GRUtopia: Dream General Robots in a City at Scale
Authors: name: Hanqing Wang, name: Jiahe Chen, name: Wensi Huang, name: Qingwei Ben, name: Tai Wang, name: Boyu Mi, name: Tao Huang, name: Siheng Zhao, name: Yilun Chen, name: Sizhe Yang, name: Peizhou Cao, name: Wenye Yu, name: Zichao Ye, name: Jialun Li, name: Junfeng Long, name: Zirui Wang, name: Huiling Wang, name: Ying Zhao, name: Zhongying Tu, name: Yu Qiao, name: Dahua Lin, name: Jiangmiao Pang

Recent works have been exploring the scaling laws in the field of Embodied
AI. Given the prohibitive costs of collecting real-world data, we believe the
Simulation-to-Real (Sim2Real) paradigm is a crucial step for scaling the
learning of embodied models. This paper introduces project GRUtopia, the first
simulated interactive 3D society designed for various robots. It features
several advancements: (a) The scene dataset, GRScenes, includes 100k
interactive, finely annotated scenes, which can be freely combined into
city-scale environments. In contrast to previous works mainly focusing on home,
GRScenes covers 89 diverse scene categories, bridging the gap of
service-oriented environments where general robots would be initially deployed.
(b) GRResidents, a Large Language Model (LLM) driven Non-Player Character (NPC)
system that is responsible for social interaction, task generation, and task
assignment, thus simulating social scenarios for embodied AI applications. (c)
The benchmark, GRBench, supports various robots but focuses on legged robots as
primary agents and poses moderately challenging tasks involving Object
Loco-Navigation, Social Loco-Navigation, and Loco-Manipulation. We hope that
this work can alleviate the scarcity of high-quality data in this field and
provide a more comprehensive assessment of Embodied AI research. The project is
available at https://github.com/OpenRobotLab/GRUtopia.