Genesis: A Universal Physics Simulation Platform Redefining Robotics and Embodied AI

Genesis is an open-source universal physics simulation platform developed by the Genesis Embodied AI team, officially released at the end of 2024. Built from scratch, it integrates multiple physics solvers, supports ultra-high-speed simulation (up to 43 million FPS on a single RTX 4090), provides a natural language-driven generative data engine, and features photorealistic rendering and differentiable design capabilities, aiming to lower the barrier to robotics research and enable an automated data flywheel.

Genesis was born to address the pain points of high barriers and high costs in physical simulation for traditional robotics R&D: the need to master complex APIs, cross-platform compatibility issues, and time-consuming manual data collection. As a universal physics engine built from scratch, it plays four roles: universal physics engine, lightweight robotics simulation platform, photorealistic rendering system, and generative data engine, enabling a complete workflow from physical modeling to data generation.

Unified Physics Engine

Integrates multiple solvers: rigid body dynamics, MPM (deformable objects/liquids/gases/particles), SPH (fluids), FEM (elastic/plastic bodies), PBD (cloth/ropes), and stable fluids (smoke/gases), supporting complex scene interactions.

Extreme Performance

Simulates Franka robotic arm at 43 million FPS on a single RTX4090, with GPU acceleration, parallelized design, and cross-platform compatibility (CPU/NVIDIA/AMD GPU/Apple Metal).

Photorealistic Rendering

Built-in ray tracing system provides near-real visual data to facilitate vision-driven strategy training.

Differentiable Design

MPM and tool solvers already support gradient computation; differentiable rigid body/joint body solvers are under development, supporting gradient-based optimization.

Genesis's generative agent framework aims to automatically generate multi-modal data (visual, physical state, action sequences, etc.) through natural language descriptions, changing the inefficient paradigm of traditional manual scene design, code writing, and data annotation, and enabling an automated data flywheel. Currently, the underlying engine is open-source, and generative functions will be gradually released.

Simple Installation

Installable with two commands in a Python3.10+ environment: pip install torch + pip install genesis-world, supporting source code installation and Docker images (including ray tracing).

Robot Support

Compatible with robotic arms, quadruped robots, drones, etc., supporting import of formats like MJCF/URDF/obj/glb/ply/stl.

Active Community

Provides Discord/WeChat group communication channels, and maintains documentation and example code.

Amid the embodied AI boom, Genesis serves as key infrastructure with application scenarios including:

• Reinforcement learning training: ultra-high-speed simulation supports large-scale strategy learning
• Sim2Real transfer: strategies trained in photorealistic environments transferred to real robots
• Multi-modal data synthesis: generating annotated visual/tactile/kinematic data
• Algorithm prototype verification: rapid testing of control/perception algorithms
• Education and research: lowering the entry barrier for robotics learning

Genesis has seen active iterations since its release: v0.2.1 in January 2025, v0.3.0 in August, supported by Genesis AI. It has released performance benchmark reports comparing with mainstream simulators like Isaac Gym and MuJoCo. In the future, with the opening of the generative framework, it is expected to become an infrastructure for robot data production, connecting human intentions and machine capabilities.

Genesis integrates the capabilities of scattered tools, combining industrial-grade performance with academic flexibility, making it a worthwhile platform for robotics, embodied AI, and physical AI research. Project Resources:

• Homepage: https://genesis-embodied-ai.github.io/
• GitHub: https://github.com/Genesis-Embodied-AI/genesis-world
• Documentation: https://genesis-world.readthedocs.io/