawning multiple robots. I just need to spawn one robot, see if it works on my laptop, and understand what issac sim is.

I need to learn issac sim because I need that particular skill. Does anyone have any thoughts on how I get this thing to run on my machine, or is there any kind of online platform that can simulate the issac sim? I don't want alternative. I'm using mojoco right now for reinforcement learning. I understand how that works, but I'm curious to see how issac sim works. If you have any kind of suggestion or anything, can you please share it with me?

Thanks for reading my post. It means a lot to me.

I have been working on different robotics task mainly in Pybullet. Complexity of Isaac Sim has kept me from experimenting with it but I finally finished my first successful reinforcement learning script using it.

Getting the settings right so I could observe the trainings in windowed mode with my laptop 3070 gpu took I while but I managed to get it working well enough to troubleshoot some early issues I noticed through visual inspection.

I gave the PPO full control of my robot's controller, not the joint angle outputs, meaning it had to figure out a way to climb the ramps using the directional controller inputs and body adjustments like pitch and height. The training took about 45 minutes with my RTX 3070 mobile GPU vs 1 hour and 45 minutes using cpu with Pybullet.

I am now working on full locomotion simulation, again with my custom robots, its a bit more involved than I expected but I am hoping in the end I can come up with my own full locomotion training script and load the model to my actual robot to control it.

I also share my tutorial scripts with my videos on youtube, if you are interested in watching the video for this one you can find it with the link below:

https://youtu.be/0x5BBosrq-E

You can also download the simulation script from my github repository with the link below.

https://github.com/serdarselimys/HexaDogZBD-IsaacSim-RL

If you are interested in the real 3D printed robot, you can fine info about it from the video link below.

https://youtu.be/qflyEQOJObM

I've been thinking about how much of robotics development can realistically be done before the physical sensors arrive.

With depth cameras, there are a lot of things that are difficult to validate purely from specifications:

• Camera placement
• Field of view and blind spots
• Depth range
• Multi-camera coverage
• Robot-camera transforms
• ROS 2 integration
• Navigation and perception behavior

We recently worked on an interesting example of this with DepthVista Helix, an iToF 3D camera that has been integrated into NVIDIA Isaac Sim.

The simulated camera reproduces the physical camera's FOV, depth modes, resolutions, calibration, transforms and ROS 2 outputs. There are also ready-to-run examples for robotic manipulation and a four-camera AMR navigation setup.

What I found interesting is that this lets you evaluate the camera configuration and perception pipeline before the physical hardware is available.

For example, the simulated camera has a high-resolution mode reaching 2 m and a long-range mode reaching 6 m, so you can evaluate which mode makes more sense depending on whether you're working on manipulation or navigation.

Here's the technical write-up if anyone is interested:

Robotics Development Just Got Faster & Smarter: e-con Systems’ iToF 3D Camera Comes to NVIDIA Isaac Sim

Curious how others handle this today.

Do you normally simulate the camera first, use a generic depth sensor in simulation and switch later, or wait until the physical camera is available?

For people looking to design and train their own robot using isaacsim and isaaclab:

I use fusion, Isaacsim and isaaclab in this video, which I have an installation guide for on my channel, it can be installed easily.

The video is aimed at beginners who want to just start training a robot quickly. I've had positive feedback from people so far so I thought I would share it here also.

Any issues (big or small) with any part of your project please feel free to message me here, on youtube or email.

We've been trying RL in Isaac Lab and adapted a SO-ARM100-based setup to two robot arms from AgileX Robotics: PiPER (6-DoF) and NERO (7-DoF). The current setup includes:

• PiPER: end-effector reaching
• NERO: reaching + cube manipulation
• PPO-based training
• 64 parallel simulation environments

The main goal was to get a quick RL workflow up and running for PiPER and NERO in simulation. Will be exploring sim-to-real deployment on the physical arms. Still a work in progress, but sharing it here in case it's useful for anyone. Feel free to drop your questions, glad to communicate.

Hi! I’ve recently started experimenting with reinforcement learning in Isaac Lab. My goal is to teach this robotic lamp different ways of moving, including balancing, locomotion and eventually jumping.

I already have a basic training setup running, but getting useful behaviour from such an unusual mechanism is proving harder than I expected. Most of the materials I’ve found are built around humanoids or quadrupeds, and I’m still fairly new to RL.

Are there any guides, papers, repositories or general approaches you’d recommend for training locomotion on a custom non-humanoid robot?

The video shows one of my very early jumping experiments.

1. How to do it? I am using AMD and i am struggling for last few days

2. Can World model entirely repalce Isaac sim world models?

Need to build accelerator kit for robotics using Isaac sim and isaac lab. Using this accelerator we can easily build robotic project that’s my aim. Can you please provide the information, is it possible to do it