DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918107237318656 |
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| author | Jia, Yufei Wang, Guangyu Dong, Yuhang Wu, Junzhe Zeng, Yupei Lin, Haonan Wang, Zifan Ge, Haizhou Gu, Weibin Ding, Kairui Yan, Zike Cheng, Yunjie Li, Yue Wang, Ziming Li, Chuxuan Sui, Wei Shi, Lu Tian, Guanzhong Huang, Ruqi Zhou, Guyue |
| author_facet | Jia, Yufei Wang, Guangyu Dong, Yuhang Wu, Junzhe Zeng, Yupei Lin, Haonan Wang, Zifan Ge, Haizhou Gu, Weibin Ding, Kairui Yan, Zike Cheng, Yunjie Li, Yue Wang, Ziming Li, Chuxuan Sui, Wei Shi, Lu Tian, Guanzhong Huang, Ruqi Zhou, Guyue |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21981 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments Jia, Yufei Wang, Guangyu Dong, Yuhang Wu, Junzhe Zeng, Yupei Lin, Haonan Wang, Zifan Ge, Haizhou Gu, Weibin Ding, Kairui Yan, Zike Cheng, Yunjie Li, Yue Wang, Ziming Li, Chuxuan Sui, Wei Shi, Lu Tian, Guanzhong Huang, Ruqi Zhou, Guyue Robotics 68T40 I.2.9 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/. |
| title | DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments |
| topic | Robotics 68T40 I.2.9 |
| url | https://arxiv.org/abs/2507.21981 |