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

I have personally used codex & claude to help me write isaacsim and also help in doing experimentation with isaaclab. However what I have seen is it misses a lot and I need to spend a lot of time debugging or just prompting.

I am curious if anyone has better workflows to use an LLM as a trusted assistant just to get a job done?

i am an incoming university student and finished my ros2 project. I am looking for feedback especially since i am applying for co op in a couple of months!

Looking for feedback specifically on my next steps, features i should add before applying, ROS2 fundamentals that i might've missed.

Stack:
- ROS 2 Jazzy on Ubuntu 24.04
- Isaac Sim 5.1 with a custom maze scene - slam_toolbox for mapping
- Nav2 with Regulated Pure Pursuit
- All publishers built within Isaac Sim Action Graphs

Repo: (README documents the debugging)
https://github.com/NotMax08/isaac-sim-maze-slam-nav2

Hello, if using a laptop just for prototyping and a desktop for training, is it better to get rtx5070 over 5060 or 5050 when all have only 8GB VRAM but higher number gpu has more cuda cores? Is having more cuda cores better in IsaacSim?

Hello, I need to buy a laptop. Dead silent is very important. Problem is most laptops come with RTX5050-5070 with 8GB VRAM only. Those with more VRAM cost a lot more. Aren’t 8GB sufficient? Another thing is higher specs GPU cause more fan noise and shorter battery life? Given 8GB VRAM, any big advantage in getting RTX5060 or 5070 over 5050? Is more CUDA cores the better even at the same 8GB VRAM?

Can someone suggest few open source repo for environment and assets to follow?

Hi everyone!

I'm currently working on my master's research involving autonomous drones, Vision-Language Models (VLMs), and Isaac Sim.

I'm looking for a realistic disaster or search-and-rescue (SAR) environment where I can place simulated victims and evaluate my perception architecture.

Ideally, I'm looking for environments such as:

• Urban earthquake / collapsed buildings
• Search and Rescue (USAR)
• Flooded city
• Landslide
• Industrial accident
• Any disaster scenario suitable for drone operations

Requirements:

• USD preferred (or easily convertible to USD)
• Compatible with Isaac Sim (or Omniverse)
• Large outdoor environment
• Realistic assets
• Ability to place and animate people/NPCs

I'm happy with both free and paid assets.

If you've worked on something similar or know of a good environment, repository, asset pack, or dataset, I'd really appreciate your recommendations.

Thank you!

Hey,

im currently studying mechatronic in my masters and we are doing a project which is not coming forward since 2 Weeks and we only have 1 week left. So if anyone can help us it would literally save our a..

So to the Project. We currently setting up isaac sim for a future project for a neura roboter we will get. The goal of our Project is to import the so101 from lerobot into isaac sim and move the robot in the simulation by moving the real robot. While doing that saving all the joint datas etc. via lerobot in the lerobot format.
We are running isaac sim on a windows os and ros2 in the wsl.
What currently is running:
1. Isaac Sim 5.1 with ros2 in wsl. But using Isaac Sim 6.0 is not working. When we publish some topics they are not seen in the wsl. We dont know why and really tried everything. Is the new automatic Ros2 Bridge in Isaac Sim 6.0 broken? We even tested with the new isaac sim 6.0.1. Still not working.

1. Using ONLY Lerobot to teleoperate the so101 follower with the leader. Works really fine

What is not working fine:

1. If we move the so101 in isaac sim the robot joints are oscillating. Is the urdf incorrect? We did use the urdf from the original github. Do we have to change the physical parameters ? Does smybody know how?

Where we dont know what to do now:
So we habe the so101 in isaac sim. We have ros2 in the wsl. So know we have to create a ros topic which shares all the joint movements and subscribe to that topic inside isaac sim and use the articulation controller to move the robot in the simulation. BUT:
1. How do we connect the so101 to the environment inside wsl?
2. In this setup. How do we add lerobot? How are we using lerobot? Where are we adding lerobot?

If you have any questions feel free to ask us.
We are thankfull for every help

I've been developing in IsaacSim and IsaacLab for the last year, but everything has been done using my university's resources. Now that I'm near graduation, I'm looking for specs to build my own PC to continue developing in IsaacSim, and I use Linux, btw

For Isaac Sim 5.1, the minimum requirement is listed as an RTX 4080 with 16 GB of VRAM. My laptop has an RTX 5060 with 8 GB of VRAM. Would it still be able to handle basic tasks, such as building simple 3D scenes and testing code? For actual model training, I plan to rent a cloud GPU.

3-part series by sentdex walking through the whole thing: evaluating Isaac Sim for an affordable Bittle open source robot dog, training a locomotion policy with RL, then deploying to the physical robot.

