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Genesis: a generative world for general-purpose robotics & embodied AI learning

Dec 21, 2024

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https://genesis-embodied-ai.github.io/https://github.com/Genesis-Embodied-AI/Genesis

Genesis is a physics platform designed for general purpose Robotics/Embodied AI/Physical AI applications.

It is simultaneously multiple things:

  1. A universal physics engine re-built from the ground up, capable of simulating a wide range of materials and physical phenomena.
  2. A lightweight, ultra-fast, pythonic, and user-friendly robotics simulation platform.
  3. A powerful and fast photo-realistic rendering system.
  4. A generative data engine that transforms user-prompted natural language description into various modalities of data.

Powered by a universal physics engine re-designed and re-built from the ground up, Genesis integrates various physics solvers and their coupling into a unified framework. This core physics engine is further enhanced by a generative agent framework that operates at an upper level, aiming towards fully automated data generation for robotics and beyond.

Currently, we are open-sourcing the underlying physics engine and the simulation platform. Our generative framework is a modular system that incorporates many different generative modules, each handling a certain range of data modalities, routed by a high level agent. Some of the modules integrated existing papers and some are still under submission. Access to our generative feature will be gradually rolled out in the near future. If you are interested, feel free to explore more the paper list below.

Genesis is built and will continuously evolve with the following long-term missions:

  1. Lowering the barrier to using physics simulations and making robotics research accessible to everyone. (See our commitment)
  2. Unifying a wide spectrum of state-of-the-art physics solvers into a single framework, allowing re-creating the whole physical world in a virtual realm with the highest possible physical, visual and sensory fidelity, using the most advanced simulation techniques.
  3. Minimizing human effort in collecting and generating data for robotics and other domains, letting the data flywheel spin on its own.

Key Features

  • Speed: Genesis delivers an unprecedented simulation speed -- over 43 million FPS when simulating a Franka robotic arm with a single RTX 4090 (430,000 times faster than real-time).
  • Cross-platform: Genesis runs natively across different systems (Linux, MacOS, Windows), and across different compute backend (CPU, Nvidia GPU, AMD GPU, Apple Metal).
  • Unification of various physics solvers: Genesis develops a unified simulation framework that integrates various physics solvers: Rigid body, MPM, SPH, FEM, PBD, Stable Fluid.
  • Support a wide range of material models: Genesis supports simulation (and the coupling) of rigid and articulated bodies, various types of liquids, gaseous phenomenon, deformable objects, thin-shell objects and granular materials.
  • Support for a wide range of robots: Robot arm, legged robot, drone, soft robot, etc., and extensive support for loading different file types: MJCF (.xml), URDF, .obj, .glb, .ply, .stl, etc.
  • Photorealistic and high-performance ray-tracer: Genesis supports native ray-tracing based rendering.
  • Differentiability: Genesis is designed to be fully compatible with differentiable simulation. Currently, our MPM solver and Tool Solver are differentiable, and differentiability for other solvers will be added soon (starting with rigid-body simulation).
  • Physics-based Tactile Sensor: Genesis involves a physics-based and differentiable tactile sensor simulation module. This will be integrated to the public version soon (expected in version 0.3.0).
  • User-friendliness: Genesis is designed in a way to make using simulation as simple as possible. From installation to API design, if there's anything you found counter-intuitive or difficult to use, please let us know.

Getting Started

Quick Installation

Genesis is available via PyPI:

pip install genesis-world  # Requires Python >=3.9;

You also need to install PyTorch following the official instructions.

Documentation

Please refer to our documentation site for detailed installation steps, tutorials and API references.