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What you can do with Lyapunov

Lyapunov is an LLM-first, 3D-native robotics and agent desktop framework that brings scene building, robot preparation, controlled simulation and camera data into one Linux workspace.

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ManfredCh/Lyapunov on GitHub

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What do you want to do?

4 use cases

  • Prepare and run a robot action in simulation

    Set up a robot in a 3D scene, initialize physics, and run one bounded action with feedback.

    View prompt
    In the ManfredCh/Lyapunov repo, prepare the robot <robot-name> in a paused world using the default MuJoCo engine, initialize physics, and run one bounded joint action. Report the robot's ready state and the actual world feedback after the action. Verify that the robot was initialized before any action was executed.

  • Collect RGB-D camera data from a simulated scene

    Place scene cameras, calibrate, annotate and record data for a dataset where the engine supports it.

    View prompt
    Using the ManfredCh/Lyapunov repo, read docs/CAPABILITY_MATRIX.md and docs/SCENE_CAMERA_PIPELINE.md, then set up RGB-D capture for the scene <scene-name> on the engine <engine-name>. Report which camera types and dataset export options are supported for that engine. Verify support against the capability matrix before recording.

  • Build a collision-ready 3D scene from a GLB file

    Import a GLB environment, align it, add collision geometry and confirm the physical response.

    View prompt
    In the ManfredCh/Lyapunov repo, import the GLB file <path-to-glb> into a new scene, prepare static collision geometry aligned with it, and apply it to the current world. Report the scale and orientation checks and the saved scene name. Verify the physical result by checking the world response after reopening the scene.

  • Install Lyapunov on Linux and open a local workspace

    Run the published installer, confirm the launcher, and open a guest workspace without an account.

    View prompt
    Using the ManfredCh/Lyapunov repo, install Lyapunov on this Linux x64 machine with the published installer, then confirm that the launcher exists at $HOME/.local/bin/lyapunov. Report whether the installer reported ready and whether the native physics check passed. Verify the launcher path before starting the app.

How to use Lyapunov

  1. Check you have what it needs: Linux x64 machine with a regular graphical desktop session (X11 is the verified keyboard path); Terminal access and internet access to run the published installer; Optional: Isaac Sim or Newton installed separately if you want those engines.
  2. Open ManfredCh/Lyapunov on GitHub and read the README and license.
  3. Install Lyapunov and launch it, then open or create a project in the workbench.
  4. Select the robot and the physics engine, then review the available controls, dependencies, placement and collision geometry.
  5. Prepare the world while it is paused, then initialize physics and confirm the robot is ready.
  6. Run one bounded action with the robot controls, then stop execution and inspect the actual world feedback.

These steps come from our top use case, Prepare and run a robot action in simulation, which also has a copy-paste agent prompt.

Lyapunov alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • Linux x64 machine with a regular graphical desktop session (X11 is the verified keyboard path)
  • Terminal access and internet access to run the published installer
  • Optional: Isaac Sim or Newton installed separately if you want those engines
  • Optional: a Peiri account with Credits, or your own model provider credentials, only if you want agent-driven work
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Questions about Lyapunov

What is Lyapunov?

Lyapunov is an LLM-first, 3D-native robotics and agent desktop framework that brings scene building, robot preparation, controlled simulation and camera data into one Linux workspace. It is an open-source project on GitHub: https://github.com/ManfredCh/Lyapunov.

Who is Lyapunov for?

Robotics developers, simulation and research teams, and 3D creators who work on a Linux x64 graphical desktop.

What can I do with Lyapunov?

4 use cases so far, for example: Prepare and run a robot action in simulation; Collect RGB-D camera data from a simulated scene; Build a collision-ready 3D scene from a GLB file. Each has steps and a copy-paste agent prompt.

What do I need to run Lyapunov?

Linux x64 machine with a regular graphical desktop session (X11 is the verified keyboard path); Terminal access and internet access to run the published installer; Optional: Isaac Sim or Newton installed separately if you want those engines; Optional: a Peiri account with Credits, or your own model provider credentials, only if you want agent-driven work.

What are alternatives to Lyapunov?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is Lyapunov safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.