v1.1.3 to git:20260907.863840e

- version: 1.1.3

- description: >

- 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).

- # 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.** <!-- id: prefer-prebuilt-image --> `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 `runpod` skill for the general Pod inventory,

- networking, diagnostics, and lifecycle mechanics this builds on.

- - **The GPU/driver floor is `isaac-sim`'s, not restated here.** <!-- id: gpu-floor-inherited-from-isaac-sim --> 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.** <!-- id: start-from-prebuilt-task --> 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.** <!-- id: no-task-id-facts-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** <!-- id: confirm-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.** <!-- id: cloud-gpu-prebuilt-image-step -->

- `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** <!-- id: workstation-pip-source-install --> (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:** <!-- id: list-registered-tasks -->

-

- ```bash

- python scripts/environments/list_envs.py

- ```

-

- **4. Train on a prebuilt task** <!-- id: train-prebuilt-task-command --> 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.** <!-- id: run-prebuilt-task-ids --> Task IDs follow `Isaac-<Name>-v0` (manager-based

- workflow) or `Isaac-<Name>-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=<n>` 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.** <!-- id: train-monitor-tensorboard --> 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/<library>/<task>/`; 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.** <!-- id: go2-checkpoint-path-trap --> 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/<timestamp>/`, **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.** <!-- id: export-policy-jit-onnx --> `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.** <!-- id: sim-imitation-learning-path --> 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.** <!-- id: lerobot-isaaclab-arena-handoff --> `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."** <!-- id: version-pairing-narrower --> 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`.** <!-- id: headless-default-training -->

- `--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 `runpod` skill for general Pod inventory, networking, diagnostics, and

- lifecycle 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

- <!-- One dated line per battle-tested change, added by skill-author hardening sessions. -->

- - 1.1.3 (2026-08-24): route general RunPod mechanics from the

- workload-specific prebuilt-image reference to the new `runpod` skill.

- - 1.1.2 (2026-08-03): style pass; removed em dashes throughout (no content changes).

- - 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.