轻量级框架mjlab，用GPU加速机器人学习仿真。
mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning
- 模块化设计，可组合观测、奖励与事件组件。
- 单命令安装，依赖少，直接访问MuJoCo原生数据结构。
- 适合快速验证机器人控制算法的研究者。
我们提出mjlab，一个轻量级开源机器人学习框架，结合了GPU加速仿真与可组合环境，极大降低设置门槛。mjlab采用Isaac Lab提出的基于管理器的API，用户可通过模块化组件构建观测、奖励和事件逻辑，并搭配MuJoCo Warp实现物理引擎的GPU加速。该框架仅需单命令安装，依赖极少，且支持直接访问原生MuJoCo数据结构。mjlab内置速度跟踪、动作模仿和操作任务的参考实现，便于快速实验与验证。
原文摘要 · Abstract (English)
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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