LibreAI / Agents IA / genesis-world
// agents · Genesis-Embodied-AI/genesis-world

genesis-world

genesis-world est un projet de agents IA open source, créé par Genesis-Embodied-AI (Genesis-Embodied-AI/genesis-world sur GitHub). Il compte 30 035 étoiles sur GitHub et 2 878 forks, il est publié sous la licence Apache-2.0 et a été mis à jour pour la dernière fois sur GitHub le 8 octobre 2026.

A Python simulation platform for robotics and embodied AI — fast physics, rendering and learning

★ 30,0 k étoiles⑂ 2,9 k forksApache-2.0mis à jour oct. 2026
▦ visuel — le README de ce projet ne contient aucune capture intégrable,
voici donc un emplacement réservé. Le guide ci-dessous est complet malgré tout.
// détails du projet

Détails du projet

Étoiles GitHub
30 035
Forks
2 878
Licence
Apache-2.0
Dernière mise à jour sur GitHub
8 octobre 2026
Catégorie
Agents IA
Type de fiche
Guide d'installation complet
Site officiel
Official site
// fonctionnement

Comment ça marche

  1. Clarify your goal

    Decide what you want to achieve with genesis-world and check the README on GitHub for requirements before installing.

  2. Get the code

    Clone the repository from GitHub (or take the packaged install route in step 3 if the README offers one).

  3. Install and configure

    Follow the project's documented install commands — the Run section below lists the exact ones — and add any required API keys or models.

  4. Run your first task

    Work through the README's quickstart example end to end, then adapt it to your own project or data.

// fonctionnalités

Pourquoi on l'adore

Multiple physics solvers

A catalogue of solvers for rigid bodies, fluids, soft bodies and more, per the README's physics catalogue.

Built-in rendering

Rendering options for visualizing simulations and generating training imagery.

Python simulation interface

A clean Python API for building scenes, adding robots and stepping the simulation.

Optional extras

Installable extras add solvers and renderers (for example pyuipc for the IPC solver, gs-nyx for the Nyx renderer).

GPU and CPU paths

Documented install paths for NVIDIA CUDA, CPU-only and Apple Silicon (Metal/MPS) via uv/PyTorch builds.

Research-ready

Ships with citation info and is used as a platform for embodied-AI learning research.

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// lancez-le

Lancez-le vous-même

  1. Clone the repository

    Get the Genesis source for the development/editable route; most users pip-install instead.

    git clone https://github.com/Genesis-Embodied-AI/genesis-world.git
    cd genesis-world
  2. Install Genesis

    Install the released package (requires Python >=3.10, <3.14).

    pip install genesis-world
  3. Build your first scene

    Follow the documentation quickstart to create a scene, add a robot and step the simulation in Python.

    # see https://genesis-world.readthedocs.io for the first-scene example code
// cas d'usage

Ce que vous pouvez créer

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// faq

Questions fréquentes

Who maintains genesis-world?

genesis-world is maintained by the Genesis-Embodied-AI organization/account on GitHub, together with open-source contributors, in the public repository Genesis-Embodied-AI/genesis-world.

Is genesis-world free to use?

Yes — the code is open source under the Apache-2.0 license. You can use, modify and redistribute it under that license's terms.

Does genesis-world need a paid API key?

No API key — Genesis is a local simulation platform. You need suitable hardware (an NVIDIA GPU is recommended for larger scenes) and PyTorch.

How does genesis-world compare to MuJoCo / Isaac Sim?

Genesis aims for a unified, Pythonic platform spanning many physics types and renderers, positioning itself as an accessible all-in-one alternative to single-purpose simulators.

How do I get started with genesis-world?

Clone the repository from GitHub, follow the install commands in the Run section (taken from the project's README), and complete the documented quickstart example before adapting it to your own use case.

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// alternatives

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// plus en agents

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