DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments

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/.

Authors

• Yufei Jia
• Guangyu Wang
• Yuhang Dong
• Junzhe Wu
• Yupei Zeng
• Haonan Lin
• Zifan Wang
• Haizhou Ge
• Weibin Gu
• Kairui Ding
• Zike Yan
• Yunjie Cheng
• Yue Li
• Ziming Wang
• Chuxuan Li
• Wei Sui
• Lu Shi
• Guanzhong Tian
• Ruqi Huang
• Guyue Zhou

Keywords

• Geometry
• Solid modeling
• Three-dimensional displays
• Codes
• Imitation learning
• Pipelines
• Robot sensing systems
• Robot learning
• Physics
• Intelligent robots
• Efficient Simulation
• Robot Model
• Open-source Framework
• Open-source Simulation
• Sensor Modalities
• Interactive
• Actuator
• 3D Reconstruction
• Data Augmentation
• Domain Shift
• Robotic Arm
• Tactile Sensor
• Seamless Integration
• Real-world Tasks
• Physical Simulation
• Physics Engine
• Real-world Scenes
• Robot Operating System
• Average Success Rate
• Precise Geometry
• Modular Framework
• Mobile Manipulator
• Exemplary Application
• Game Engine
• Multi-view Stereo