---

name: mjlab description: "Use mjlab for MuJoCo Warp robot-learning environments, manager-based MDP configuration, RSL-RL training/playback, sensors, terrain, and debugging workflows." metadata: disco-role: operating scenarios: - reinforcement-learning-workflows - embodied-ai-simulation disable-model-invocation: true license: Apache 2.0

mjlab

mjlab is a lightweight robot-learning framework that brings an Isaac Lab-style manager API to MuJoCo Warp. Use this repo skill when a task involves composing robot simulation scenes, writing manager-based RL environments, configuring MDP terms, training or playing policies with RSL-RL, using cameras/raycasts/contact sensors, generating terrain, exporting scenes, or debugging NaNs/rendering.

Fast orientation

• Package/import name: mjlab.
• Public command surface: train, play, demo, list-envs, export-scene, and viz-nan.
• Primary runtime: Linux with NVIDIA CUDA for training and full MuJoCo Warp throughput. CPU mode is useful for config loading, CLI help, and some evaluation/debugging tasks; it is not proof of full GPU training behavior.
• CLI parsing uses Tyro: boolean values must be explicit and collection values use Python-literal syntax.

Read installation-runtime.md before installing or choosing CPU/GPU/Docker execution. Read package-overview.md for the architectural map and high-value entry points. Read troubleshooting.md for cross-cutting install, backend, CLI, rendering, W&B, and data issues. Use scripts/mjlab_environment_smoke.py for a safe installed-package smoke check.

Route by task

If the user asks about...	Read this
ManagerBasedRlEnvCfg, observation/reward/event/command dictionaries, term lifecycle, reset/step ordering, SceneEntityCfg, custom manager terms	environment-configuration
SceneCfg, EntityCfg, MJCF/spec editing, simulation config, asset-zoo robots, variants, scene export API internals	scene-simulation-assets
action configs, actuator configs, built-in observations/rewards/terminations/events/commands/curriculum/metrics, differential IK, task-specific MDP terms	mdp-components
cameras, raycasts, contacts, terrain generation, flat patches, terrain curricula, domain randomization, sensor context or rendering issues	perception-terrain-randomization
list-envs, train, play, demo, export-scene, viz-nan, task registry, RSL-RL configs, checkpoints, W&B, motion CSV/NPZ, cloud/distributed training	training-evaluation-cli

Safe first commands

Use the installed package, not a source checkout, for normal user workflows:

uv run list-envs
uv run train Mjlab-Cartpole-Balance --help
uv run play Mjlab-Cartpole-Balance --help
uv run export-scene g1 --help

For package-level verification from any project that has mjlab installed, copy this skill's smoke script into a temporary location or run it by path:

uv run python path/to/mjlab_environment_smoke.py --json

Installation decision points

• For a new uv-managed user project, prefer uv add mjlab.
• For contributing to mjlab itself, use uv sync and invoke commands with uv run.
• For Linux NVIDIA training, use the CUDA-capable dependency variant selected by the project environment or a Docker image with the NVIDIA container runtime.
• For macOS or CPU-only machines, limit expectations to evaluation, config inspection, CLI help, and workflows that do not require CUDA graph capture.

Do not use this skill when

• The task is about a generic RL algorithm library unrelated to mjlab's environment/runtime layer.
• The task requires Isaac Sim or Omniverse APIs rather than MuJoCo/MuJoCo Warp.
• The user is asking for benchmark systemd jobs, W&B sweeps, or cloud launches without explicitly authorizing credentials, network access, and GPU cost.
• The user is editing this repository's code as a maintainer task; then combine this operating skill with normal repository-maintenance evidence and tests.

Provenance and routing metadata

• repo-provenance.md records the source commit, tag, package version, and evidence baseline used to create this skill.
• repo-routing-metadata.json is the structured import metadata for the managed repo-skills router.