Welcome to mjlab!

Welcome to mjlab!#

mjlab

mjlab is a lightweight, open-source framework for robot learning that combines GPU-accelerated simulation with composable environments and minimal setup friction. It adopts the manager-based API introduced by Isaac Lab, where users compose modular building blocks for observations, rewards, and events, and pairs it with MuJoCo Warp for GPU-accelerated physics. The result is a framework installable with a single command, requiring minimal dependencies, and providing direct access to native MuJoCo data structures.

Key features:

  • Composable environments: users define observations, rewards, terminations, and other MDP terms as modular building blocks

  • Minimal dependencies: single-command install via uv, low startup latency

  • Direct MuJoCo data structures: native MjModel/MjData access with no translation layers

  • PyTorch-native: observations, rewards, and actions are PyTorch tensors backed by zero-copy GPU memory sharing

For more on the design decisions behind mjlab, see Why mjlab?.

Try it now (no installation needed):

uvx --from mjlab --refresh demo

Table of Contents#

User Guide

Concepts

The Manager Layer

Training & Debugging

API Reference

Further Reading

License & citation#

mjlab is licensed under the Apache License, Version 2.0. Please refer to the LICENSE file for details.

If you use mjlab in your research, we would appreciate a citation:

@article{Zakka_mjlab_A_Lightweight_2026,
    author = {Zakka, Kevin and Liao, Qiayuan and Yi, Brent and Le Lay, Louis and Sreenath, Koushil and Abbeel, Pieter},
    title = {{mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning}},
    url = {https://arxiv.org/abs/2601.22074},
    year = {2026}
}

Acknowledgments#

mjlab would not exist without the excellent work of the Isaac Lab team, whose API design and abstractions mjlab builds upon.

Thanks also to the MuJoCo Warp team — especially Erik Frey and Taylor Howell — for answering our questions, giving helpful feedback, and implementing features based on our requests countless times.

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