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genesis-embodied-ai/genesis-world

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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

Things people build with this

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.

Tech stack

PythonGPUCUDAPhysics engines

Getting 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 simulation platform built for robotics and AI research. In this context, "simulation" means creating a virtual world where robots and physical objects can be tested without needing real hardware. Researchers and developers use it to train AI systems that control robots, like robotic arms, drones, or legged robots, by running millions of practice scenarios in the simulated environment before deploying to the real world. What makes Genesis stand out is speed: it can simulate a robotic arm at over 43 million frames per second on a single high-end GPU, which the README describes as 430,000 times faster than real time. This speed matters because training AI with simulated data requires running enormous numbers of experiments, and faster simulation means faster learning. Genesis integrates several different physics "solvers", specialized engines for different types of physical matter, into one unified platform. This means you can simulate rigid objects, liquids, gases, deformable objects, and granular materials all together in one scene. It also includes photo-realistic rendering and supports loading standard robot description file formats. A second component described in the README is a "generative data engine" that can generate training data from natural language descriptions, though this part is noted as still being rolled out gradually. Installation is via Python's pip package manager, and the library supports Linux, macOS, and Windows. It requires Python 3.10 or newer. Written in Python, it is aimed at robotics and AI researchers who need fast, flexible physics simulation. The full README is longer than what was provided.

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?
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Generated 2026-05-21 · Model: sonnet-4-6 · Verify against the repo before relying on details.