Genesis Introduction
Genesis is a comprehensive physics-based simulation platform that combines generative AI with universal physics engines to enable general-purpose robotics and embodied AI learning through automated environment generation and skill acquisition.
View MoreWhat is Genesis
Genesis is an innovative physics platform designed for Robotics, Embodied AI, and Physical AI applications that integrates multiple cutting-edge technologies into a unified framework. At its core, it's built as a universal physics engine that has been redesigned from the ground up, offering a lightweight, ultra-fast, and user-friendly simulation environment. The platform stands out for its ability to handle various physics solvers, support different materials and physical phenomena, and generate automated training data through its generative framework. Currently available as open-source software for its physics engine and simulation platform components, Genesis aims to make robotics research more accessible while pushing the boundaries of physical simulation fidelity.
How does Genesis work?
Genesis operates through a multi-layered architecture that combines various physics solvers (including Rigid body, MPM, SPH, FEM, PBD, Stable Fluid) into a unified simulation framework. It leverages GPU-accelerated parallel computation to achieve remarkable simulation speeds - up to 43 million FPS when simulating a Franka robotic arm on a single RTX 4090. The platform includes a powerful ray-tracing based rendering system for photorealistic visualization and supports cross-platform operation across different systems and compute backends. The generative framework sits at a higher level, using AI to automate the creation of diverse environments, robots, tasks, and skills based on natural language descriptions. This framework routes different data modalities through specialized generative modules, creating a self-guided system for continuous learning and skill acquisition.
Benefits of Genesis
Users of Genesis benefit from its unprecedented simulation speed, cross-platform compatibility, and comprehensive support for various robot types and file formats. The platform significantly reduces the barrier to entry for physics simulations and robotics research through its user-friendly design and extensive documentation. Its differentiable simulation capabilities enable advanced machine learning applications, while the physics-based tactile sensor simulation supports realistic contact-rich manipulations. The platform's ability to automatically generate diverse training data minimizes human effort in data collection and accelerates the development of robotic skills. Additionally, its photorealistic rendering and support for various material models ensure high-fidelity simulations that closely match real-world conditions.
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