IROS Conference 2025 Conference Paper
DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments
- Yufei Jia
- Guangyu Wang
- Yuhang Dong
- Junzhe Wu
- Yupei Zeng
- Haonan Lin
- Zifan Wang
- Haizhou Ge
We present Discoverse, 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, Dis coverse demonstrates state-of-the-art zero-shot Sim2Real transfer performance compared to existing simulators. For code and demos: https://air-discoverse.github.io/.