开源机器人学习框架，支持多种机器人快速训练与真实世界部署。
MuJoCo Playground
- 基于MJX构建，集成物理引擎与渲染器，支持多平台机器人仿真。
- 单卡训练可在数分钟内完成，支持状态与视觉输入的零样本迁移到真实机器人。
- 适合机器人研究者快速搭建实验，尤其关注模拟到现实迁移的团队。
我们提出MuJoCo Playground，一个完全开源的机器人学习框架，基于MJX构建，旨在简化仿真、训练及模拟到现实（sim-to-real）的迁移过程。仅需执行“pip install playground”，研究人员即可在单张GPU上于数分钟内完成策略训练。该框架支持四足、人形、灵巧手和机械臂等多种机器人平台，实现从状态输入和像素输入出发的零样本模拟到现实迁移。这一目标通过整合物理引擎、批量渲染器和训练环境的完整栈实现。配套视频演示及完整代码已公开，可免费访问playground.mujoco.org。
原文摘要 · Abstract (English)
We introduce MuJoCo Playground, a fully open-source framework for robot learning built with MJX, with the express goal of streamlining simulation, training, and sim-to-real transfer onto robots. With a simple "pip install playground", researchers can train policies in minutes on a single GPU. Playground supports diverse robotic platforms, including quadrupeds, humanoids, dexterous hands, and robotic arms, enabling zero-shot sim-to-real transfer from both state and pixel inputs. This is achieved through an integrated stack comprising a physics engine, batch renderer, and training environments. Along with video results, the entire framework is freely available at playground.mujoco.org
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