MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility

MetaUrban is a cutting-edge simulation platform designed for Embodied AI research in urban spaces. It offers:

• 🌆 Infinite Urban Scene Generation: Create diverse, interactive city environments.
• 🏗️ High-Quality Urban Objects: Includes real-world infrastructure and clutter.
• 🧍 Rigged Human Models: SMPL-compatible models with diverse motions.
• 🤖 Urban Agents: Including delivery robots, cyclists, skateboarders, and more.
• 🕹️ Flexible User Interfaces: Compatible with mouse, keyboard, joystick, and racing wheel.
• 🎥 Configurable Sensors: Supports RGB, depth, semantic map, and LiDAR.
• ⚙️ Rigid-Body Physics: Realistic mechanics for agents and environments.
• 🌍 OpenAI Gym Interface: Seamless integration for AI and reinforcement learning tasks.
• 🔗 Framework Compatibility: Seamlessly integrates with Ray, Stable Baselines, Torch, Imitation, etc.

📖 Check out MetaUrban Documentation to learn more!

Latest Updates

• [24/01/25] v0.1.0: The first official release of MetaUrban :wrench: [release notes]

Table of Contents

• MetaUrban
  • 📎 Citation
  • 🛠 Quick Start
    • Hardware Recommendations
    • Installation
    • Docker Setup
  • 🏃‍♂️ Simulation Environment Roam
    • Point Navigation Environment
    • Social Navigation Environment
  • 🤖 Run a Pre-Trained (PPO) Model
  • 🚀 Model Training and Evaluation
    • Reinforcement Learning
      • Training
      • Evaluation
    • Imitation Learning
      • Data collection
      • Training
  • 📖 Questions and Support

📎 Citation

If you find MetaUrban helpful for your research, please cite the following BibTeX entry.

@article{wu2025metaurban,
  title={MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility},
  author={Wu, Wayne and He, Honglin and He, Jack and Wang, Yiran and Duan, Chenda and Liu, Zhizheng and Li, Quanyi and Zhou, Bolei},
  journal={ICLR},
  year={2025}
}

🛠 Quick Start

Hardware Recommendations

To ensure the best experience with MetaUrban, please review the following hardware guidelines:

• Tested Platforms:
  • Linux: Supported and Recommended (preferably Ubuntu).
  • Windows: Works with WSL2.
  • MacOS: Supported.
• Recommended Hardware:
  • GPU: Nvidia GPU with at least 8GB RAM and 3GB VRAM.
  • Storage: Minimum of 10GB free space.

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