- Robotics researchers needing ultra-fast physics simulation for policy training
- Embodied AI teams generating synthetic training data from natural language prompts
- Developers building sim-to-real pipelines for manipulation and locomotion
Apache-2.0 · Bots
Genesis
Universal physics engine and simulation platform for robotics and embodied AI at 430,000x real-time speed.
29K stars 2.7K forks Apache-2.0 license 2026-06-04 verified
$
pip install genesis-world Open source
Overview
What is Genesis?
Genesis is a universal physics platform re-built from the ground up for general-purpose robotics, embodied AI, and physical AI applications. It integrates a high-performance physics engine, photo-realistic rendering, and a generative data engine into one Pythonic framework. Supported by a generative agent framework, Genesis can transform natural language prompts into multimodal training data at unprecedented speed.
Unified multi-solver physics engine
Genesis integrates rigid body, MPM, SPH, FEM, PBD, and fluid solvers into a single framework, enabling coupled simulation of diverse materials.
Real-world tasks often involve multiple physical phenomena — a robot arm picking up a wet cloth, for example. Genesis simulates the whole scene without switching engines.430,000x real-time simulation speed
Simulating a Franka robotic arm at over 43 million FPS on a single RTX 4090 — 430,000 times faster than real-time.
This speed makes it practical to generate massive synthetic datasets for training robot policies in hours instead of months.Generative data engine from natural language
A generative agent framework that takes natural language prompts and produces multimodal training data — scenes, tasks, trajectories, and sensor streams.
Data scarcity is the main bottleneck in robotics. Genesis turns language descriptions into infinite training data.Photo-realistic ray-tracing rendering
Native ray-tracing-based rendering pipeline for generating visually realistic synthetic images that bridge the sim-to-real gap.
Policies trained on photo-realistic simulation transfer better to real-world deployment. Install
One command to start
$
pip install genesis-world Use cases
What teams use it for
Ecosystem
Tags & capabilities
Comparison
How it stacks up
Choose Genesis for generative data at scale
vs traditional robotics simulatorsMuJoCo and PyBullet are excellent for rigid-body simulation but lack Genesis's multi-solver integration, generative AI pipeline, and extreme simulation speed.
FAQ
Questions
What hardware do I need to run Genesis?
Genesis runs on Linux, macOS, and Windows. GPU acceleration (NVIDIA/AMD) provides the best performance, but CPU mode is also available. The 430,000x speed benchmark was on a single RTX 4090.
Does Genesis support my robot?
Genesis supports loading MJCF (.xml), URDF, .obj, .glb, .ply, and .stl files. If your robot has a URDF, it can be simulated in Genesis.
Is Genesis differentiable?
Yes, Genesis is designed to be fully differentiable. Currently the MPM solver and Tool Solver support differentiability, with rigid body differentiation planned.
Decision brief
JSON
Should you use Genesis?
- Production-level real-time robot control (it is a simulation platform, not a robot OS)
- Verified 2026-06-04
- License: Apache-2.0
- Repo: Genesis-Embodied-AI/Genesis
- Open-source signal
Deployment
cloud
Permission surface
messages, hardware
Decision signals
No extra signals recorded
Agent packet
Structured decision data for Genesis
This packet is the compact machine-readable view agents should use before following source links or taking action.
Capabilities
robotics, messaging
Constraints
open source
Deployment
cloud
Permission surface
messages, hardware
Recommended workflows
Robotics or embodied agent workflow
Overview
What Genesis does
What it is
Genesis is simultaneously a universal physics engine, a lightweight ultra-fast robotics simulation platform, a photo-realistic rendering system, and a generative data engine. It integrates diverse physics solvers — rigid body, MPM, SPH, FEM, PBD, Stable Fluid — into one unified Pythonic framework, and wraps them with a generative agent framework that produces multimodal training data from natural language prompts.
