# Genesis-Embodied-AI/genesis-world

Simulation platform for general-purpose robotics & embodied AI learning.

Repository: https://github.com/Genesis-Embodied-AI/genesis-world
Canonical: https://ross.abutalabs.com/products/genesis-world
Homepage: https://genesis-world.readthedocs.io
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-08-26T16:28:42+00:00

## Health v2 (maintenance only)
Score: 93/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 97, longevity 74
- inputs: {"age_days": 1037, "days_push": 7, "days_rel": 20, "gap_med": 8, "n_releases_24m": 36}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 29809, forks 2844 (observed 2026-08-28T04:11:53.684642+00:00)

## What it is
Genesis World is a Python simulation platform for physical AI and robotics that combines a unified multi-physics engine, a photorealistic renderer (Nyx), and a cross-platform compiler (Quadrants) behind a single Pythonic API. It scales from laptop CPUs to datacenter-grade GPUs and supports asset formats like URDF, MJCF, USD, and GLB.

## Use cases
- simulate robots for embodied AI research
- train reinforcement learning policies in physics simulation
- generate synthetic training data for robotics
- render photorealistic sensor data from simulated scenes
- run parallel physics environments on GPUs
- simulate soft bodies, fluids, and rigid bodies in one scene

## When to choose
- you need a unified multi-physics engine for robotics research
- you want GPU-accelerated simulation that scales to datacenter hardware
- you need photorealistic rendering integrated with physics simulation
- you prefer a Pythonic API embeddable in ML pipelines

## When to avoid
- you need a battle-tested simulator for safety-critical industrial deployment
- you require a lightweight 2D physics engine for simple games
- you need a mature technical report or long-term stability guarantees, as the project is young and evolving

## Facets
- artifact type: library
- maturity: active
- function: simulation, graphics, machine-learning, robotics
- domain: simulation, robotics, machine-learning, artificial-intelligence, gpu-computing
- platform: python, cross-platform, windows
- tags: physics-engine, embodied-ai, robotics-simulation, multi-physics, renderer, reinforcement-learning-environments, urdf, usd, gpu, linux, macos

## Member repositories
- Genesis-Embodied-AI/genesis-world (main) score 93

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:53.684642+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:52:52.848522+00:00, confidence not recorded.
  - readme: https://github.com/Genesis-Embodied-AI/genesis-world (fetched 2026-08-28T04:11:53.684642+00:00, sha e4c4283a42c7)
  - homepage: https://genesis-world.readthedocs.io (fetched 2026-08-29T07:49:55.742943+00:00, sha abd7f351dd67)
- Data as of 2026-08-30T08:39:29.467469+00:00.