🏆 Winner of the Outstanding Demo Paper Award at RSS 2025
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 installation process (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, and enables 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. MuJoCo Playground was a community effort involving multiple groups, and we hope it proves valuable to researchers and developers alike.
@misc{zakka2025mujocoplayground,
title={MuJoCo Playground},
author={Kevin Zakka and Baruch Tabanpour and Qiayuan Liao and Mustafa Haiderbhai and Samuel Holt and Jing Yuan Luo and Arthur Allshire and Erik Frey and Koushil Sreenath and Lueder A. Kahrs and Carmelo Sferrazza and Yuval Tassa and Pieter Abbeel},
year={2025},
eprint={2502.08844},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2502.08844},
}
Disclaimer: All videos on this page except the teaser are shown in real-time (1x speed).