Why it matters
Robotics research has been constrained by slow simulation (most simulators run at 1x to 10x real-time) and the manual effort required to create diverse training scenarios. At 430,000x real-time, Genesis removes the speed constraint. Its generative pipeline removes the manual-scenario constraint. Together, they open the door to training policies on synthetic data at a scale previously limited to companies with massive compute budgets.
How to evaluate it
Genesis uses a unified physics engine architecture that integrates multiple solver types (rigid body for articulated robots, MPM for granular materials, SPH for fluids, FEM for deformables, PBD for soft bodies) into a single computational graph. Each solver runs on GPU via the Taichi compute backend. The generative framework sits on top: a high-level agent takes natural language, plans a scene, generates trajectories, and outputs complete multimodal datasets ready for training.
Facts
Known metadata and operating surface
These fields are separated from editorial interpretation so agents can reason over facts and missing checks.
Resource type bot
Category Bots
Maturity active
Difficulty Unknown
License Apache-2.0
Pricing open source
Verified 2026-06-04
Source confidence high
Risk level elevated
Fit matrix
Where Genesis fits in an agent stack
strong
Robotics or embodied agent workflow
Genesis has multiple signals for robotics or embodied agent workflow, including matching tags, capabilities, category, or positioning.
- Separate simulator claims from hardware claims and verify safety boundaries before real-world operation.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Browser automation
Genesis has at least one signal for browser automation, but should be checked against a real task before adoption.
- Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Coding agent workflow
Genesis has at least one signal for coding agent workflow, but should be checked against a real task before adoption.
- Run a small repository change and inspect the diff, tests, and rollback path.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Evaluation and observability
Genesis has at least one signal for evaluation and observability, but should be checked against a real task before adoption.
- Add one repeatable test case and confirm results can run again in review or CI.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Reusable skill workflow
Genesis has at least one signal for reusable skill workflow, but should be checked against a real task before adoption.
- Run one skill end to end and check whether it produces evidence or structured output.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak
Connector or protocol layer
Genesis is not primarily positioned for connector or protocol layer in the current metadata.
- Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs
What an agent should inspect
Likely inputs
- Repositories, files, issues, terminal output, and test results
- Official setup instructions and a small real workflow
Likely outputs
- Diffs, commits, explanations, test results, or review notes
- A decision on whether this resource fits the target workflow
Evidence
Sources, claims, and missing checks
Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.
GitHub github
Repository source for code, license, issues, releases, and implementation details.
Homepage homepageOfficial or project-controlled source for this resource profile.
Genesis is listed as open source.
License metadata: Apache-2.0Genesis has a recorded GitHub repository: Genesis-Embodied-AI/Genesis.
Resource facts and GitHub source link.Genesis supports these recorded deployment modes: cloud.
OpenAgent decision signal metadata.Genesis is tagged with robotics, messaging capabilities.
OpenAgent capability taxonomy.- Dedicated docs link is missing.
- Repository freshness has not been recorded.
Next action
How to start evaluating Genesis
Inspect repository
Check license, recent activity, issues, examples, and security-sensitive code paths.
Open sourceOpen Homepage
Start from the official source before adopting third-party instructions.
Open sourceInstall Genesis
Install the Genesis simulation platform via pip.
pip install genesis-world Compare
Alternatives and nearby resources
Use related resources to compare category fit, license, deployment model, and first-workflow behavior.
FAQ
Common questions about Genesis
What hardware do I need to run Genesis?
Genesis runs on Linux, macOS, and Windows. GPU acceleration (NVIDIA/AMD) provides the best performance, but CPU mode is also available. The 430,000x speed benchmark was on a single RTX 4090.
Does Genesis support my robot?
Genesis supports loading MJCF (.xml), URDF, .obj, .glb, .ply, and .stl files. If your robot has a URDF, it can be simulated in Genesis.
Is Genesis differentiable?
Yes, Genesis is designed to be fully differentiable. Currently the MPM solver and Tool Solver support differentiability, with rigid body differentiation planned.