用3DGS构建高保真机器人仿真，实现真实世界到虚拟世界的无缝迁移。
DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments
- 基于3DGS与MuJoCo的模块化框架，支持多传感器并行仿真。
- 在模仿学习中实现零样本跨域迁移的顶尖性能。
- 开源且兼容现有3D资产与ROS插件，适合复杂机器人任务研究。
我们提出首个统一、模块化、开源的基于3DGS的机器人仿真框架DISCOVERSE，用于实现真实世界到虚拟世界再到真实世界的机器人学习。该框架构建了完整的Real2Sim流程，能够合成复杂现实场景的超真实几何与视觉外观，为分析和弥合Sim2Real差距提供新路径。依托高斯泼溅（Gaussian Splatting）与MuJoCo，DISCOVERSE支持多种传感器模态的大规模并行仿真与精确物理计算，兼容现有3D资产、机器人模型及ROS插件，赋能大规模机器人学习与复杂机器人基准测试。在模仿学习的大量实验中，相较于现有模拟器，DISCOVERSE展现出业界领先的零样本Sim2Real迁移性能。代码与演示见：https://air-discoverse.github.io/。
原文摘要 · Abstract (English)
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/.
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