README.md

![Genesis](imgs/big_text.png)

![Teaser](imgs/teaser.png)

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# Genesis

## Table of Contents

1. [What is Genesis?](#what-is-genesis)
2. [Key Features](#key-features)
3. [Quick Installation](#quick-installation)
4. [Documentation](#documentation)
5. [Example Usage](#example-usage)
6. [Contributing to Genesis](#contributing-to-genesis)
7. [Support](#support)
8. [License and Acknowledgments](#license-and-acknowledgments)
9. [Associated Papers](#associated-papers)
10. [Citation](#citation)

## What is Genesis?

Genesis is a physics platform designed for general-purpose *Robotics/Embodied AI/Physical AI* applications. It is simultaneously multiple things:

1. A **universal physics engine** re-built from the ground up, capable of simulating a wide range of materials and physical phenomena.
2. A **lightweight**, **ultra-fast**, **pythonic**, and **user-friendly** robotics simulation platform.
3. A powerful and fast **photo-realistic rendering system**.
4. A **generative data engine** that transforms user-prompted natural language description into various modalities of data.

Genesis aims to:

- **Lower the barrier** to using physics simulations, making robotics research accessible to everyone. See our [mission statement](https://genesis-world.readthedocs.io/en/latest/user_guide/overview/mission.html).
- **Unify diverse physics solvers** into a single framework to recreate the physical world with the highest fidelity.
- **Automate data generation**, reducing human effort and letting the data flywheel spin on its own.

Project Page: <https://genesis-embodied-ai.github.io/>

## Key Features

- **Speed**: Over 43 million FPS when simulating a Franka robotic arm with a single RTX 4090 (430,000 times faster than real-time).
- **Cross-platform**: Runs on Linux, macOS, Windows, and supports multiple compute backends (CPU, Nvidia/AMD GPUs, Apple Metal).
- **Integration of diverse physics solvers**: Rigid body, MPM, SPH, FEM, PBD, Stable Fluid.
- **Wide range of material models**: Simulation and coupling of rigid bodies, liquids, gases, deformable objects, thin-shell objects, and granular materials.
- **Compatibility with various robots**: Robotic arms, legged robots, drones, *soft robots*, and support for loading `MJCF (.xml)`, `URDF`, `.obj`, `.glb`, `.ply`, `.stl`, and more.
- **Photo-realistic rendering**: Native ray-tracing-based rendering.
- **Differentiability**: Genesis is designed to be fully differentiable. Currently, our MPM solver and Tool Solver support differentiability, with other solvers planned for future versions (starting with rigid & articulated body solver).
- **Physics-based tactile simulation**: Differentiable [tactile sensor simulation](https://github.com/Genesis-Embodied-AI/DiffTactile) coming soon (expected in version 0.3.0).
- **User-friendliness**: Designed for simplicity, with intuitive installation and APIs.

## Quick Installation

Genesis is available via PyPI:

```bash
pip install genesis-world  # Requires Python >=3.9;
```

You also need to install **PyTorch** following the [official instructions](https://pytorch.org/get-started/locally/).

For the latest version, clone the repository and install locally:

```bash
git clone https://github.com/Genesis-Embodied-AI/Genesis.git
cd Genesis
pip install -e .
```

## Docker Installation

First build the Docker image:

```bash
docker build -t genesis -f docker/Dockerfile docker
```

And then run the examples inside the docker image (mounted to `/workspace/examples`):

```bash
xhost +local:root # Allow the container to access the display

docker run --gpus all --rm -it \
-e DISPLAY=$DISPLAY \
-v /tmp/.X11-unix/:/tmp/.X11-unix \
-v $PWD:/workspace \
genesis bash
```

## Documentation

Comprehensive documentation is available in [English](https://genesis-world.readthedocs.io/en/latest/user_guide/index.html) and [Chinese](https://genesis-world.readthedocs.io/zh-cn/latest/user_guide/index.html). This includes detailed installation steps, tutorials, and API references.

## Contributing to Genesis

The Genesis project is an open and collaborative effort. We welcome all forms of contributions from the community, including:

- **Pull requests** for new features or bug fixes.
- **Bug reports** through GitHub Issues.
- **Suggestions** to improve Genesis's usability.

Refer to our [contribution guide](https://github.com/Genesis-Embodied-AI/Genesis/blob/main/CONTRIBUTING.md) for more details.

## Support

- Report bugs or request features via GitHub [Issues](https://github.com/Genesis-Embodied-AI/Genesis/issues).
- Join discussions or ask questions on GitHub [Discussions](https://github.com/Genesis-Embodied-AI/Genesis/discussions).

## License and Acknowledgments

The Genesis source code is licensed under Apache 2.0.

