Isaac Lab Train Humanoid, Quadruped & Arm Robots in RL

Train Unitree G1, Go2 and a Franka arm in NVIDIA Isaac Lab: RL curves, reward ablation, one 6 GB laptop GPU.
What you'll learn

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Install Isaac Lab correctly, including the traps: it is not on PyPI, the 3.0 backend split breaks imports, and the GUI is a seventh package needing DRI3.
Train a Unitree G1 humanoid to walk from scratch on a 6 GB laptop GPU: 512 environments, 3000 iterations, reaching 979.5 of a 1000 episode-length ceiling.
Read a reinforcement learning curve properly and avoid the 200-iteration trap, where rising episode length with falling reward looks exactly like a broken rewar
Ablate reward terms with Hydra and verify it took effect, then prove which terms are load-bearing by rolling out the policy instead of trusting the log.
Requirements

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Python, a Linux machine and an NVIDIA GPU with 6 GB or more. Isaac Sim and Isaac Lab are free. No reinforcement learning or robotics background required.
Description

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This course contains the use of artificial intelligence.
Isaac Lab is NVIDIA's reinforcement learning framework for robots, but most tutorials stop at cartpole.
This course trains actual robot models.
You will work with aUnitree G1 humanoid,Unitree Go2 quadruped, andFranka arm, taking each from simulation setup to a trained reinforcement learning policy.
Everything runs on asingle NVIDIA GPU with 6 GB VRAM. No cluster, no cloud training bill, and no pretrained checkpoint replacing the training process.
What You Will Learn

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Install and configure Isaac Lab correctly
Work with its robot task registry and environments
Train aUnitree G1 humanoid for locomotion
Train aUnitree Go2 quadruped
Train aFranka arm for manipulation
Understand observations, rewards, actions, and terminations
Run large numbers of parallel environments
Modify experiments usingHydra
Read reward curves and episode length correctly
Perform reward ablations and evaluate whether a policy actually works
Learn From Real Training Runs
The G1 training run uses512 environments for 3000 iterations and reaches979.5 out of a 1000-step episode ceiling.
You will also see why training metrics can be misleading. In one run, episode length improved while reward became significantly worse before recovering later.
We then remove reward terms one at a time and compare both numerical results and actual rollouts. Some policies look healthy in the metrics but track commands poorly or fall much more often.
The goal is not just to train a policy.
It is to understand whether the robot actually learned the behavior you intended.
What You Need
Required: Python, Linux, and an NVIDIA GPU with6 GB VRAM or more.
Not required: physical robots, cloud compute, or a GPU cluster.
Who this course is for

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Robotics and ML engineers who want to train real robots in NVIDIA Isaac Lab on hardware they already own, and read a training run instead of guessing at it.
Homepage

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