We present the first unified, modular, open-source 3DGS-based simulation framework for Real2Sim2Real robot learning. It features a holistic Real2Sim pipeline that synthesizes hyper-realistic geometry and appearance of complex real-world scenarios, paving the way for analyzing and bridging the Sim2Real gap. Powered by Gaussian Splatting and MuJoCo, Discoverse enables massively parallel simulation of multiple sensor modalities and accurate physics, with inclusive supports for existing 3D assets, robot models, and ROS plugins, empowering large-scale robot learning and complex robotic benchmarks. Through extensive experiments on imitation learning, Discoverse demonstrates state-of-the-art zero-shot Sim2Real transfer performance compared to existing simulators. For code and demos: https://air-discoverse.github.io/.
@article{arxiv.2507.21981,
title = {DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments},
author = {Yufei Jia and Guangyu Wang and Yuhang Dong and Junzhe Wu and Yupei Zeng and Haonan Lin and Zifan Wang and Haizhou Ge and Weibin Gu and Kairui Ding and Zike Yan and Yunjie Cheng and Yue Li and Ziming Wang and Chuxuan Li and Wei Sui and Lu Shi and Guanzhong Tian and Ruqi Huang and Guyue Zhou},
journal= {arXiv preprint arXiv:2507.21981},
year = {2025}
}