Genesis's development has been made possible thanks to these open-source projects:

- [Taichi](https://github.com/taichi-dev/taichi): High-performance cross-platform compute backend. Kudos to the Taichi team for their technical support!
- [FluidLab](https://github.com/zhouxian/FluidLab): Reference MPM solver implementation.
- [SPH_Taichi](https://github.com/erizmr/SPH_Taichi): Reference SPH solver implementation.
- [Ten Minute Physics](https://matthias-research.github.io/pages/tenMinutePhysics/index.html) and [PBF3D](https://github.com/WASD4959/PBF3D): Reference PBD solver implementations.
- [MuJoCo](https://github.com/google-deepmind/mujoco): Reference for rigid body dynamics.
- [libccd](https://github.com/danfis/libccd): Reference for collision detection.
- [PyRender](https://github.com/mmatl/pyrender): Rasterization-based renderer.
- [LuisaCompute](https://github.com/LuisaGroup/LuisaCompute) and [LuisaRender](https://github.com/LuisaGroup/LuisaRender): Ray-tracing DSL.

## Associated Papers

Genesis is a large scale effort that integrates state-of-the-art technologies of various existing and on-going research work into a single system. Here we include a non-exhaustive list of all the papers that contributed to the Genesis project in one way or another:

- Xian, Zhou, et al. "Fluidlab: A differentiable environment for benchmarking complex fluid manipulation." arXiv preprint arXiv:2303.02346 (2023).
- Xu, Zhenjia, et al. "Roboninja: Learning an adaptive cutting policy for multi-material objects." arXiv preprint arXiv:2302.11553 (2023).
- Wang, Yufei, et al. "Robogen: Towards unleashing infinite data for automated robot learning via generative simulation." arXiv preprint arXiv:2311.01455 (2023).
- Wang, Tsun-Hsuan, et al. "Softzoo: A soft robot co-design benchmark for locomotion in diverse environments." arXiv preprint arXiv:2303.09555 (2023).
- Wang, Tsun-Hsuan Johnson, et al. "Diffusebot: Breeding soft robots with physics-augmented generative diffusion models." Advances in Neural Information Processing Systems 36 (2023): 44398-44423.
- Katara, Pushkal, Zhou Xian, and Katerina Fragkiadaki. "Gen2sim: Scaling up robot learning in simulation with generative models." 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024.
- Si, Zilin, et al. "DiffTactile: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation." arXiv preprint arXiv:2403.08716 (2024).
- Wang, Yian, et al. "Thin-Shell Object Manipulations With Differentiable Physics Simulations." arXiv preprint arXiv:2404.00451 (2024).
- Lin, Chunru, et al. "UBSoft: A Simulation Platform for Robotic Skill Learning in Unbounded Soft Environments." arXiv preprint arXiv:2411.12711 (2024).
- Zhou, Wenyang, et al. "EMDM: Efficient motion diffusion model for fast and high-quality motion generation." European Conference on Computer Vision. Springer, Cham, 2025.
- Qiao, Yi-Ling, Junbang Liang, Vladlen Koltun, and Ming C. Lin. "Scalable differentiable physics for learning and control." International Conference on Machine Learning. PMLR, 2020.
- Qiao, Yi-Ling, Junbang Liang, Vladlen Koltun, and Ming C. Lin. "Efficient differentiable simulation of articulated bodies." In International Conference on Machine Learning, PMLR, 2021.
- Qiao, Yi-Ling, Junbang Liang, Vladlen Koltun, and Ming Lin. "Differentiable simulation of soft multi-body systems." Advances in Neural Information Processing Systems 34 (2021).
- Wan, Weilin, et al. "Tlcontrol: Trajectory and language control for human motion synthesis." arXiv preprint arXiv:2311.17135 (2023).
- Wang, Yian, et al. "Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting." arXiv preprint arXiv:2411.09823 (2024).
- Zheng, Shaokun, et al. "LuisaRender: A high-performance rendering framework with layered and unified interfaces on stream architectures." ACM Transactions on Graphics (TOG) 41.6 (2022): 1-19.
- Fan, Yingruo, et al. "Faceformer: Speech-driven 3d facial animation with transformers." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.
- Wu, Sichun, Kazi Injamamul Haque, and Zerrin Yumak. "ProbTalk3D: Non-Deterministic Emotion Controllable Speech-Driven 3D Facial Animation Synthesis Using VQ-VAE." Proceedings of the 17th ACM SIGGRAPH Conference on Motion, Interaction, and Games. 2024.
- Dou, Zhiyang, et al. "C· ase: Learning conditional adversarial skill embeddings for physics-based characters." SIGGRAPH Asia 2023 Conference Papers. 2023.

... and many more on-going work.

## Citation

If you use Genesis in your research, please consider citing:

```bibtex
@software{Genesis,
  author = {Genesis Authors},
  title = {Genesis: A Universal and Generative Physics Engine for Robotics and Beyond},
  month = {December},
  year = {2024},
  url = {https://github.com/Genesis-Embodied-AI/Genesis}
}