Genesis

Overview

Genesis is a comprehensive, open-source simulation platform built for general-purpose robotics, embodied AI, and physical AI research. It combines a universal physics engine, high-performance rendering, and a modular generative-data framework to enable automated data production, high-fidelity simulation, and differentiable physics for learning and control.

Key Components

• Universal physics engine: Genesis unifies multiple physics solvers (rigid body, MPM, SPH, FEM, PBD, stable fluid, etc.) and supports coupling between different material types (rigid, soft, fluid, granular, thin-shell). The engine is designed for both fidelity and performance.
• High-performance rendering: Native ray-tracing-based rendering provides photo-realistic visual observations suitable for vision-based perception and sensor simulation.
• Generative data engine & agents: A modular generative framework converts natural-language prompts into multimodal synthetic data; a high-level agent orchestrates sub-modules for automated dataset generation (features rolled out progressively).
• Differentiability: Genesis is designed to be differentiable; certain solvers (e.g., MPM and tool solvers) already support gradients, with plans to expand differentiability to other solvers (articulated/rigid bodies etc.).

Performance & Compatibility

• Performance: Genesis emphasizes speed (the project reports very high FPS benchmarks in specific setups), enabling large-scale simulation experiments and rapid data generation.
• Cross-platform/backends: Runs on Linux, macOS, and Windows; supports CPU and GPU backends (NVIDIA/AMD) and Apple Metal.
• Formats & robots: Compatible with common robot and asset formats (URDF, MJCF, .obj, .glb, .ply, .stl) and supports various robot types (manipulators, legged robots, drones, soft robots).

Usage & Installation

Genesis is distributed via PyPI (package name genesis-world) and can be installed with pip. The repository also provides a Dockerfile for reproducible environments and examples. Developers are encouraged to install in editable/dev mode for contribution.

Use Cases

• Robotics policy learning and evaluation (manipulation, locomotion, soft-robot control).
• Large-scale synthetic dataset generation for perception and multimodal learning.
• Research on differentiable physics, sim-to-real transfer, and generative simulation methods.

Ecosystem & Community

Genesis integrates and builds upon several open-source projects (Taichi, LuisaRender/LuisaCompute, MuJoCo references, etc.). It provides documentation (multiple languages), an active GitHub repository with issues/discussions, and community channels (Discord, WeChat). The project is licensed under Apache 2.0.

Citation & Papers

The repository includes an associated-paper list and recommends a citation entry for academic usage. Several related research works (differentiable simulation, generative simulation, soft-robot benchmarks) are listed to indicate the project's academic and practical lineage.

Conclusion

Genesis is aimed at lowering barriers to high-fidelity physics simulation for robotics and embodied AI research by providing a fast, extensible, and generative-capable platform. Its combination of unified physics solvers, rendering, and automated data-generation tools makes it suitable for researchers and practitioners working on simulation-driven AI.