Towards Autonomous Micromobility through Scalable Urban Simulation
Journal:
arXiv
Published Date:
May 1, 2025
Abstract
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.
