MuJoCo Playground

2025-02-12

AI Summary

To address the inefficiency of simulation-based training and the difficulty of sim-to-real transfer in robot learning, this paper introduces the first fully open-source, integrated framework built upon MJX (JAX-accelerated MuJoCo), unifying physics simulation, batched GPU rendering, and reinforcement learning training. The framework supports diverse robotic morphologies—including quadrupeds, humanoid robots, dexterous hands, and manipulators—and enables end-to-end policy training in minutes on a single GPU. Crucially, it achieves cross-modal zero-shot sim-to-real transfer—demonstrated for both state-based and pixel-based inputs—within a unified open platform; pre-trained policies deploy directly onto real-world quadrupeds and robotic arms without fine-tuning. All code, pretrained models, and demonstration videos are publicly released at playground.mujoco.org.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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

Problem

• Streamlines robot learning simulation
• Enables fast policy training
• Supports diverse robotic platforms

Innovation

• Open-source robot learning framework
• Single GPU policy training
• Zero-shot sim-to-real transfer