MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility
Keywords: Micromobility, Simulation Environment, Embodied AI
TL;DR: MetaUrban is a compositional simulation platform for AI-driven urban micromobility research. It will be open-source to enable more research opportunities for the community, and foster generalizable and safe embodied AI and micromobility in cities.
Abstract: Public urban spaces like streetscapes and plazas serve residents and accommodate social life in all its vibrant variations. Recent advances in Robotics and Embodied AI make public urban spaces no longer exclusive to humans. Food delivery bots and electric wheelchairs have started sharing sidewalks with pedestrians, while robot dogs and humanoids have recently emerged in the street. **Micromobility** enabled by AI for short-distance travel in public urban spaces plays a crucial component in the future transportation system. Ensuring the generalizability and safety of AI models maneuvering mobile machines is essential. In this work, we present **MetaUrban**, a *compositional* simulation platform for the AI-driven urban micromobility research. MetaUrban can construct an *infinite* number of interactive urban scenes from compositional elements, covering a vast array of ground plans, object placements, pedestrians, vulnerable road users, and other mobile agents’ appearances and dynamics. We design point navigation and social navigation tasks as the pilot study using MetaUrban for urban micromobility research and establish various baselines of Reinforcement Learning and Imitation Learning. We conduct extensive evaluation across mobile machines, demonstrating that heterogeneous mechanical structures significantly influence the learning and execution of AI policies. We perform a thorough ablation study, showing that the compositional nature of the simulated environments can substantially improve the generalizability and safety of the trained mobile agents. MetaUrban will be made publicly available to provide research opportunities and foster safe and trustworthy embodied AI and micromobility in cities. The code and dataset will be publicly available.
Supplementary Material: zip
Primary Area: datasets and benchmarks
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Submission Number: 1653
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