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arXiv · 2601.22074

mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning

Kevin Zakka·Qiayuan Liao·Brent Yi·Louis Le Lay·Koushil Sreenath·Pieter Abbeel
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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.

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Kevin Zakka, Qiayuan Liao, Brent Yi, Louis Le Lay, Koushil Sreenath, Pieter Abbeel. 2026-01-29. mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning. https://arxiv.org/abs/2601.22074

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