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Repo symbol

discoverse repository

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/tatp-233/discoverse.git
VCS Type git
VCS Version main
Last Updated 2026-02-24
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

No packages found.

README

DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments

[![Paper](https://img.shields.io/badge/Paper-arXiv-red.svg)](https://arxiv.org/abs/2507.21981) [![Website](https://img.shields.io/badge/Website-DISCOVERSE-blue.svg)](https://air-discoverse.github.io/) [![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE) [![Python](https://img.shields.io/badge/Python-3.8%2B-blue.svg)](https://www.python.org/) [![Docker](https://img.shields.io/badge/Docker-Available-blue.svg)](#docker-quick-start) https://github.com/user-attachments/assets/78893813-d3fd-48a1-8bb4-5b0d87bf900f *A unified, modular, open-source 3DGS-based simulation framework for Real2Sim2Real robot learning*

中文文档

🎉 DISCOVERSE Accepted by IROS 2025!

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
  1. 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

File truncated at 100 lines see the full file

CONTRIBUTING

No version for distro jazzy showing github. Known supported distros are highlighted in the buttons above.
Repo symbol

discoverse repository

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/tatp-233/discoverse.git
VCS Type git
VCS Version main
Last Updated 2026-02-24
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

No packages found.

README

DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments

[![Paper](https://img.shields.io/badge/Paper-arXiv-red.svg)](https://arxiv.org/abs/2507.21981) [![Website](https://img.shields.io/badge/Website-DISCOVERSE-blue.svg)](https://air-discoverse.github.io/) [![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE) [![Python](https://img.shields.io/badge/Python-3.8%2B-blue.svg)](https://www.python.org/) [![Docker](https://img.shields.io/badge/Docker-Available-blue.svg)](#docker-quick-start) https://github.com/user-attachments/assets/78893813-d3fd-48a1-8bb4-5b0d87bf900f *A unified, modular, open-source 3DGS-based simulation framework for Real2Sim2Real robot learning*

中文文档

🎉 DISCOVERSE Accepted by IROS 2025!

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
  1. 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

File truncated at 100 lines see the full file

CONTRIBUTING

No version for distro kilted showing github. Known supported distros are highlighted in the buttons above.
Repo symbol

discoverse repository

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/tatp-233/discoverse.git
VCS Type git
VCS Version main
Last Updated 2026-02-24
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

No packages found.

README

DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments

[![Paper](https://img.shields.io/badge/Paper-arXiv-red.svg)](https://arxiv.org/abs/2507.21981) [![Website](https://img.shields.io/badge/Website-DISCOVERSE-blue.svg)](https://air-discoverse.github.io/) [![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE) [![Python](https://img.shields.io/badge/Python-3.8%2B-blue.svg)](https://www.python.org/) [![Docker](https://img.shields.io/badge/Docker-Available-blue.svg)](#docker-quick-start) https://github.com/user-attachments/assets/78893813-d3fd-48a1-8bb4-5b0d87bf900f *A unified, modular, open-source 3DGS-based simulation framework for Real2Sim2Real robot learning*

中文文档

🎉 DISCOVERSE Accepted by IROS 2025!

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
  1. 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

File truncated at 100 lines see the full file

CONTRIBUTING

No version for distro rolling showing github. Known supported distros are highlighted in the buttons above.
Repo symbol

discoverse repository

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/tatp-233/discoverse.git
VCS Type git
VCS Version main
Last Updated 2026-02-24
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

No packages found.

README

DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments

[![Paper](https://img.shields.io/badge/Paper-arXiv-red.svg)](https://arxiv.org/abs/2507.21981) [![Website](https://img.shields.io/badge/Website-DISCOVERSE-blue.svg)](https://air-discoverse.github.io/) [![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE) [![Python](https://img.shields.io/badge/Python-3.8%2B-blue.svg)](https://www.python.org/) [![Docker](https://img.shields.io/badge/Docker-Available-blue.svg)](#docker-quick-start) https://github.com/user-attachments/assets/78893813-d3fd-48a1-8bb4-5b0d87bf900f *A unified, modular, open-source 3DGS-based simulation framework for Real2Sim2Real robot learning*

中文文档

🎉 DISCOVERSE Accepted by IROS 2025!

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
  1. 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

File truncated at 100 lines see the full file

CONTRIBUTING

Repo symbol

discoverse repository

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/tatp-233/discoverse.git
VCS Type git
VCS Version main
Last Updated 2026-02-24
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

No packages found.

README

DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments

[![Paper](https://img.shields.io/badge/Paper-arXiv-red.svg)](https://arxiv.org/abs/2507.21981) [![Website](https://img.shields.io/badge/Website-DISCOVERSE-blue.svg)](https://air-discoverse.github.io/) [![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE) [![Python](https://img.shields.io/badge/Python-3.8%2B-blue.svg)](https://www.python.org/) [![Docker](https://img.shields.io/badge/Docker-Available-blue.svg)](#docker-quick-start) https://github.com/user-attachments/assets/78893813-d3fd-48a1-8bb4-5b0d87bf900f *A unified, modular, open-source 3DGS-based simulation framework for Real2Sim2Real robot learning*

中文文档

🎉 DISCOVERSE Accepted by IROS 2025!