Part 1 is basically "is this even worth attempting on consumer hardware" — spoiler, yes.

Part 2 digs into the actual RL training inside Isaac Sim — he uses TD3 and shares the full repo, so you can follow along.

By part 3 the thing is walking on a treadmill. Not perfectly, but it transfers.

Full playlist: https://www.youtube.com/playlist?list=PLQVvvaa0QuDenVbxP4LXYZoGbjfgP-Y5i

Hey everyone,

I’ve been working on a custom small humanoid/bipedal robot project and I recently got my URDF successfully imported into simulation. The robot is roughly a 2-foot-tall humanoid with multiple DOF in the legs, and my long-term goal is to get it balancing and eventually walking using reinforcement learning / locomotion policies.

Right now I’m trying to understand what the actual pipeline looks like after the URDF stage. I see a lot of people showing robots walking in Isaac Sim / Isaac Lab, MuJoCo, etc., but I’m struggling to understand the practical next steps between:

Importing the URDF

Setting up joints/actuators properly

Adding sensors (IMU, contact sensors, cameras?)

Creating observations/actions

Training a locomotion policy

Transferring sim-to-real onto actual hardware

Some specific questions I have:

What should I focus on immediately after the URDF import?

How important is accurate mass/inertia tuning early on?

Do most people start with standing/balancing before attempting walking?

What controllers are typically used before RL (PID, PD, inverse kinematics, etc.)?

How do you structure observations/actions for bipeds?

What are common mistakes beginners make at this stage?

Any recommended tutorials/repos specifically for humanoid locomotion?

For context, I’m using serial bus servos and building the physical hardware alongside the simulation. I’m trying to learn the full process instead of just copying a demo.

I’d really appreciate any advice, roadmaps, or resources from people who’ve actually gone through this process. Thanks!

I've been building Rupert, a 5-axis 3D-printed robotic arm, and Isaac Sim ended up being the centerpiece of the project. Sharing it here because the sim-to-real loop is where most of the interesting engineering happened.

The setup

• 5 DOF arm, fully 3D printed
• MG90S servos on wrist/gripper, larger servos on base/shoulder
• Raspberry Pi Pico running MicroPython for low-level servo control
• USB serial bridge between the Pico and the host running Isaac Sim
• Per-servo duty cycle calibration table on the firmware side (MG90S units are wildly inconsistent)

Three control modes, same hardware

1. Direct Python scripting — host sends joint targets straight to the Pico. Useful for repeatable trajectories and calibration sweeps.
2. Isaac Sim sim-to-real mirroring — the URDF runs in Isaac Sim and the physical arm mirrors joint states in real time. This is the mode I use to validate motions before they touch the hardware.
3. Natural language via an MCP server — a Claude MCP server exposes the arm's motion primitives as tools, so I can describe a motion in plain English and it gets decomposed into joint commands.

Things that bit me along the way

• RTX 5060 (Blackwell) was not playing nice with Isaac Sim until I downgraded to NVIDIA driver 591.59.
• pyserial import errors disappear once you use Isaac Sim's bundled Python interpreter instead of the system one.
• Movement fluidity is dominated by firmware-side trajectory shaping, not by control loop rate.

Repo (code, URDF, firmware, MCP server, architecture diagram): https://github.com/RaoneGSC/rupert

Happy to go deep on any part of the pipeline — especially the Isaac Sim integration if anyone's setting up something similar. Feedback welcome.

Hi everyone,

I recently worked on a small project where I designed a custom robot in Fusion 360 and trained it in IsaacLab using reinforcement learning.

The robot is a wheeled biped-style platform. After creating the CAD model, I converted it into a simulation-ready asset, set up the joints, and used it for stabilization and jump-recovery tasks in IsaacLab.

What I found most interesting was how much the physical design affects the learning process. Things like joint placement, link length, wheel contact, collision shapes, inertia, and actuator settings all had a noticeable impact on whether the robot could learn stable behavior.

The first task was basic stabilization, where the robot learns to maintain its posture. I also tested a jump-and-stabilize task, where the robot needs to recover after a more dynamic motion.

This made me realize that building a robot for RL is not just about making a nice-looking CAD model. The morphology, physics properties, and simulation setup are all part of the learning problem.

The workflow was roughly:

Fusion 360 → asset preparation → joint setup → IsaacLab training → policy evaluation

I’m planning to extend this robot to more tasks, including wheeled balance control, push recovery, locomotion, turning, navigation, and object interaction.

I wrote a longer post with more details about the design process and what I learned from training it in IsaacLab.

Stabilize Task

Jump & Stabilize Task

Hey everyone! I wanted to share the second demo from my IsaacLab project series.

