用大模型生成真实感人机导航环境，支持快速测试机器人策略。
Arena 4.0: A Comprehensive ROS2 Development and Benchmarking Platform for Human-centric Navigation Using Generative-Model-based Environment Generation
- 通过文本或平面图驱动大模型与扩散模型生成复杂人机共存场景。
- 平台用户研究显示易用性与效率显著优于前代版本。
- 专为现代机器人设计，支持真实部署与社交导航算法评测。
基于前期工作，本文提出 Arena 4.0，相较于 Arena 3.0、Arena-Bench、Arena 1.0 与 Arena 2.0 实现了显著升级。其核心贡献包括：(1) 基于生成模型的世界与场景生成方法，利用大语言模型（LLMs）与扩散模型，从文本提示或 2D 平面图动态生成复杂、以人为中心的环境，适用于社交导航策略的开发与评测；(2) 构建可扩展的 3D 模型数据库，支持语义标注与动态调用，实现 3D 场景中资产的智能布局；(3) 完成向 ROS 2 的全面迁移，兼容现代硬件，提升导航性能、可用性及在真实机器人上的部署便利性。通过综合用户研究验证，平台在易用性与效率方面相比前代版本有显著提升。项目开源地址：https://github.com/Arena-Rosnav。
原文摘要 · Abstract (English)
Building on the foundations of our previous work, this paper introduces Arena 4.0, a significant advancement over Arena 3.0, Arena-Bench, Arena 1.0, and Arena 2.0. Arena 4.0 offers three key novel contributions: (1) a generative-model-based world and scenario generation approach that utilizes large language models (LLMs) and diffusion models to dynamically generate complex, human-centric environments from text prompts or 2D floorplans, useful for the development and benchmarking of social navigation strategies; (2) a comprehensive 3D model database, extendable with additional 3D assets that are semantically linked and annotated for dynamic spawning and arrangement within 3D worlds; and (3) a complete migration to ROS 2, enabling compatibility with modern hardware and enhanced functionalities for improved navigation, usability, and easier deployment on real robots. We evaluated the platform's performance through a comprehensive user study, demonstrating significant improvements in usability and efficiency compared to previous versions. Arena 4.0 is openly available at https://github.com/Arena-Rosnav.
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