GRID Docs | General Robotics

A skill is a plain Python program that drives your robot through GRID’s APIs. You develop it against a live robot in a generated notebook, save it as a script, and deploy it from the shell. No environment setup — the CLI provisions the Python environment for you.

1. Open a dev session

GRID # session start robot

Pick your robot, then choose Create a new skill. The CLI opens a VS Code workspace with a generated notebook whose first cell already holds the live connection:

from grid_nexus_client import connect

robot = connect("my-go2")
print(f"Connected: {robot}")

2. Explore, then act

See every method your robot exposes:

for name in robot.session.methods_info():
    print(f"  {name}")

Then drive it — state, motion, camera, and Cortex calls all work from the notebook:

robot.getState()
robot.standUp()
robot.moveByVelocity((0.1, 0.0, 0.0), (0.0, 0.0, 0.0))

Motion cells move the robot physically. Make sure it has clearance, and run robot.shutdown() when you’re done — it releases the session cleanly.

3. Save it as a skill

When the notebook logic works, turn it into a program: a function decorated with @program() that takes the robot (and a Cortex client, if it calls models) plus your own parameters. while tick(hz=...) is the control loop — the body runs at that rate, decisions live in small @func helpers, and the program ends by breaking on a condition. This skill spins in place until the camera sees the prompt, then stops:

import argparse
import os

from grid_nexus_client import connect, resolve_grid_profile
from grid_nexus_program import Cortex, Robot, func, program, tick
from grid_types import Velocity


@func
def found(detections) -> bool:
    """True once OWLv2 sees the prompt with reasonable confidence."""
    scores = detections.get("scores", [])
    if hasattr(scores, "tolist"):
        scores = scores.tolist()
    if not isinstance(scores, list):
        scores = [scores] if scores is not None else []
    return len(scores) > 0 and max(scores) > 0.3


@program()
def find_object(robot: Robot, cortex: Cortex, prompt: str):
    robot.standUp()
    while tick(hz=2):
        image = robot.getImage("front")
        detections = cortex.run("owlv2", image_input=image, prompt=prompt)
        if found(detections):
            robot.stop()
            break
        robot.moveByVelocity(Velocity(0.0, 0.0, 0.0), Velocity(0.0, 0.0, 0.4))


def main(argv=None):
    parser = argparse.ArgumentParser(description="Spin until the camera sees the prompt")
    parser.add_argument("--prompt", type=str, default="water bottle")
    args = parser.parse_args(argv)

    robot_id = os.environ.get("GRID_ROBOT_ID", "")
    if not robot_id:
        raise ValueError("GRID_ROBOT_ID must name the robot to connect to")

    cortex = Cortex(base_url=resolve_grid_profile().cortex_base_url)
    robot = connect(robot_id, program=find_object)
    try:
        find_object(robot, cortex, prompt=args.prompt)
    finally:
        robot.shutdown()
        cortex.close()


if __name__ == "__main__":
    main()

connect(robot_id, program=find_object) opens the connection in deployment mode — the platform stages your program, streams its camera and state topics, and shows the run live in the session monitor. (A bare connect(robot_id) is the direct mode you used in the notebook; deployments always pass program=.) The deploy runner supplies GRID_ROBOT_ID and your Cortex API key through the environment.

4. Deploy it

GRID # session start robot

This time choose Deploy an existing skill and pick your script. The CLI:

provisions the workflow Python environment (GRID’s client packages install from a private index with credentials fetched from your cluster — don’t pip install them by hand),
prompts for your script’s arguments,
exports GRID_ROBOT_ID and your Cortex API key, then runs the script against the robot you selected, streaming logs live. Press Esc to cancel a run.

Where next

The full Cortex model catalog for perception in your skills: AI models
Calling models from Python: grid-cortex-client
Every shell command: CLI reference

Get Started

Simulation

Deployment

FAQ

Your first skill

1. Open a dev session

2. Explore, then act

3. Save it as a skill

4. Deploy it

Where next

​1. Open a dev session

​2. Explore, then act

​3. Save it as a skill

​4. Deploy it

​Where next

1. Open a dev session

2. Explore, then act

3. Save it as a skill

4. Deploy it

Where next