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Repository Summary
Description | |
Checkout URI | https://github.com/tatp-233/discoverse.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-07-31 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Packages
Name | Version |
---|---|
airbot_msgs | 0.0.0 |
README
DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments
[](https://arxiv.org/abs/2507.21981)
[](https://air-discoverse.github.io/)
[](LICENSE)
[](https://www.python.org/)
[](#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*
🎉 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
- 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
- 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: Other algorithms available with [diffusion-policy] and [rdt]
Scenario 4: High-Fidelity Visual Simulation
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
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 |
File truncated at 100 lines see the full file
CONTRIBUTING
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