GRUtopia: Dream General Robots in a City at Scale
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.
669606b85e5e72a43406f888
Hanqing Wang
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2025-07-16T15:07:56.537Z
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hanqingwang
user
hanqing94
669606b85e5e72a43406f889
Jiahe Chen
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Wensi Huang
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Qingwei Ben
669606b85e5e72a43406f88c
Tai Wang
669606b85e5e72a43406f88d
Boyu Mi
669606b85e5e72a43406f88e
Tao Huang
669606b85e5e72a43406f88f
Siheng Zhao
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Yilun Chen
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Sizhe Yang
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Peizhou Cao
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Wenye Yu
669606b85e5e72a43406f894
Zichao Ye
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Jialun Li
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Junfeng Long
669606b85e5e72a43406f897
Zirui Wang
669606b85e5e72a43406f898
Huiling Wang
669606b85e5e72a43406f899
Ying Zhao
669606b85e5e72a43406f89a
Zhongying Tu
669606b85e5e72a43406f89b
Yu Qiao
669606b85e5e72a43406f89c
Dahua Lin
669606b85e5e72a43406f89d
Jiangmiao Pang
GRUtopia: Dream General Robots in a City at Scale
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.
Project GRUtopia introduces GRScenes, GRResidents, and GRBench to simulate a comprehensive 3D interactive society for embodied AI, focusing on diverse environments and advanced robotic tasks.
Sim2Real Embodied AI interactive 3D society GRScenes scene dataset Large Language Model (LLM) Non-Player Character (NPC) GRResidents GRBench legged robots Object Loco-Navigation Social Loco-Navigation Loco-Manipulation