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
  1. 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

File truncated at 100 lines see the full file

CONTRIBUTING

No version for distro galactic showing github. Known supported distros are highlighted in the buttons above.
Repo symbol

discoverse repository

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/tatp-233/discoverse.git
VCS Type git
VCS Version main
Last Updated 2026-02-24
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

No packages found.

README

DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments

[![Paper](https://img.shields.io/badge/Paper-arXiv-red.svg)](https://arxiv.org/abs/2507.21981) [![Website](https://img.shields.io/badge/Website-DISCOVERSE-blue.svg)](https://air-discoverse.github.io/) [![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE) [![Python](https://img.shields.io/badge/Python-3.8%2B-blue.svg)](https://www.python.org/) [![Docker](https://img.shields.io/badge/Docker-Available-blue.svg)](#docker-quick-start) https://github.com/user-attachments/assets/78893813-d3fd-48a1-8bb4-5b0d87bf900f *A unified, modular, open-source 3DGS-based simulation framework for Real2Sim2Real robot learning*

中文文档

🎉 DISCOVERSE Accepted by IROS 2025!

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
  1. 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

File truncated at 100 lines see the full file

CONTRIBUTING

No version for distro iron showing github. Known supported distros are highlighted in the buttons above.
Repo symbol

discoverse repository

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/tatp-233/discoverse.git
VCS Type git
VCS Version main
Last Updated 2026-02-24
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

No packages found.

README

DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments

[![Paper](https://img.shields.io/badge/Paper-arXiv-red.svg)](https://arxiv.org/abs/2507.21981) [![Website](https://img.shields.io/badge/Website-DISCOVERSE-blue.svg)](https://air-discoverse.github.io/) [![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE) [![Python](https://img.shields.io/badge/Python-3.8%2B-blue.svg)](https://www.python.org/) [![Docker](https://img.shields.io/badge/Docker-Available-blue.svg)](#docker-quick-start) https://github.com/user-attachments/assets/78893813-d3fd-48a1-8bb4-5b0d87bf900f *A unified, modular, open-source 3DGS-based simulation framework for Real2Sim2Real robot learning*

中文文档

🎉 DISCOVERSE Accepted by IROS 2025!

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
  1. 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

File truncated at 100 lines see the full file

CONTRIBUTING

No version for distro melodic showing github. Known supported distros are highlighted in the buttons above.
Repo symbol

discoverse repository

ROS Distro
github

Repository Summary

Description
Checkout URI https://github.com/tatp-233/discoverse.git
VCS Type git
VCS Version main
Last Updated 2026-02-24
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

No packages found.

README

DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments

[![Paper](https://img.shields.io/badge/Paper-arXiv-red.svg)](https://arxiv.org/abs/2507.21981) [![Website](https://img.shields.io/badge/Website-DISCOVERSE-blue.svg)](https://air-discoverse.github.io/) [![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE) [![Python](https://img.shields.io/badge/Python-3.8%2B-blue.svg)](https://www.python.org/) [![Docker](https://img.shields.io/badge/Docker-Available-blue.svg)](#docker-quick-start) https://github.com/user-attachments/assets/78893813-d3fd-48a1-8bb4-5b0d87bf900f *A unified, modular, open-source 3DGS-based simulation framework for Real2Sim2Real robot learning*

中文文档

🎉 DISCOVERSE Accepted by IROS 2025!

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
  1. 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

File truncated at 100 lines see the full file

CONTRIBUTING

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discoverse repository

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Repository Summary

Description
Checkout URI https://github.com/tatp-233/discoverse.git
VCS Type git
VCS Version main
Last Updated 2026-02-24
Dev Status UNKNOWN
Released UNRELEASED
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Packages

No packages found.

README

DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments

[![Paper](https://img.shields.io/badge/Paper-arXiv-red.svg)](https://arxiv.org/abs/2507.21981) [![Website](https://img.shields.io/badge/Website-DISCOVERSE-blue.svg)](https://air-discoverse.github.io/) [![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE) [![Python](https://img.shields.io/badge/Python-3.8%2B-blue.svg)](https://www.python.org/) [![Docker](https://img.shields.io/badge/Docker-Available-blue.svg)](#docker-quick-start) https://github.com/user-attachments/assets/78893813-d3fd-48a1-8bb4-5b0d87bf900f *A unified, modular, open-source 3DGS-based simulation framework for Real2Sim2Real robot learning*

中文文档

🎉 DISCOVERSE Accepted by IROS 2025!

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
  1. 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

File truncated at 100 lines see the full file

CONTRIBUTING