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Our paper "DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments" has been accepted by IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2025.

1. Clone repository

# Install Git LFS (if not already installed)
## Linux
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
sudo apt-get install git-lfs

## macOS using Homebrew
brew install git-lfs

git clone https://github.com/TATP-233/DISCOVERSE.git
cd DISCOVERSE

2. Choose installation method

conda create -n discoverse python=3.10 # >=3.8 is ok
conda activate discoverse
pip install -e .

## Auto-detect and download required submodules
python scripts/setup_submodules.py

## Verify installation
python scripts/check_installation.py

pip install -e .  # Core functionality only

Includes: MuJoCo, OpenCV, NumPy and other basic dependencies

pip install -e ".[lidar,visualization]"

• Includes: Taichi GPU acceleration, LiDAR simulation, visualization tools
• Function: High-performance LiDAR simulation with Taichi GPU acceleration
• Dependencies: taichi>=1.6.0
• Use Cases: Mobile robot SLAM, LiDAR sensor simulation, point cloud processing

pip install -e ".[act_full]"

• Includes: ACT algorithm, data collection tools, visualization
• Function: Imitation learning, robot skill training, policy optimization
• Dependencies: torch, einops, h5py, transformers, wandb
• Algorithms: Other algorithms available with [diffusion-policy] and [rdt]

pip install -e ".[gaussian-rendering]"

• Includes: 3D Gaussian Splatting, PyTorch
• Function: Photorealistic 3D scene rendering with real-time lighting
• Dependencies: torch>=2.0.0, torchvision>=0.14.0, plyfile, PyGlm
• Use Cases: High-fidelity visual simulation, 3D scene reconstruction, Real2Sim pipeline

We provide Docker installation method.

# Set up repository
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
    && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
    && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

# Update and install
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit nvidia-docker2

# Restart Docker service
sudo systemctl restart docker

• Download pre-built Docker image

  Baidu Netdisk: https://pan.baidu.com/s/1mLC3Hz-m78Y6qFhurwb8VQ?pwd=xmp9

  Currently updated to v1.8.6. After downloading the .tar file, use the docker load command to load the docker image.

  Replace discoverse_tag.tar below with the actual downloaded image tar file name.

  docker load < discoverse_tag.tar

• Or build from Dockerfile

  git clone https://github.com/TATP-233/DISCOVERSE.git
  cd DISCOVERSE
  python scripts/setup_submodules.py --module gaussian-rendering
  docker build -f docker/Dockerfile -t discoverse:latest .

  Dockerfile.vnc is a configuration version that supports VNC remote access. It adds VNC server support on top of docker/Dockerfile, allowing you to remotely access the container's graphical interface through a VNC client. This is particularly useful for remote development or environments without a local display server. If needed, change docker build -f docker/Dockerfile -t discoverse:latest . to docker build -f docker/Dockerfile.vnc -t discoverse:latest .

# Run with GPU support
docker run -dit --rm --name discoverse \
    --gpus all \
    -e DISPLAY=$DISPLAY \
    -v /tmp/.X11-unix:/tmp/.X11-unix \
    discoverse:latest
# Note: Replace `latest` with the actual docker image tag (e.g., v1.8.6).

# Set visualization window permissions
xhost +local:docker

# Enter container terminal
docker exec -it discoverse bash

# Test run
python3 discoverse/examples/active_slam/camera_view.py

For high-fidelity 3DGS rendering functionality, if you don't need high-fidelity rendering or are using docker installation, you can skip this section.

Install CUDA 11.8+ from NVIDIA's official site, choose the corresponding CUDA version based on your graphics card driver.

# Install Gaussian Splatting requirements
pip install -e ".[gaussian-rendering]"

# Build diff-gaussian-rasterization
cd submodules/diff-gaussian-rasterization/

# Apply patches
sed -i 's/(p_view.z <= 0.2f)/(p_view.z <= 0.01f)/' cuda_rasterizer/auxiliary.h
sed -i '361s/D += depths\[collected_id\[j\]\] \* alpha \* T;/if (depths[collected_id[j]] < 50.0f)\n        D += depths[collected_id[j]] * alpha * T;/' cuda_rasterizer/forward.cu

# Install
cd ../..
pip install submodules/diff-gaussian-rasterization

• Baidu Netdisk
• Tsinghua Cloud

.ply model files are large, choose only the models you need.

Place in the models/3dgs directory as follows:

models/
├── meshes/          # Mesh geometries
├── textures/        # Material textures  
├── 3dgs/           # Gaussian Splatting models
│   ├── airbot_play/
│   ├── mmk2/
│   ├── objects/
│   ├── scenes/
│   └── ......
├── mjcf/           # MuJoCo scene descriptions
└── urdf/           # Robot descriptions

View and edit 3DGS models online using SuperSplat - simply drag and drop .ply files.

DISCOVERSE features a comprehensive Real2Sim pipeline for creating digital twins of real environments. For detailed instructions, visit our Real2Sim repository.

# Launch Airbot Play / MMK2
python discoverse/robots_env/airbot_play_base.py
python discoverse/robots_env/mmk2_base.py

# Run manipulation tasks (automated data generation)
python discoverse/examples/tasks_airbot_play/place_coffeecup.py
python discoverse/examples/tasks_mmk2/kiwi_pick.py

# Tactile hand leaphand
python discoverse/examples/robots/leap_hand_env.py

# Inverse kinematics
python discoverse/examples/mocap_ik/mocap_ik_airbot_play.py # optional [--mjcf mjcf/tasks_airbot_play/stack_block.xml]
python discoverse/examples/mocap_ik/mocap_ik_mmk2.py # optional [--mjcf mjcf/tasks_mmk2/pan_pick.xml]

• 'h' - Show help menu
• 'F5' - Reload MJCF scene
• 'r' - Reset simulation state
• '['/'']' - Switch camera views
• 'Esc' - Toggle free camera mode
• 'p' - Print robot state information
• 'Ctrl+g' - Toggle Gaussian rendering (requires gaussian-splatting installation and set cfg.use_gaussian_renderer = False)
• 'Ctrl+d' - Toggle depth visualization

DISCOVERSE provides complete workflows for data collection, training, and inference:

1. Data Collection: Guide
2. Model Training: Guide
3. Policy Inference: Guide

• ACT
• Diffusion Policy
• RDT
• Custom algorithms via extensible framework

• 2025.01.13: 🎉 DISCOVERSE open source release
• 2025.01.16: 🐳 Docker support added
• 2025.01.14: 🏁 S2R2025 Competition launched
• 2025.02.17: 📈 Diffusion Policy baseline integration
• 2025.02.19: 📡 Point cloud sensor support added

Join our WeChat community for updates and discussions

For installation and runtime issues, please refer to our comprehensive Troubleshooting Guide.

DISCOVERSE is released under the MIT License. See the license file for details.

If you find DISCOVERSE helpful in your research, please consider citing our work:

@article{jia2025discoverse,
      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 Chuxuan Li and Ziming Wang and Yunjie Cheng and Wei Sui and Ruqi Huang and Guyue Zhou},
      journal={arXiv preprint arXiv:2507.21981},
      year={2025},
      url={https://arxiv.org/abs/2507.21981}
}