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-
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@@ -9,47 +8,75 @@
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+ 
+ 
+
# 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.
-Powered by a universal physics engine re-designed and re-built from the ground up, Genesis integrates various physics solvers and their coupling into a unified framework. This core physics engine is further enhanced by a generative agent framework that operates at an upper level, aiming towards fully **automated data generation** for robotics and beyond.
+Powered by a universal physics engine re-designed and re-built from the ground up, Genesis integrates various physics solvers and their coupling into a unified framework. This core physics engine is further enhanced by a generative agent framework that operates at an upper level, aiming towards fully **automated data generation** for robotics and beyond.
Currently, we are open-sourcing the **underlying physics engine and the simulation platform**. Our generative framework is a modular system that incorporates many different generative modules, each handling a certain range of data modalities, routed by a high level agent. Some of the modules integrated existing papers and some are still under submission. Access to our generative feature will be gradually rolled out in the near future. If you are interested, feel free to explore more the [paper list](#papers-behind-genesis) below.
Genesis is built and will continuously evolve with the following ***long-term missions***:
+
1. **Lowering the barrier** to using physics simulations and making robotics research accessible to everyone. (See our [commitment](https://genesis-world.readthedocs.io/en/latest/user_guide/overview/mission.html))
2. **Unifying a wide spectrum of state-of-the-art physics solvers** into a single framework, allowing re-creating the whole physical world in a virtual realm with the highest possible physical, visual and sensory fidelity, using the most advanced simulation techniques.
3. **Minimizing human effort** in collecting and generating data for robotics and other domains, letting the data flywheel spin on its own.
-Project Page: https://genesis-embodied-ai.github.io/
+Project Page:
## Key Features
+
- **Speed**: Genesis delivers an unprecedented simulation 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**: Genesis runs natively across different systems (Linux, MacOS, Windows), and across different compute backend (CPU, Nvidia GPU, AMD GPU, Apple Metal).
- **Unification of various physics solvers**: Genesis develops a unified simulation framework that integrates various physics solvers: Rigid body, MPM, SPH, FEM, PBD, Stable Fluid.
- **Support a wide range of material models**: Genesis supports simulation (and the coupling) of rigid and articulated bodies, various types of liquids, gaseous phenomenon, deformable objects, thin-shell objects and granular materials.
-- **Support for a wide range of robots**: Robot arm, legged robot, drone, _soft robot_, etc., and extensive support for loading different file types: `MJCF (.xml)`, `URDF`, `.obj`, `.glb`, `.ply`, `.stl`, etc.
+- **Support for a wide range of robots**: Robot arm, legged robot, drone, *soft robot*, etc., and extensive support for loading different file types: `MJCF (.xml)`, `URDF`, `.obj`, `.glb`, `.ply`, `.stl`, etc.
- **Photorealistic and high-performance ray-tracer**: Genesis supports native ray-tracing based rendering.
- **Differentiability**: Genesis is designed to be fully compatible with differentiable simulation. Currently, our MPM solver and Tool Solver are differentiable, and differentiability for other solvers will be added soon (starting with rigid-body simulation).
- **Physics-based Tactile Sensor**: Genesis involves a physics-based and differentiable [tactile sensor simulation module](https://github.com/Genesis-Embodied-AI/DiffTactile). This will be integrated to the public version soon (expected in version 0.3.0).
- **User-friendliness**: Genesis is designed in a way to make using simulation as simple as possible. From installation to API design, if there's anything you found counter-intuitive or difficult to use, please [let us know](https://github.com/Genesis-Embodied-AI/Genesis/issues).
## Getting Started
+
### 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/).
If you would like to try out the latest version, we suggest you to git clone from the repo and do `pip install -e .` instead of via PyPI.
### Documentation
+
Please refer to our [documentation site](https://genesis-world.readthedocs.io/en/latest/user_guide/index.html) for detailed installation steps, tutorials and API references.
## Contributing to Genesis
@@ -59,12 +86,16 @@ The goal of the Genesis project is to build a fully transparent, user-friendly e
We sincerely welcome *any forms of contributions* from the community to make the world a better place for robots. From **pull requests** for new features, **bug reports**, to even tiny **suggestions** that will make Genesis API more intuitive, all are wholeheartedly appreciated!
## Support
-* Please use Github [Issues](https://github.com/Genesis-Embodied-AI/Genesis/issues) for bug reports and feature requests.
-* Please use GitHub [Discussions](https://github.com/Genesis-Embodied-AI/Genesis/discussions) for discussing ideas, and asking questions.
