genesis-world-nightly 0.0.0

A universal and generative physics engine

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Genesis

Genesis is a physical 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. Currently, we are open-sourcing the underlying physics engine and the simulation platform. The generative framework will be released in the near future.

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)
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.

Getting Started

Quick Installation

Genesis is available via PyPI:

pip install genesis-world

You also need to install PyTorch following the official instructions.

Documentation

Please refer to our documentation site to for detailed installation steps, tutorials and API references.

Contributing to Genesis

The goal of the Genesis project is to build a fully transparent, user-friendly ecosystem where contributors from both robotics and computer graphics can come together to collaboratively create a high-efficiency, realistic (both physically and visually) virtual world for robotics research and beyond.

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 for bug reports and feature requests.
• Please use GitHub 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 for providing a cross-platform compute backend
• FluidLab for providing a reference MPM solver implementation
• SPH_Taichi for providing a reference SPH solver implementation
• MuJoCo and Brax for providing reference for rigid body dynamics
• PyRender for rasterization-based renderer
• LuisaCompute and LuisaRender for its ray-tracing DSL
• trimesh, PyMeshLab and 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).
• 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).
• 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.
• Wan, Weilin, et al. "Tlcontrol: Trajectory and language control for human motion synthesis." arXiv preprint arXiv:2311.17135 (2023).
• 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.

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