GRUtopia: Dream General Robots in a City at Scale
Abstract
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.
Keywords
Cite
@article{arxiv.2407.10943,
title = {GRUtopia: Dream General Robots in a City at Scale},
author = {Hanqing Wang and Jiahe Chen and Wensi Huang and Qingwei Ben and Tai Wang and Boyu Mi and Tao Huang and Siheng Zhao and Yilun Chen and Sizhe Yang and Peizhou Cao and Wenye Yu and Zichao Ye and Jialun Li and Junfeng Long and Zirui Wang and Huiling Wang and Ying Zhao and Zhongying Tu and Yu Qiao and Dahua Lin and Jiangmiao Pang},
journal= {arXiv preprint arXiv:2407.10943},
year = {2024}
}