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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.

📦 Installation & Quick Start

Quick Start

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

Installation by Use Case

Scenario 1: Learning Robot Simulation Basics

pip install -e .  # Core functionality only

Includes: MuJoCo, OpenCV, NumPy and other basic dependencies

Scenario 2: LiDAR SLAM

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

Scenario 3: Robotic Arm Imitation Learning

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: Additional algorithms available: [diffusion-policy] and [rdt]

Scenario 4: High-Fidelity Visual Simulation

pip install -e ".[gs]"

• Includes: 3D Gaussian Splatting, PyTorch
• Function: Photorealistic 3D scene rendering with real-time lighting
• Dependencies: gaussian_renderer
• Use Cases: High-fidelity visual simulation, 3D scene reconstruction, Real2Sim pipeline

Module Feature Overview

Module	Install Command	Function	Use Cases
Core	pip install -e .	Core simulation	Learning, basic development
LiDAR	.[lidar]	High-performance LiDAR simulation	SLAM, navigation research
Rendering	.[gs]	3D Gaussian Splatting rendering	Visual simulation, Real2Sim
GUI	.[xml-editor]	Visual scene editing	Scene design, model debugging
ACT	.[act]	Imitation learning algorithm	Robot skill learning
Diffusion Policy	.[diffusion-policy]	Diffusion model policy	Complex policy learning
RDT	.[rdt]	Large model policy	General robot skills
Hardware Integration	.[hardware]	RealSense+ROS	Real robot control

Docker Quick Start

We provide a Docker installation method.

1. Install NVIDIA Container Toolkit:

# 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

2. Build Docker Image

• 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 discoverse/docker/Dockerfile -t discoverse:latest .
  

  Dockerfile.vnc is a configuration that supports VNC remote access. It adds VNC server support to discoverse/docker/Dockerfile, allowing remote access to the container's GUI via a VNC client. This is useful for remote development or headless environments. To use it, replace docker build -f discoverse/docker/Dockerfile ... with docker build -f discoverse/docker/Dockerfile.vnc ....

3. Create Docker Container

# 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 examples/active_slam/camera_view.py

📷 High-Fidelity Rendering Setup

This section covers the setup for high-fidelity 3DGS rendering. If you do not require this feature or are using Docker, you can skip this section.

1. CUDA Installation

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

2. 3DGS Dependencies

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

3. Download 3DGS Models

PLY models will be automatically downloaded from Hugging Face when you first run a simulation that requires them. Login to Hugging Face with: hf auth login

Models are stored in the models/3dgs directory:

models/
├── meshes/          # Mesh geometries
├── textures/        # Material textures  
├── 3dgs/           # Gaussian Splatting models (auto-downloaded)
│   ├── hinge/
│   ├── manipulator/
│   ├── mobile_chassis/
│   ├── objaverse/
│   ├── object/
│   ├── rm2_car/
│   ├── scene/
│   └── skyrover/
├── mjcf/           # MuJoCo scene descriptions
└── urdf/           # Robot descriptions

For users in China, the automatic download uses HF-Mirror for faster speeds.

4. Model Visualization

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

🔨 Real2Sim Pipeline

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

💡 Usage Examples

Basic Robot Simulation

# 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 examples/tasks_airbot_play/place_coffeecup.py
python examples/tasks_mmk2/kiwi_pick.py

# Tactile hand Leap Hand
python examples/robots/leap_hand_env.py

# Inverse Kinematics
python examples/mocap_ik/mocap_ik_manipulator.py # optional [--robot airbot_play --mjcf mjcf/task_environments/stack_block.xml]
python examples/mocap_ik/mocap_ik_mmk2.py # optional [--mjcf mjcf/tasks_mmk2/pan_pick.xml]

https://github.com/user-attachments/assets/6d80119a-31e1-4ddf-9af5-ee28e949ea81

Multiple Robot Models and Task Scenarios

• '-h, --help' - Print the help messages
• '-m MJCF, --mjcf MJCF' - Path to the MJCF file. Defaults to 'robot_airbot_play.xml' if not provided.
• '-r ROBOT, --robot ROBOT' - Select a ROBOT. Available Robots: {airbot_play, airbot_play_force, arx_l5, arx_x5, iiwa14, panda, piper, rm65, ur5e, xarm7}
• '-t TASK, --task TASK' - Select a TASK. Available Tasks: {block_bridge_place, close_laptop, cover_cup, open_drawer, peg_in_hole, pick_jujube, place_block, place_coffeecup, place_jujube, place_jujube_coffeecup, place_kiwi_fruit, push_mouse, stack_block}
• '-y' - For macOS: Bypass mjpython prompt and launch viewer directly
• '--mouse-3d' - Enable 3D Mouse for arm control (requires 3D mouse hardware)
• '--hide-mocap' - Hide mocap target
• '--record' - Enable Recording
• '--record-frequency RECORD_FREQUENCY' - Record requency (Hz)
• '--camera-names [CAMERA_NAMES]' - Specify the list of camera names to render (optional)
• '--inference' - Enable inference mode
• '--infer-hz INFER_HZ' - Set inference frequency
• '--plot' - Enable plot

# Robot model: arx_l5, task: block_bridge_place
python3 examples/mocap_ik/mocap_ik_manipulator.py -r arx_l5 -t block_bridge_place

Interactive Controls

• '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 cfg.use_gaussian_renderer = True)
• 'Ctrl+d' - Toggle depth visualization

🎓 Learning & Training

Imitation Learning Quick Start

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

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

Supported Algorithms

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

⏩ Recent Updates

• 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

❔ Troubleshooting

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

⚖️ License

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

📜 Citation

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}
}