Step-by-step guides that take you from a standing start to a working result. Each recipe is self-contained with prerequisites, runnable commands, and verification steps.
Note
Recipes assume deployed infrastructure. Complete the Quickstart first if you have not provisioned Azure resources.
| Goal | Recipe | Time |
|---|---|---|
| Train an RL policy | Your First RL Training Job | 30 min |
| Train a LeRobot policy | Your First LeRobot Training Job | 30 min |
| Run the full train → eval → register pipeline | End-to-End LeRobot Pipeline | 60 min |
| Configure edge recording | Configuring Edge Data Recording | 20 min |
| Prepare a dataset for training | Preparing Datasets for Training | 30 min |
| Recipe | Description | Prerequisites |
|---|---|---|
| Your First RL Training Job | Submit an Isaac Lab RL training job on OSMO with SKRL | Deployed infrastructure, OSMO running |
| Your First LeRobot Training Job | Submit a LeRobot behavioral cloning job on OSMO | Deployed infrastructure, HuggingFace dataset |
| End-to-End LeRobot Pipeline | Orchestrate train → evaluate → register in one command | Completed basic LeRobot recipe |
| Recipe | Description | Prerequisites |
|---|---|---|
| Configuring Edge Data Recording | Set up ROS 2 edge recording on Jetson with chunking and compression | Jetson device, ROS 2 |
| Preparing Datasets for Training | Download, inspect, and validate datasets for LeRobot training | Python 3.11+, Azure CLI |
- Getting Started — infrastructure deployment and first training job
- Training Guide — reference documentation for RL and IL workflows
- Data Pipeline — edge recording configuration reference
- Scripts Reference — CLI parameter tables for all submission scripts
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