mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning

Abstract

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.

Comments: 11 pages; Code is available at https://github.com/mujocolab/mjlab ; Expanded sensor and domain randomization sections, added references, minor edits