Robotics: Science and Systems XXI

RoboVerse: A Unified Platform, Benchmark and Dataset for Scalable and Generalizable Robot Learning

Haoran Geng, Feishi Wang, Songlin Wei, Yuyang Li, Bangjun Wang, Boshi An, Haozhe Lou, Charlie Tianyue Cheng, Peihao Li, Haozhe Chen, Yutong Liang, Yuxi Qian, Jiageng Mao, Weikang Wan, Yiran Geng, Mingtong Zhang, Jiangran Lyu, Siheng Zhao, Jiazhao Zhang, Chaoyi Xu, Jialiang Zhang, Chengyang Zhao, Haoran Lu, Yufei Ding, Ran Gong, Yuran Wang, Yuxuan Kuang, Ruihai Wu, Baoxiong Jia, Hao Dong, Siyuan Huang, Yue Wang, Jitendra Malik, Pieter Abbeel

Abstract:

Data scaling and standardized evaluation benchmarks have driven significant advances in natural language processing and computer vision. However, robotics faces unique challenges in scaling data and establishing reliable evaluation protocols. Collecting real-world robotic data is resource-intensive and inefficient, while benchmarking in real-world scenarios remains highly complex. Synthetic data and simulation offer promising alternatives, yet existing efforts often fall short in data quality, diversity, and benchmark standardization. To address these challenges, we introduce RoboVerse, a comprehensive framework comprising a simulation platform, a synthetic dataset, and unified benchmarks. Our simulation platform supports multiple simulators and robotic embodiments, enabling seamless transitions between different environments. The synthetic dataset, featuring high-fidelity physics and photorealistic rendering, is constructed through multiple approaches including migration from public datasets, policy rollout, and motion planning, etc., enhanced by data augmentation. Additionally, we propose unified benchmarks for imitation learning and reinforcement learning, enabling consistent evaluation across different levels of generalization. At the core of the simulation platform is MetaSim, an infrastructure that abstracts diverse simulation environments into a universal interface. It restructures existing simulation environments into a simulator-agnostic configuration system, as well as an API aligning different simulator functionalities, such as launching simulation environments, loading assets with initial states, stepping the physics engine, etc. This abstraction ensures interoperability and extensibility. Comprehensive experiments demonstrate that RoboVerse enhances the performance of imitation learning, reinforcement learning, and world model learning, improving sim-to-real transfer. These results validate the reliability of our dataset and benchmarks, establishing RoboVerse as a robust solution for advancing simulation-assisted robot learning. Code and dataset can be found at https://roboverseorg.github.io/.

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Bibtex:

  
@INPROCEEDINGS{GengH-RSS-25, 
    AUTHOR    = {Haoran Geng AND Feishi Wang AND Songlin Wei AND Yuyang Li AND Bangjun Wang AND Boshi An AND Haozhe Lou AND Charlie Tianyue Cheng AND Peihao Li AND Haozhe Chen AND Yutong Liang AND Yuxi Qian AND Jiageng Mao AND Weikang Wan AND Yiran Geng AND Mingtong Zhang AND Jiangran Lyu AND Siheng Zhao AND Jiazhao Zhang AND Chaoyi Xu AND Jialiang Zhang AND Chengyang Zhao AND Haoran Lu AND Yufei Ding AND Ran Gong AND Yuran Wang AND Yuxuan Kuang AND Ruihai Wu AND Baoxiong Jia AND Hao Dong AND Siyuan Huang AND Yue Wang AND Jitendra Malik AND Pieter Abbeel}, 
    TITLE     = {{RoboVerse: A Unified Platform, Benchmark and Dataset for Scalable and Generalizable Robot Learning}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2025}, 
    ADDRESS   = {LosAngeles, CA, USA}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2025.XXI.022} 
}