Genesis is a comprehensive, open-source platform designed for general-purpose robotics, embodied AI, and physical AI research. It distinguishes itself as a multi-faceted tool, functioning as a high-performance physics engine, a user-friendly robotics simulation platform, a powerful rendering system, and a generative data engine. The project's core purpose is to accelerate research in these fields by providing a unified, accessible, and efficient environment for simulating and generating data related to physical interactions.

At its heart, Genesis is built upon a re-engineered physics engine capable of simulating a wide array of materials and physical phenomena. This engine integrates various physics solvers, including those for rigid bodies, Material Point Method (MPM), Smoothed Particle Hydrodynamics (SPH), Finite Element Method (FEM), Position Based Dynamics (PBD), and Stable Fluid, within a unified framework. This allows for the simulation of diverse physical interactions, from the movement of robotic arms to the behavior of liquids, gases, deformable objects, and granular materials. The platform's versatility extends to its compatibility with various robot types, including robotic arms, legged robots, drones, and soft robots, and supports the import of models from common formats like MJCF, URDF, OBJ, GLB, and STL.

One of Genesis's key strengths is its focus on speed and efficiency. The platform boasts impressive performance, achieving over 43 million frames per second when simulating a Franka robotic arm on a single RTX 4090 GPU. This speed advantage, coupled with its cross-platform compatibility (Linux, macOS, Windows) and support for multiple compute backends (CPU, Nvidia/AMD GPUs, Apple Metal), makes it a powerful tool for researchers. Furthermore, Genesis is designed to be fully differentiable, with differentiability currently supported in its MPM and Tool solvers, and plans for expansion to other solvers. This feature is crucial for applications involving gradient-based optimization and reinforcement learning.

Beyond its core physics engine, Genesis offers a photo-realistic rendering system based on native ray-tracing. This feature enhances the visual fidelity of simulations, providing researchers with high-quality visualizations. The platform also incorporates a generative data engine, which transforms user-prompted natural language descriptions into various data modalities. This generative capability aims to automate data generation, reducing the manual effort required for robotics research and allowing for the creation of large, diverse datasets. While the generative framework is still under development, the underlying physics engine and simulation platform are fully open-sourced.

Genesis is designed to be user-friendly, with intuitive installation and APIs. Installation is straightforward via pip, and the project provides comprehensive documentation in multiple languages (English, Chinese, and Japanese). The project actively encourages community contributions, welcoming pull requests, bug reports, and suggestions. The project is licensed under Apache 2.0. The project acknowledges the contributions of several open-source projects, including Taichi, FluidLab, SPH_Taichi, Ten Minute Physics, PBF3D, MuJoCo, libccd, PyRender, LuisaCompute, LuisaRender, Madrona, and Madrona-mjx. Genesis is a rapidly evolving project, with ongoing development and the release of new features and improvements. The project is supported by Genesis AI, and the team regularly releases updates and benchmarks to showcase its capabilities.