---
id: mjlab
version: "1.6.0"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# mjlab — Isaac Lab API, powered by MuJoCo-Warp, for RL and robotics research.

License: permissive · Maintenance: active · Downloads: 97.6K/mo

## What it is and what it does

mjlab is a reinforcement learning and robotics simulation framework that combines Isaac Lab's composable environment API with MuJoCo Warp, a GPU-accelerated physics engine. It provides building blocks for designing robot control tasks with direct access to native MuJoCo data structures, enabling efficient training of policies for humanoid robots and other agents on GPU clusters.

The framework is designed for research and production robot learning workflows. It supports multi-GPU distributed training, motion imitation from reference trajectories, velocity tracking, and policy evaluation. Users define tasks declaratively (e.g., 'Mjlab-Velocity-Flat-Unitree-G1') and train agents using built-in RL algorithms, with integration to Weights & Biases for experiment tracking and checkpoint management.

Use it for:

- Train humanoid robots to follow velocity commands or track reference motions on flat or complex terrain
- Run large-scale parallel simulations across multiple GPUs for faster policy learning
- Evaluate trained policies in simulation before deployment using checkpoint loading from experiment tracking
- Prototype new robot control tasks using composable environment building blocks and native MuJoCo data access
- Conduct robotics research with reproducible, GPU-accelerated physics simulation and integrated logging

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

mjlab provides a GPU-accelerated reinforcement learning and robotics simulation framework combining Isaac Lab's manager-based API with MuJoCo Warp for composable environment design and training.

Yes, if you are conducting GPU-accelerated robot learning research or building production robot control systems. The active maintenance, production-stable status, permissive license, and integration with standard ML tools (PyTorch, Weights & Biases, TensorBoard) make it a solid choice. The large dependency footprint and GPU requirement are expected for this use case. Not suitable for CPU-only or lightweight simulation needs.

## Install

pip install mjlab
uv add mjlab
poetry add mjlab

## Installing mjlab

Before you install: Low install friction with a pure-Python wheel. Active maintenance (last commit 2026-08-14, 5 days old) and production-stable status. However, 20 runtime dependencies including torch, mujoco, and warp-lang create a substantial dependency footprint; installation will pull in significant machine-learning and simulation libraries.

License in practice: Apache-2.0 permissive license allows commercial and private use. Some utilities are forked from NVIDIA Isaac Lab (BSD-3-Clause); those components retain their original licenses per file headers.

Quickstart:

# Install
pip install mjlab

# Run demo (requires NVIDIA GPU for training)
from mjlab import demo

# Or use command-line interface
# uv run train Mjlab-Velocity-Flat-Unitree-G1 --env.scene.num-envs 4096

NVIDIA GPU required for training; macOS supported for evaluation only. Requires Python 3.10–3.13.

Verify before relying:

- Whether torch and mujoco-warp installation on macOS works for evaluation-only workflows despite GPU-training requirement
- Specific CUDA version compatibility requirements not stated in fact sheet
- Whether all 20 runtime dependencies are strictly required or if some are optional for basic usage

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 97.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

gpu accelerated robotics simulation, reinforcement learning framework mujoco, robot learning environment builder, mujoco warp training, isaac lab api robotics, humanoid control training, physics simulation reinforcement learning, robotics, gpu-simulation, reinforcement-learning

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