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genesis-embodied-ai/genesis-world

Analysis updated 2026-05-18

28,625PythonAudience · researcherComplexity · 4/5LicenseSetup · hard

TLDR

Fast physics simulator for training AI-controlled robots. Runs 430,000× faster than real time, supporting rigid bodies, liquids, gases, and deformable objects in one unified platform.

Mindmap

mindmap
  root((Genesis))
    What it does
      Physics simulation
      Robot training
      Multi-material support
    Key features
      43M frames per second
      Photo-realistic rendering
      Generative data engine
    Use cases
      Train robotic arms
      Test drone behavior
      Legged robot control
    Tech stack
      Python library
      GPU-accelerated
      Standard formats
    Audience
      Robotics researchers
      AI developers
      Hardware teams
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Code map

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What do people build with it?

USE CASE 1

Train AI models to control robotic arms by running millions of simulated practice scenarios before real-world deployment.

USE CASE 2

Test drone flight behavior and collision avoidance in virtual environments without risking hardware damage.

USE CASE 3

Simulate legged robot locomotion across different terrains to develop walking and running algorithms.

USE CASE 4

Generate synthetic training data for computer vision systems using photo-realistic rendering of robot interactions.

What is it built with?

PythonGPUCUDAPhysics engines

How does it compare?

genesis-embodied-ai/genesis-world521xueweihan/github520deepinsight/insightface
Stars28,62528,63128,609
LanguagePythonPythonPython
Setup difficultyhardeasymoderate
Complexity4/51/53/5
Audienceresearchergeneralresearcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · hard Time to first run · 1h+

Requires CUDA-capable GPU, CUDA toolkit installation, and likely compilation of physics engine bindings.

Use freely for any purpose including commercial. Keep the notice and disclose changes to the patent grant.

In plain English

Genesis is a physics platform designed for general-purpose robotics, embodied AI, and "physical AI", research where software agents have to interact with a simulated physical world before being deployed on real robots. Training those agents needs lots of realistic simulation, and existing simulators are often slow, hard to install, or limited to one kind of object. Genesis bundles several pieces into one package: a universal physics engine rebuilt from the ground up, a lightweight Python-friendly robotics simulation platform, a photo-realistic rendering system, and a "generative data engine" that aims to turn natural-language descriptions into useful simulation data. The way it works is that Genesis integrates several physics solvers, for rigid bodies, particle-based MPM and SPH for materials, finite-element FEM, position-based dynamics, and a stable-fluid solver, under one unified framework. That single framework can simulate rigid bodies, liquids, gases, deformable objects, thin shells, and granular materials, and couple them together. Robots are loaded from standard description formats including MJCF XML and URDF, plus mesh formats like obj, glb, ply, and stl. Genesis advertises very high simulation speed (over 43 million frames per second on a single RTX 4090 GPU when running a Franka robotic arm in their benchmark, they describe this as roughly 430,000 times faster than real time). It runs on Linux, macOS, and Windows, with support for CPU, Nvidia and AMD GPUs, and Apple Metal. Some solvers are also differentiable, which means gradients can flow back through the physics, useful for learning-based control. A native ray-traced renderer provides photorealistic visuals. Installation is via pip from PyPI, with a Docker option available, and Python 3.10 or newer is required. You would use Genesis if you are training or evaluating robotics policies, doing research in embodied AI, or generating synthetic data for robotic arms, legged robots, drones, or soft robots, and want a single Python-first toolkit that covers the physics, the rendering, and the data side. The repository is written primarily in Python.

Copy-paste prompts

Prompt 1
How do I set up Genesis to simulate a robotic arm and train a neural network to control it?
Prompt 2
Show me how to load a robot description file into Genesis and run a physics simulation.
Prompt 3
How can I use Genesis to generate training data for my robot's vision system?
Prompt 4
What's the fastest way to run 10,000 simulated robot experiments in Genesis on my GPU?
Prompt 5
How do I simulate liquids and deformable objects alongside rigid robots in Genesis?

Frequently asked questions

What is genesis-world?

Fast physics simulator for training AI-controlled robots. Runs 430,000× faster than real time, supporting rigid bodies, liquids, gases, and deformable objects in one unified platform.

What language is genesis-world written in?

Mainly Python. The stack also includes Python, GPU, CUDA.

What license does genesis-world use?

Use freely for any purpose including commercial. Keep the notice and disclose changes to the patent grant.

How hard is genesis-world to set up?

Setup difficulty is rated hard, with roughly 1h+ to a first successful run.

Who is genesis-world for?

Mainly researcher.

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