# Isaac Lab > NVIDIA Isaac Lab: reinforcement-learning and imitation-learning workflows on top of Isaac Sim — prebuilt environments and tasks, training runs, and exporting policies. Use when: 'isaac lab', 'GPU RL for robots', 'train in isaac', sim-to-real policy training in the NVIDIA stack. Load after isaac-sim basics are settled (same GPU requirements apply — RTX-class NVIDIA GPU, no macOS). Alternative ML path to lerobot; the architect skill decides between them. Not for: Isaac Sim setup itself (isaac-sim) or imitation learning on real-robot datasets (lerobot). - Skill: `robium-ai/isaac-lab-5` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add robium-ai/isaac-lab-5` - Raw SKILL.md: https://api.skillmd.com/api/skills/robium-ai/isaac-lab-5/raw - Safety review: WARNING - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: robium-ai (https://skillmd.com/u/robium-ai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/robium-ai/isaac-lab-5 --- # isaac-lab The GPU-parallel RL/IL training layer of robium's NVIDIA stack, built on top of an already-running Isaac Sim: prebuilt environments and tasks (`isaaclab_tasks`), training entry points for several RL libraries, an imitation-learning path for generating and training on simulated demonstrations, and exporting a trained policy. Isaac Lab (`isaac-sim/IsaacLab`, current release **v3.0.0-beta2.patch1**, published 2026-07-02 — verified via direct fetch of the GitHub releases API this session) is NVIDIA's own framework layered on Isaac Sim, not a separate product to install independently. Its main branch's own installation docs state support for Isaac Sim 4.5/5.0/5.1 and recommend the latest 5.1.0 release specifically (verified via direct fetch of the installation docs on 2026-07-10) — that may trail the newest Isaac Sim release the `isaac-sim` skill cites, so confirm the current supported-version pairing before installing rather than assuming the two always track together. ## When to use this skill - Running a prebuilt Isaac Lab task, training a policy with an RL library on top of a working Isaac Sim install, generating/training on simulated demonstrations, or exporting a trained policy for deployment. - The trigger phrases in the description: 'isaac lab', 'GPU RL for robots', 'train in isaac', sim-to-real policy training in the NVIDIA stack. - Cross-references — go to the sibling skill instead when the question is: - **Isaac Sim itself is not installed/working yet** (GPU floor, container, USD scene, robot/sensor import, ROS 2 bridge) → `isaac-sim`. This skill assumes Isaac Sim is already running; it only adds the training layer on top. - **Imitation learning on datasets recorded from a real robot** (the LeRobotDataset format, `lerobot-train`/`lerobot-record`) → `lerobot`. This skill's own imitation-learning path (see Usage patterns) starts from demonstrations recorded *inside Isaac Sim*, not real hardware — that distinction is the actual boundary, not "imitation learning" as a category. - **Whether to use the NVIDIA stack (Isaac Sim/Lab) at all vs. LeRobot's own sim/eval tooling** → `architect` decides this, gated on the GPU floor (see Platform gotchas). - **Which simulator to use in general**, before Isaac Sim is chosen → `simulation`. - **Deciding data-sourcing strategy** (how much sim-generated vs. real data a project needs) → the `data` umbrella skill. This skill only covers the mechanics of Isaac Lab's own demonstration-generation and training tools, not the sourcing decision. ## Key directives - **Delegation posture: embed + links.** The install-on-top-of-Isaac-Sim sequence, task-ID convention, and the RL/IL/export commands below are embedded because no single upstream page walks a new robium project through all three together — but every command is sourced from `isaac-sim.github.io/IsaacLab`'s own docs or the `isaac-sim/IsaacLab` GitHub repo, fetched directly on 2026-07-10, rather than retyped from an older Isaac Lab release's memory. See References. - **On a cloud GPU, prefer the prebuilt Isaac Lab image over a source install.** `nvcr.io/nvidia/isaac-lab` (latest tag observed 3.0.0-beta2-post1 on the NGC catalog 2026-07-26..28) bundles a matched Isaac Sim + Isaac Lab in one container, which sidesteps the version- pairing trap (below) and the multi-step source install. This is the battle-tested path from the go2-locomotion app on RunPod; the pip+source route in Quick start remains valid for a workstation you own. See `references/prebuilt-image-runpod.md` for the provisioning specifics (NGC auth, EULA env vars, the `/workspace` volume-shadow gotcha, and the entrypoint override), and the `environments` skill's GPU-and-remote reference for the general pod networking/SSH mechanics this builds on. - **The GPU/driver floor is `isaac-sim`'s, not restated here.** Isaac Lab runs inside Isaac Sim, so it inherits that skill's GPU requirement verbatim — check the exact minimum/recommended GPU and VRAM numbers there, don't re-derive or re-type them in this skill. Isaac Lab's own RL training workloads (many parallel environments) also want more VRAM headroom than a bare Isaac Sim scene; treat `isaac-sim`'s stated floor as a minimum, not a comfortable working point for large `--num_envs` runs. - **Start from a prebuilt task before writing a custom environment.