We present mjlab, a lightweight, open-source framework for robot learning that combines GPU-accelerated simulation with composable environments and minimal setup friction. mjlab 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. mjlab ships with reference implementations of velocity tracking, motion imitation, and manipulation tasks.
@article{arxiv.2601.22074,
title = {mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning},
author = {Kevin Zakka and Qiayuan Liao and Brent Yi and Louis Le Lay and Koushil Sreenath and Pieter Abbeel},
journal= {arXiv preprint arXiv:2601.22074},
year = {2026}
}
Comments
Comments: 11 pages; Code is available at https://github.com/mujocolab/mjlab ; Expanded sensor and domain randomization sections, added references, minor edits