标题： 迈向自主微移动的规模化城市仿真 显示英文标题

标题： Towards Autonomous Micromobility through Scalable Urban Simulation

摘要： 微移动利用轻型移动设备在城市公共空间中移动，如配送机器人和电动滑板车，成为车辆移动的一种有前景的替代方案。当前的微移动主要依赖于人工手动操作（现场或远程控制），这在导航繁忙的城市环境时引发了安全性和效率问题，因为这些环境中充满了不可预测的障碍物和行人。使用AI代理协助人类操控微移动设备为提高安全性和效率提供了一种可行的解决方案。在这项工作中，我们提出了一个可扩展的城市模拟解决方案以推进自主微移动的发展。 首先，我们构建了URBAN-SIM——一个高性能的机器人学习平台，用于在交互式城市场景中大规模训练具身代理。URBAN-SIM包含了三个关键模块：分层城市生成流水线、交互动力学生成策略以及异步场景采样方案，以提高仿真中机器人学习的多样性、真实性和效率。 然后，我们提出了URBAN-BENCH——一系列基本任务和基准，用于衡量AI代理在实现自主微移动方面的能力。URBAN-BENCH包括基于代理的三种核心技能的八项任务：城市移动、城市导航和城市穿行。我们在这些任务上评估了四种具有不同形态的机器人，例如轮式和腿足机器人。 在不同地形和城市结构上的实验揭示了每种机器人的优势和局限性。 显示英文摘要

摘要： Micromobility, which utilizes lightweight mobile machines moving in urban public spaces, such as delivery robots and mobility scooters, emerges as a promising alternative to vehicular mobility. Current micromobility depends mostly on human manual operation (in-person or remote control), which raises safety and efficiency concerns when navigating busy urban environments full of unpredictable obstacles and pedestrians. Assisting humans with AI agents in maneuvering micromobility devices presents a viable solution for enhancing safety and efficiency. In this work, we present a scalable urban simulation solution to advance autonomous micromobility. First, we build URBAN-SIM - a high-performance robot learning platform for large-scale training of embodied agents in interactive urban scenes. URBAN-SIM contains three critical modules: Hierarchical Urban Generation pipeline, Interactive Dynamics Generation strategy, and Asynchronous Scene Sampling scheme, to improve the diversity, realism, and efficiency of robot learning in simulation. Then, we propose URBAN-BENCH - a suite of essential tasks and benchmarks to gauge various capabilities of the AI agents in achieving autonomous micromobility. URBAN-BENCH includes eight tasks based on three core skills of the agents: Urban Locomotion, Urban Navigation, and Urban Traverse. We evaluate four robots with heterogeneous embodiments, such as the wheeled and legged robots, across these tasks. Experiments on diverse terrains and urban structures reveal each robot's strengths and limitations.