Training documentation for reinforcement learning with Isaac Lab and behavioral cloning with LeRobot. Both frameworks run on Azure ML and NVIDIA OSMO platforms.
| Guide | Description |
|---|---|
| Isaac Lab Training | RL training with SKRL and RSL-RL backends on Azure ML and OSMO |
| LeRobot Training | Behavioral cloning with ACT and Diffusion policies |
| Experiment Tracking | MLflow and WANDB setup, model registration, checkpoint flows |
| MLflow Integration | SKRL metric logging internals, metric filtering, and troubleshooting |
| Aspect | Azure ML | OSMO |
|---|---|---|
| Submission | az ml job create |
osmo workflow submit |
| Orchestration | Azure ML compute targets | OSMO workflow engine + KAI Scheduler |
| Experiment tracking | MLflow (managed) | MLflow + WANDB (credential injection) |
| Dataset injection | Azure ML datastores | OSMO buckets (base64 or dataset upload) |
| Model registration | az ml model create |
Via MLflow or post-training script |
| Monitoring | Azure ML Studio | OSMO UI Dashboard |
Isaac Lab RL training on Azure ML:
./scripts/submit-azureml-training.sh --task Isaac-Velocity-Rough-Anymal-C-v0
LeRobot behavioral cloning on OSMO:
./scripts/submit-osmo-lerobot-training.sh -d lerobot/aloha_sim_insertion_human
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