** List and run an existing task first (see Quick start) to confirm the install works end to end with zero environment-authoring risk, the same "validate the pipeline before customizing" posture `lerobot` takes with a pretrained policy. - **Never write task IDs, script paths, or CLI flags from memory.** Isaac Lab's task registry and script layout change across releases (the top-level scripts directory was itself reorganized into `reinforcement_learning` and `imitation_learning` subdirectories) — list the currently-registered tasks instead of assuming a task name from a prior release still exists, and re-verify script paths against `isaac-sim/IsaacLab`'s `main` branch before repeating one in a real project. ## Quick start Source: `isaac-sim.github.io/IsaacLab`'s installation and quickstart docs, and the `isaac-sim/IsaacLab` GitHub repo's scripts directory tree, fetched directly on 2026-07-10. **1. Confirm the GPU floor** — see the `isaac-sim` skill; do not proceed until the target machine meets it. **2a. Cloud GPU (preferred): pull the prebuilt Isaac Lab image.** `nvcr.io/nvidia/isaac-lab` bundles a matched Isaac Sim + Isaac Lab, so there is no separate Isaac Sim install and no version-pairing to reconcile. See `references/prebuilt-image-runpod.md` for the RunPod provisioning specifics (NGC auth, EULA env vars, the `/workspace` volume-shadow gotcha, the entrypoint override, and the observed image tag/driver/Python versions). **2b. Workstation you own (alternative): install Isaac Sim via pip, then Isaac Lab from source on top of it** (per-release version pins matter — verify the current recommended Isaac Sim version against the installation docs before pinning it): ```bash pip install "isaacsim[all,extscache]==5.1.0" --extra-index-url https://pypi.nvidia.com git clone https://github.com/isaac-sim/IsaacLab.git --branch main cd IsaacLab ./isaaclab.sh --install ``` **3. List the registered tasks:** ```bash python scripts/environments/list_envs.py ``` **4. Train on a prebuilt task** with one of the shipped RL libraries (`rsl_rl`, `skrl`, `rl_games`, `sb3`): ```bash python scripts/reinforcement_learning/skrl/train.py --task=Isaac-Ant-v0 --headless ``` **5. Watch progress and evaluate/export** — see Usage patterns. ## Usage patterns **Run a prebuilt task.** Task IDs follow `Isaac--v0` (manager-based workflow) or `Isaac--Direct-v0` (direct workflow) — `list_envs.py` (Quick start) prints the full current table with entry points, rather than guessing a name from a tutorial. `--num_envs=` sets how many parallel environments run (the GPU-parallel core of Isaac Lab's speed advantage); drop `--headless` only for local interactive debugging on a machine with a display, since it costs render throughput. **Train + monitor.** Each RL library ships its own `train.py` under its own subdirectory of the reinforcement-learning scripts tree, with a matching `play.py` for evaluation and checkpoint loading: ```bash python scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --headless --num_envs=4096 ``` Runs log to a timestamped directory under `logs///`; RSL-RL's own agent config exposes a `logger` field (`tensorboard` by default, or `wandb`/`neptune` — confirmed via direct fetch of `isaaclab_rl`'s RL-library config on 2026-07-10) — point `tensorboard --logdir logs/rsl_rl` at the run directory to watch reward/loss curves live. `--max_iterations` overrides the task's default training length for a short smoke run before committing to a full one, the same small-scale-first posture `lerobot` uses for fine-tunes. **Verified Go2 locomotion walkthrough.** A battle-tested RSL-RL run on the Unitree Go2 (task `Isaac-Velocity-Flat-Unitree-Go2-v0`, PPO) — smoke, full-training profile, reward/cost config, custom-robot scaffolding, and checkpoint portability — lives in `references/go2-rl-workflow.md`. One trap worth stating up front: RSL-RL writes checkpoints under the **experiment name**, `logs/rsl_rl/unitree_go2_flat//`, **not** the task ID, so a smoke test asserting on a task-ID-shaped path fails even when training succeeded. Reward and cost also live in config, not code (a cost is a reward term with a negative weight) — the reference names the exact files and the 3-layer weight-override chain. **Evaluate and export a trained policy.** `play.py` (same per-library directory as `train.py`) loads a checkpoint and runs it in the environment; for RSL-RL specifically, it also exports the policy to both TorchScript (JIT) and ONNX under the checkpoint's `exported/` directory automatically — confirmed by direct fetch of the RSL-RL `play.py` source on 2026-07-10, which calls `export_policy_to_jit`/`export_policy_to_onnx` (or the older `export_policy_as_jit`/`export_policy_as_onnx` helpers on RSL-RL < 4.0). This exported artifact is the sim-to-real hand-off point — deploying it onto real hardware is outside this skill's depth once exported. **Imitation learning from simulated demonstrations.