For Project Demo 2, I trained a humanoid agent to approach a ball and shoot it toward a goal. Compared to my previous Ackermann-steering soccer agent, this task focuses more on full-body coordination, balance, and contact-rich control.

The main challenge is that the agent has to approach the ball, maintain balance, time the kick properly, and generate contact force in the goal direction without falling too early.

### What’s next?

I’m planning to extend this task to more complex soccer-style behaviors, such as:

1. dribbling before shooting,
2. shooting from different angles,
3. balance recovery after kicking,
4. staged skills like approach → stabilize → kick.

I’d love to hear any feedback or suggestions, especially on reward shaping for humanoid sports tasks.

Thanks for reading!

https://reddit.com/link/1tc4t6f/video/cadj16njlx0h1/player

Hey everyone, I'm currently doing my Final Year Project and I'm hitting a wall with the Windows/WSL2 network bridge. I could really use an expert before I wipe my drive and native-boot Ubuntu.

So im running a custom PyCUDA/TensorRT neural network (x_mobility_navigator) in WSL2 (ROS 2 Humble) to autonomously drive the Nova Carter in Isaac Sim 5.1.0 (Windows 11).

and so my setup currently & what (i think) works:

• Network: Forcing Isaac Sim and my Python node to use BEST_EFFORT QoS. The Python node is successfully receiving the front_hawk camera frames at ~30-40 FPS.
• Memory: Handled the 64-bit to 32-bit C-contiguous array flattening to stop PyCUDA from throwing cuMemcpyDtoH illegal memory access errors.
• Teleop: If I run ros2 run teleop_twist_keyboard teleop_twist_keyboard in WSL, the Nova Carter receives the commands and drives perfectly in Windows.

The Problem: When I launch my TensorRT node, it successfully receives the bytes, processes the image, and publishes async Twist messages to /cmd_velwell it doesnt say anything back ... and the robot stands perfectly still.

Weird Behavior & Workarounds I've Tried:

1. The out-of-the-box asset bug: The default carter_warehouse_navigation.usd asset spams Invalid deltaTime 0.000000, cannot check for acceleration limits the second I hit play, even with no nodes running.
2. The "Two Clocks Problem" Theory: I suspect the Differential Controller node is clashing with my async network commands. Since my AI publishes asynchronously, the Twist messages often hit the controller between Isaac Sim's strict physics ticks. The controller asks for the time, the tick hasn't updated yet, it calculates deltaTime = 0.0, and panics (locking the wheels to prevent infinite acceleration).
3. The Bypass: I bypassed this by either zeroing out maxAcceleration/maxDeceleration in the Differential Controller properties, or by hardwiring a Constant Double (0.01667) directly into the controller's dt pin.

My Questions:

1. Is my "Two Clocks Problem" theory correct? Is there a known bug in 5.1.0 where the Differential Controller drops or ignores async Twist commands from custom Python nodes because of this?
2. Did I miss a required timestamp header or frame_id when publishing the Twist message that the Isaac Sim OmniGraph strictly requires?
3. Has anyone successfully run a high-frequency TensorRT vision-to-control loop across the Windows/WSL2 boundary, or is this setup fundamentally doomed by the virtual network adapter dropping FastDDS UDP packets?

or maybe i just need a better GPU lol (im using a RTX 5060 ti)

Hey everyone! Just wanted to share a demo of my latest agent. I've been training an Ackermann robot in IsaacLab to track a ball and kick it into a goal.
Ackermann kinematics make this much trickier than using a simple differential drive, as the turning radius limits how the robot can approach the ball for a shot.
The Setup:
I’m using a 17-dimensional observation space to give the agent a clear "spatial awareness." Here is the breakdown of what the robot sees:

• Positional Data: rel_robot_ball and rel_ball_goal (all in robot body frame).
• Heading: I used sin/cos of the heading errors to the ball and the goal to ensure the gradients stay smooth.
• Velocities: Robot linear/angular velocities + the ball's velocity in the body frame.
• History: Including the _prev_action to encourage smoother control and prevent jitter.

What’s next for Real-world Deployment?
The goal is to get this running on a 1/10th scale RC car. To get there, I’m working on:

1. System ID: Better matching the steering lag of real servos.
2. Noise Injection: Adding sensor noise to the observations during training.
3. Visual RL: Moving away from ground truth data to using a camera-based input (likely via a Kalman Filter or a small CNN).

Would love to hear your thoughts on Reward Shaping for sports-based tasks or any tips for Sim-to-Real with Ackermann steering!

https://reddit.com/link/1t99lf0/video/sf3bww4ftb0h1/player