Isaac Lab

Isaac Lab adds a training loop to a matched Isaac Sim runtime. Prove a shipped task end to end before creating a robot, environment, or reward.

Establish the runtime

• Inherit the current hardware and operating-system gate from Isaac Sim.
• Verify the supported Isaac Sim and Isaac Lab pairing; newest plus newest is not automatically compatible.
• Prefer NVIDIA's matched Isaac Lab image on a cloud GPU. Use a source install when the workstation and version pairing are intentionally maintained.
• List registered tasks from the installed release instead of guessing a task ID or script path from an older tutorial.

Prove the policy loop

• Choose the learning path explicitly: reinforcement learning from rewards, or imitation learning from demonstrations and generated variants.
• Run a known task headless with few environments and few iterations.
• Verify environment reset, observation/action shapes, reward terms, logging, and checkpoint creation before scaling parallel environments.
• Locate outputs using the training library's experiment name and current configuration, not an assumed task-name directory.
• Evaluate a named checkpoint through the matching play script. Export only after its observed behavior and metrics are useful.
• Add or change one reward, termination, terrain, or robot dimension at a time; a larger batch of edits hides which contract broke.

Go deeper only when needed

• For NVIDIA's prebuilt image on RunPod, read references/prebuilt-image-runpod.md after the cloud provider is chosen.
• For the measured Unitree Go2 RSL-RL workflow, rewards, checkpoints, and custom task route, read references/go2-rl-workflow.md.
• For teleoperation, Mimic/robomimic imitation learning, export, sim-to-sim, or hardware deployment, read IMITATION-AND-DEPLOYMENT.md.
• For runtime, output, task-registry, or interactive-viewer symptoms, start with FAILURES.md.
• Use the current Isaac Lab documentation and source for task IDs, script paths, configuration, and export behavior.
• Isaac Sim owns the underlying scene and sensors. LeRobot owns LeRobot-format dataset and real-robot training workflows; data owns the simulation-versus-real sourcing decision.

Done

• A small shipped task trains, writes a discoverable checkpoint, plays back through the matching runtime, and provides a measured baseline for any custom task or scaled run.