Problem
Research questions and friction points this paper is trying to address.
occupancy-grid simulators
3D simulators
scalability
autonomous racing
policy acquisition
RoboRacer Arena: Scaling High-Fidelity Autonomous Racing in Isaac Sim
🤖 AI Summary
为解决自动驾驶赛车模拟中环境创建和扩展问题，提出RoboRacer Arena系统，利用占用图生成3D赛道，并支持自然语言描述生成赛道。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
occupancy maps
3D racing environments
flood fill algorithm
USD stage
natural language descriptions
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