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.

翻译：本文提出mjlab，一个轻量级、开源的机器人学习框架，它将GPU加速仿真与可组合环境及最小化设置障碍相结合。mjlab采用了Isaac Lab引入的基于管理器的API，用户可通过该API组合用于观测、奖励和事件的模块化构建块，并将其与MuJoCo Warp配对以实现GPU加速物理计算。由此产生的框架可通过单一命令安装，依赖项极少，并能直接访问原生MuJoCo数据结构。mjlab随附了速度跟踪、运动模仿和操作任务的参考实现。