MuJoCo Playground
Kevin Zakka, Baruch Tabanpour, Qiayuan Liao, Mustafa Haiderbhai,, Samuel Holt, Jing Yuan Luo, Arthur Allshire, Erik Frey, Koushil Sreenath,, Lueder A. Kahrs, Carmelo Sferrazza, Yuval Tassa, Pieter Abbeel

TL;DR
MuJoCo Playground is an open-source framework that simplifies robot simulation and training, supporting diverse robots and enabling effective zero-shot sim-to-real transfer with minimal setup.
Contribution
It introduces a comprehensive, easy-to-use platform combining physics simulation, rendering, and training environments for rapid robot policy development.
Findings
Supports diverse robotic platforms including quadrupeds and humanoids
Enables zero-shot sim-to-real transfer from state and pixel inputs
Allows training policies in minutes on a single GPU
Abstract
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
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDigital Games and Media
