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Train in Simulation, Deploy on a Real Robot: Microduck with Isaac Lab 3.0
TL;DR
Microduck, an affordable open-source bipedal robot, can now be trained to walk using NVIDIA's Isaac Lab and Newton. Users can export walking policies as ONNX and deploy them on the real robot, facilitating advanced robotics tasks.
✦ Why It Matters
Start training your own walking policies for Microduck today using Isaac Lab and Newton.
Key Takeaways
- Train Microduck's walking policy using NVIDIA Isaac Lab.
- Export trained policies in ONNX format for real-world deployment.
- Utilize domain randomization to enhance policy robustness.
- Share and validate your models in Microduck Arena.
How It Works
The training process leverages NVIDIA Isaac Lab's GPU-parallel simulations to create realistic environments for Microduck. By employing domain randomization, developers can vary parameters like mass and friction during training, ensuring the robot learns to adapt to real-world conditions.
This approach helps in modeling mechanical delays and backlash, which are critical for effective policy transfer from simulation to hardware.