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airbot_msgs

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

[![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: 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

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discoverse repository

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discoverse repository

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discoverse repository