LabUtopia: High-Fidelity Simulation and Hierarchical Benchmark for Scientific Embodied Agents
Rui Li, Zixuan Hu, Wenxi Qu, Jinouwen Zhang, Zhenfei Yin, Sha Zhang, Xuantuo Huang, Hanqing Wang
Abstract
Scientific embodied agents play a crucial role in modern laboratories by automating complex experimental workflows. Compared to typical household environments, laboratory settings impose significantly higher demands on perception of physical-chemical transformations and long-horizon planning, making them an ideal testbed for advancing embodied intelligence. However, its development has been long hampered by the lack of suitable simulator and benchmarks. In this paper, we address this gap by introducing LabUtopia, a comprehensive simulation and benchmarking suite designed to facilitate the development of generalizable, reasoning-capable embodied agents in laboratory settings. Specifically, it integrates i) LabSim, a high-fidelity simulator supporting multi-physics and chemically meaningful interactions; ii) LabScene, a scalable procedural generator for diverse scientific scenes; and iii) LabBench, a hierarchical benchmark spanning five levels of complexity from atomic actions to long-horizon mobile manipulation. LabUtopia supports 30 distinct tasks and includes more than 200 scene and instrument assets, enabling large-scale training and principled evaluation in high-complexity environments. We demonstrate that LabUtopia offers a powerful platform for advancing the integration of perception, planning, and control in scientific-purpose agents and provides a rigorous testbed for exploring the practical capabilities and generalization limits of embodied intelligence in future research. Project web page: https://rui-li023.github.io/labutopia-site/
BibTeX
@inproceedings{
li2025labutopia,
title={LabUtopia: High-Fidelity Simulation and Hierarchical Benchmark for Scientific Embodied Agents},
author={Rui Li and Zixuan Hu and Wenxi Qu and Jinouwen Zhang and Zhenfei Yin and Sha Zhang and Xuantuo Huang and Hanqing Wang and Tai Wang and Jiangmiao Pang and Wanli Ouyang and LEI BAI and Wangmeng Zuo and LINGYU DUAN and Dongzhan Zhou and SHIXIANG TANG},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2025},
url={https://openreview.net/forum?id=AIOq1vWSgK}
}