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The GRID CLI connected to a robot fleet

Install GRID

One command to the whole platform — from zero to a running session in ten minutes.

Write your first skill

Drive your live robot from a generated notebook — state, motion, cameras, and cloud AI in plain Python.

Try a simulator session — no robot required

Spin up a GPU-backed Isaac Sim scene in the cloud and drive a robot from a notebook, before any hardware is involved.
GRID runs on Apple Silicon macOS and Linux (Windows via WSL for now). You’ll need access to your organization’s GRID cluster to open sessions. A physical robot is only needed for the real-robot paths — the simulator path needs none.

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What you can do with the GRID CLI

Everything below runs from the one GRID CLI:

Connect your robots

Onboard robots to your organization’s cluster with robot add — once connected, every teammate can open a session against them.

Build and test in simulation

Start cloud simulator sessions on NVIDIA Isaac Sim or AirGen from the CLI — a GPU-backed 3D scene with your robot, open in the browser in minutes.

Build and test with a real robot

Develop in a VS Code workspace wired to the live robot: state, motion, cameras, and cloud AI calls from a notebook, no environment setup.

Look up what your robot can do

One robot interface across physical machines, AirGen, and Isaac — the same standUp() and getImage() everywhere.

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Call AI models on your robot’s data

GRID Cortex hosts detection, depth, segmentation, grasping, VLMs, and VLA policies behind one Python call — no GPUs to manage:
CortexClient() reads your GRID_CORTEX_API_KEY from the environment — inside GRID session workspaces it’s already configured. Browse the full hosted catalog under Models, or the client reference under API Reference.

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Stay in the loop

Community

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Blog

Research and product updates from General Robotics.