RoboRacer Arena: Scaling High-Fidelity Autonomous Racing in Isaac Sim
作者: Mihaela-Larisa Clement, Agnes Poks, Ezio Bartocci
分类: cs.RO
发布日期: 2026-08-24
备注: Submitted to ICRA 2027
💡 一句话要点
提出RoboRacer Arena以解决高保真自主赛车环境生成问题
🎯 匹配领域: 支柱三：空间感知与语义 (Perception & Semantics)
关键词: 自主驾驶 赛车模拟 环境生成 占用图 自然语言处理 高保真模拟 程序生成 验证测试
📋 核心要点
- 现有的占用网格模拟器无法处理物理接触，而3D模拟器需要为每个赛道单独实现，导致可扩展性不足。
- 本文提出RoboRacer Arena，通过占用图直接生成3D赛车环境，使用洪水填充算法提取可驾驶区域和赛道边界。
- 在30次匹配试验中，系统生成了21张地图，均通过验证，并在256个并行刚体车辆的基准测试中达到了每秒8707个车辆步骤的性能。
📝 摘要（中文）
RoboRacer提供了一个标准化的平台用于1:10比例的自主车辆研究，但现有赛道的多样性限制了策略获取。现有的占用网格模拟器无法处理物理接触，而3D模拟器需要为每个赛道单独实现，限制了可扩展性。为此，本文开发了RoboRacer Arena，通过占用图直接创建3D赛车环境。该方法使用洪水填充算法提取可驾驶走廊并识别赛道边界，计算距离场以定义碰撞边界，并将赛道表面、碰撞属性和材料组装成USD阶段，实现环境的自动生成。该系统支持从自然语言生成赛道规范，并在30次匹配试验中生成了21张地图，均通过验证。
🔬 方法详解
问题定义：本文旨在解决现有赛车模拟环境生成的可扩展性问题。现有方法在处理物理接触和赛道多样性方面存在不足，限制了策略的获取和应用。
核心思路：RoboRacer Arena的核心思路是通过占用图直接生成3D赛车环境，避免了传统方法中对每个赛道单独实现的需求，从而提高了生成效率和可扩展性。
技术框架：该系统的整体架构包括几个主要模块：首先使用洪水填充算法提取可驾驶走廊和赛道边界，然后计算距离场以定义碰撞边界，最后将赛道表面、碰撞属性和材料组装成USD阶段，实现环境的自动生成。
关键创新：最重要的技术创新点在于通过占用图生成3D环境的能力，结合自然语言描述生成赛道规范，显著提高了环境生成的灵活性和效率。
关键设计：在设计中，采用了几何筛选、程序生成和光栅级验证等技术细节，以确保生成环境的一致性和可重复性。
🖼️ 关键图片
📊 实验亮点
在基准测试中，RoboRacer Arena在256个并行刚体车辆的情况下达到了每秒8707个车辆步骤的性能，显示出其在高并发环境下的优越性。此外，系统生成的21张地图在30次匹配试验中均通过验证，证明了其可靠性。
🎯 应用场景
RoboRacer Arena的研究成果可广泛应用于自动驾驶、机器人竞赛和虚拟现实等领域。通过提供高保真度的赛车环境，该系统能够加速自主驾驶算法的开发与测试，推动相关技术的进步与应用。
📄 摘要（原文）
RoboRacer offers a standardized platform for research using 1:10-scale autonomous vehicles, but the variety of available tracks hinders the process of acquiring policies. Although existing occupancy-grid simulators allow for the quick addition of new maps, they fail to include physical contact, while 3D simulators require each circuit to be implemented as a separate asset, thus limiting their scalability. In order to overcome this issue, we have developed RoboRacer Arena, a system that creates 3D racing environments directly from occupancy maps. Our method starts by using a flood fill algorithm to extract the drivable corridors and to identify the track boundaries, which are then used to establish the barriers. A distance field is calculated to define the collision boundaries. The track surfaces, collision properties, and materials are assembled into a USD stage, which allows for the automated and reproducible generation of the environment in Isaac Sim. The input maps can be obtained from SLAM sessions, from rescaled Formula 1 circuits, or from natural-language descriptions. When the input is based on natural language, we use Gemma 4 31B to generate a track specification without specifying any coordinates or geometry. To guarantee consistency and reproducibility, we apply geometric screening, procedural generation, and raster-level validation. The simulation environments are initialized in a time range of 1.18 to 2.48 seconds, with the initialization time increasing linearly as the raster size increases. In 30 matched trials involving 10 tracks and 3 seeds, 21 maps were generated and all passed validation. RoboRacer Arena currently contains 130 tracks and supports the generation of tracks from natural language. In benchmark tests, the system attains 8,707 vehicle-steps per second when using 256 parallel rigid-body vehicles, excluding the time taken for rendering and policy execution.