** A separate `imitation_learning/` script tree (`isaaclab_mimic`, `robomimic`, and a `record_demos.py`/`replay_demos.py` pair under the tools scripts directory) records teleoperated or scripted demonstrations *inside Isaac Sim* and trains a policy on them — this is the sim-side imitation-learning path, distinct from `lerobot`'s real-robot-dataset training (see When to use this skill). Treat this as a pointer, not a full walkthrough — verify the current CLI against the `imitation_learning/` and `tools/` directories before running it. **Hand-off from LeRobot.** `lerobot-eval --env.type=isaaclab_arena` loads Isaac Lab Arena through LeRobot's EnvHub mechanism (`lerobot.envs.make_env`) rather than this skill's own scripts — that's `lerobot`'s territory calling into an Isaac Lab environment, not the reverse; see the `lerobot` skill's eval-and-sim reference for that specific invocation. ## Platform gotchas - **GPU floor is `isaac-sim`'s — don't re-derive it.** No macOS, RTX-class NVIDIA GPU required; see that skill for the exact minimum/recommended numbers and how they were verified. - **Isaac Sim/Isaac Lab version pairing is narrower than "whatever's newest."** Isaac Lab's `main` branch supports a specific Isaac Sim version window (4.5/5.0/5.1 as of 2026-07-10, recommending 5.1.0) rather than every Isaac Sim release — installing the two independently without checking this pairing is a common source of import-time failures. Re-check the installation docs' compatibility statement before pinning versions in a real project. - **Headless is the default for real training runs, same as `isaac-sim`.** `--headless` avoids paying render cost for a GUI viewport during a training run with thousands of parallel environments; reserve the non-headless mode for short interactive checks on a machine with a display, per `isaac-sim`'s own headless-first guidance. ## Customization - **Different task or robot:** `list_envs.py` (Quick start) is the source of truth for what's currently registered — pick an existing task close to the target robot/behavior before authoring a new one. When you do author one, a custom robot/task is an **external project, not a fork of Isaac Lab**: `./isaaclab.sh --new` scaffolds a standalone repo that pip-installs Isaac Lab and `gym.register`s the task. The in-repo "internal task" path is only for upstreaming and is auto-disabled whenever Isaac Lab is pip-installed (i.e. inside the prebuilt NGC container), so external-project is the only path there. See `references/go2-rl-workflow.md`; Isaac Lab's own tutorials (linked in References) cover task authoring in depth this skill does not duplicate. - **Different RL library:** swap which library's subdirectory of the reinforcement-learning scripts tree you invoke (`rsl_rl`, `skrl`, `rl_games`, `sb3`) — each wraps the same underlying Isaac Lab environment with that library's own agent config and CLI flags, so a task that works under one library isn't a guaranteed drop-in for another's config shape. - **No local GPU meeting the floor:** don't try to run Isaac Lab without it — route to `lerobot`'s own sim/eval tooling (per `architect`'s manipulation-vertical guidance) or provision a remote GPU host meeting `isaac-sim`'s floor first. ## References - `references/prebuilt-image-runpod.md` — the prebuilt `nvcr.io/nvidia/ isaac-lab` image (observed tag 3.0.0-beta2-post1) and its RunPod provisioning specifics: NGC auth, EULA env vars, the `/workspace` volume-shadow gotcha, and the entrypoint override + stop→start. Points to the `environments` skill for the general pod networking/SSH mechanics. - `references/go2-rl-workflow.md` — the verified Unitree Go2 RSL-RL run: task IDs, smoke and full-training profile, the experiment-name log-path trap, reward/cost-in-config layout and 3-layer override chain, the external-project route for custom robots, and checkpoint/script portability on a fresh pod. - Upstream: [Isaac Lab documentation](https://isaac-sim.github.io/IsaacLab/) (installation, quickstart, and task/training concepts — primary source for this skill, fetched directly on 2026-07-10), [isaac-sim/IsaacLab GitHub repo](https://github.com/isaac-sim/IsaacLab) (the reinforcement-learning, imitation-learning, tools, and environments scripts subdirectories, fetched directly via the GitHub Contents API and raw file URLs this session — source of the exact script paths, task-ID convention, and export-format claims above). Sibling skills: `isaac-sim` (GPU floor, install, and the Isaac Sim instance this skill runs on top of), `lerobot` (alternative manipulation ML path; owns real-robot-dataset imitation learning and the `isaaclab_arena` EnvHub hand-off), `data` (data-sourcing strategy, including how much this skill's own demo-generation tools should contribute), `simulation` (simulator selection before Isaac Sim is chosen), `architect` (routes here, GPU-gated, decides `isaac-lab` vs. `lerobot`). ## Changelog - 1.1.1 (2026-08-01): anchor IDs added to claim-bearing items (learning-engine Phase 1); no content changes. - 1.1.0 (2026-07-31): hardened from the go2-locomotion RunPod L4 run (2026-07-26..28) — added the prebuilt-image-runpod and go2-rl-workflow references, made the prebuilt `nvcr.io/nvidia/isaac-lab` image the preferred cloud path, and surfaced the experiment-name log-path trap and external-project custom-robot route. - 1.0.1 (2026-07-12): skill-refiner run 1 — provenance claims date-stamped ('this session' → 2026-07-10, the authoring session) so the staleness sweep can age them.