+
+- Please use Github [Issues](https://github.com/Genesis-Embodied-AI/Genesis/issues) for bug reports and feature requests.
+
+- Please use GitHub [Discussions](https://github.com/Genesis-Embodied-AI/Genesis/discussions) for discussing ideas, and asking questions.
## License and Acknowledgment
+
The Genesis source code is licensed under Apache 2.0.
The development of Genesis won't be possible without these amazing open-source projects:
+
- [Taichi](https://github.com/taichi-dev/taichi): for providing a high-performance cross-platform compute backend. Kudos to all the members providing technical support from taichi!
- [FluidLab](https://github.com/zhouxian/FluidLab) for providing a reference MPM solver implementation
- [SPH_Taichi](https://github.com/erizmr/SPH_Taichi) for providing a reference SPH solver implementation
@@ -76,6 +107,7 @@ The development of Genesis won't be possible without these amazing open-source p
- [trimesh](https://github.com/mikedh/trimesh), [PyMeshLab](https://github.com/cnr-isti-vclab/PyMeshLab) and [CoACD](https://github.com/SarahWeiii/CoACD) for geometry processing
## Papers behind Genesis
+
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).
@@ -101,7 +133,9 @@ Genesis is a large scale effort that integrates state-of-the-art technologies of
... and many more on-going work.
## Citation
+
If you used Genesis in your research, we would appreciate it if you could cite it. We are still working on a technical report, and before it's public, you could consider citing:
+
```
@software{Genesis,
author = {Genesis Authors},
@@ -111,4 +145,3 @@ If you used Genesis in your research, we would appreciate it if you could cite i
url = {https://github.com/Genesis-Embodied-AI/Genesis}
}
```
-
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+
+# 概述
+
+Genesis 是一个为通用 *机器人/嵌入式 AI/物理 AI* 应用设计的物理平台。它同时具备多种功能：
+
+1. 一个从头开始重建的 **通用物理引擎**，能够模拟各种材料和物理现象。
+2. 一个 **轻量级**、**超快**、**Python 风格** 和 **用户友好** 的机器人模拟平台。
+3. 一个强大且快速的 **照片级真实感渲染系统**。
+4. 一个 **生成数据引擎**，将用户提示的自然语言描述转换为各种数据模式。
+
+Genesis 由一个重新设计和重建的通用物理引擎驱动，集成了各种物理求解器及其耦合到一个统一的框架中。这个核心物理引擎通过一个生成代理框架在上层进行增强，旨在实现机器人及其他领域的完全 **自动化数据生成**。
+
+目前，我们正在开源 **底层物理引擎和模拟平台**。我们的生成框架是一个模块化系统，包含许多不同的生成模块，每个模块处理一定范围的数据模式，由一个高级代理路由。一些模块集成了现有的论文，一些仍在提交中。我们的生成功能将逐步开放。如果您感兴趣，请参阅下面的 [论文列表](#papers-behind-genesis)。
+
+Genesis 的构建和持续发展基于以下 ***长期使命***：
+
+1. **降低使用物理模拟的门槛**，使机器人研究对所有人都可访问。（参见我们的 [承诺](https://genesis-world.readthedocs.io/en/latest/user_guide/overview/mission.html)）
+2. **将各种最先进的物理求解器统一到一个框架中**，使用最先进的模拟技术在虚拟领域中重新创建整个物理世界，达到最高的物理、视觉和感官保真度。
+3. **最小化人类在收集和生成机器人及其他领域数据上的努力**，让数据飞轮自行旋转。
+
+项目页面：
+
+## 主要特点
+
+- **速度**：Genesis 提供了前所未有的模拟速度——在单个 RTX 4090 上模拟 Franka 机器人手臂时超过 4300 万 FPS（比实时快 430,000 倍）。
