# MuJoCo Playground

arXiv: https://arxiv.org/abs/2502.08844 · HTML: https://mlsanity.com/paper/2502.08844/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2025.

## Built on by (most cited first, 30 of 78)

- [Flow Matching Policy Gradients](https://mlsanity.com/paper/2507.21053/) (2025)
- [PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation](https://mlsanity.com/paper/2601.03782/) (2026)
- [ASPIRE: Agentic /Skills Discovery for Robotics](https://mlsanity.com/paper/2607.00272/) (2026)
- [RSL-RL: A Learning Library for Robotics Research](https://mlsanity.com/paper/2509.10771/) (2025)
- [FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control](https://mlsanity.com/paper/2505.22642/) (2025)
- [Learning Getting-Up Policies for Real-World Humanoid Robots](https://mlsanity.com/paper/2502.12152/) (2025)
- [Eye, Robot: Learning to Look to Act with a BC-RL Perception-Action Loop](https://mlsanity.com/paper/2506.10968/) (2025)
- [Viser: Imperative, Web-based 3D Visualization in Python](https://mlsanity.com/paper/2507.22885/) (2025)
- [GaussGym: An open-source real-to-sim framework for learning locomotion from pixels](https://mlsanity.com/paper/2510.15352/) (2025)
- [GSWorld: Closed-Loop Photo-Realistic Simulation Suite for Robotic Manipulation](https://mlsanity.com/paper/2510.20813/) (2025)
- [Learning Sim-to-Real Humanoid Locomotion in 15 Minutes](https://mlsanity.com/paper/2512.01996/) (2025)
- [Flow Policy Gradients for Robot Control](https://mlsanity.com/paper/2602.02481/) (2026)
- [SoftMimic: Learning Compliant Whole-body Control from Examples](https://mlsanity.com/paper/2510.17792/) (2025)
- [HumanoidGen: Data Generation for Bimanual Dexterous Manipulation via LLM Reasoning](https://mlsanity.com/paper/2507.00833/) (2025)
- [Simplicial Embeddings Improve Sample Efficiency in Actor-Critic Agents](https://mlsanity.com/paper/2510.13704/) (2025)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training](https://mlsanity.com/paper/2507.04452/) (2025)
- [End-to-end RL Improves Dexterous Grasping Policies](https://mlsanity.com/paper/2509.16434/) (2025)
- [FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control](https://mlsanity.com/paper/2604.04539/) (2026)
- [A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies](https://mlsanity.com/paper/2604.13645/) (2026)
- [AGILE: A Comprehensive Workflow for Humanoid Loco-Manipulation Learning](https://mlsanity.com/paper/2603.20147/) (2026)
- [πR2: Reactive Real-time Flow Policies](https://mlsanity.com/paper/2607.26055/) (2026)
- [Monte Carlo Beam Search for Actor-Critic Reinforcement Learning in Continuous Control](https://mlsanity.com/paper/2505.09029/) (2025)
- [Booster Gym: An End-to-End Reinforcement Learning Framework for Humanoid Robot Locomotion](https://mlsanity.com/paper/2506.15132/) (2025)
- [What Matters for Simulation to Online Reinforcement Learning on Real Robots](https://mlsanity.com/paper/2602.20220/) (2026)
- [Thousand-GPU Large-Scale Training and Optimization Recipe for AI-Native Cloud Embodied Intelligence Infrastructure](https://mlsanity.com/paper/2603.11101/) (2026)
- [Unreal Robotics Lab: A High-Fidelity Robotics Simulator with Advanced Physics and Rendering](https://mlsanity.com/paper/2504.14135/) (2025)
- [MOSAIC: Skill-Centric Manipulation Planning with Physics Simulation](https://mlsanity.com/paper/2504.16738/) (2025)
- [The Cell Must Go On: Agar.io for Continual Reinforcement Learning](https://mlsanity.com/paper/2505.18347/) (2025)
- [NaviGait: Navigating Dynamically Feasible Gait Libraries using Deep Reinforcement Learning](https://mlsanity.com/paper/2510.11542/) (2025)

## Abstract (arXiv)

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