+- **跨平台**：Genesis 原生运行在不同系统（Linux、MacOS、Windows）和不同计算后端（CPU、Nvidia GPU、AMD GPU、Apple Metal）上。
+- **各种物理求解器的统一**：Genesis 开发了一个统一的模拟框架，集成了各种物理求解器：刚体、MPM、SPH、FEM、PBD、稳定流体。
+- **支持广泛的材料模型**：Genesis 支持刚体和关节体、各种液体、气体现象、可变形物体、薄壳物体和颗粒材料的模拟（及其耦合）。
+- **支持广泛的机器人**：机器人手臂、腿式机器人、无人机、*软体机器人*等，并广泛支持加载不同文件类型：`MJCF (.xml)`、`URDF`、`.obj`、`.glb`、`.ply`、`.stl` 等。
+- **照片级真实感和高性能光线追踪器**：Genesis 支持基于光线追踪的原生渲染。
+- **可微分性**：Genesis 设计为完全兼容可微分模拟。目前，我们的 MPM 求解器和工具求解器是可微分的，其他求解器的可微分性将很快添加（从刚体模拟开始）。
+- **基于物理的触觉传感器**：Genesis 包含一个基于物理的可微分 [触觉传感器模拟模块](https://github.com/Genesis-Embodied-AI/DiffTactile)。这将很快集成到公共版本中（预计在 0.3.0 版本中）。
+- **用户友好性**：Genesis 设计为尽可能简化模拟的使用。从安装到 API 设计，如果有任何您觉得不直观或难以使用的地方，请 [告诉我们](https://github.com/Genesis-Embodied-AI/Genesis/issues)。
+
+## 入门
+
+### 快速安装
+
+Genesis 可通过 PyPI 获取：
+
+```bash
+pip install genesis-world # 需要 Python >=3.9;
+```
+
+您还需要按照 [官方说明](https://pytorch.org/get-started/locally/) 安装 **PyTorch**。
+
+### 文档
+
+请参阅我们的 [文档网站](https://genesis-world.readthedocs.io/en/latest/user_guide/index.html) 以获取详细的安装步骤、教程和 API 参考。
+
+## 贡献 Genesis
+
+Genesis 项目的目标是构建一个完全透明、用户友好的生态系统，让来自机器人和计算机图形学的贡献者 **共同创建一个高效、真实（物理和视觉上）的虚拟世界，用于机器人研究及其他领域**。
+
+我们真诚地欢迎来自社区的 *任何形式的贡献*，以使世界对机器人更友好。从 **新功能的拉取请求**、**错误报告**，到甚至是使 Genesis API 更直观的微小 **建议**，我们都全心全意地感谢！
+
+## 支持
+
+- 请使用 Github [Issues](https://github.com/Genesis-Embodied-AI/Genesis/issues) 报告错误和提出功能请求。
+
+- 请使用 GitHub [Discussions](https://github.com/Genesis-Embodied-AI/Genesis/discussions) 讨论想法和提问。
+
+## 许可证和致谢
+
+Genesis 源代码根据 Apache 2.0 许可证授权。
+没有这些令人惊叹的开源项目，Genesis 的开发是不可能的：
+
+- [Taichi](https://github.com/taichi-dev/taichi)：提供高性能跨平台计算后端。感谢 taichi 的所有成员提供的技术支持！
+- [FluidLab](https://github.com/zhouxian/FluidLab) 提供参考 MPM 求解器实现
+- [SPH_Taichi](https://github.com/erizmr/SPH_Taichi) 提供参考 SPH 求解器实现
+- [Ten Minute Physics](https://matthias-research.github.io/pages/tenMinutePhysics/index.html) 和 [PBF3D](https://github.com/WASD4959/PBF3D) 提供参考 PBD 求解器实现
+- [MuJoCo](https://github.com/google-deepmind/mujoco) 和 [Brax](https://github.com/google/brax) 提供刚体动力学参考
+- [libccd](https://github.com/danfis/libccd) 提供碰撞检测参考
+- [PyRender](https://github.com/mmatl/pyrender) 提供基于光栅化的渲染器
+- [LuisaCompute](https://github.com/LuisaGroup/LuisaCompute) 和 [LuisaRender](https://github.com/LuisaGroup/LuisaRender) 提供其光线追踪 DSL
+- [trimesh](https://github.com/mikedh/trimesh)、[PyMeshLab](https://github.com/cnr-isti-vclab/PyMeshLab) 和 [CoACD](https://github.com/SarahWeiii/CoACD) 提供几何处理
+
+## Genesis 背后的论文
+
+Genesis 是一个大规模的努力，将各种现有和正在进行的研究工作的最先进技术集成到一个系统中。这里我们列出了一些对 Genesis 项目有贡献的论文（非详尽列表）：
+
+- 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.
+
+... 以及许多正在进行的工作。
+
+## 引用
+
+如果您在研究中使用了 Genesis，我们将非常感谢您引用它。我们仍在撰写技术报告，在其公开之前，您可以考虑引用：
+
+```
+@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}
+}
+```
+ 
+ 
+ 
+