GRID Docs | General Robotics

A skill is plain Python that drives your robot through GRID’s APIs. You develop it against a live robot in a generated notebook — no environment setup, because 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. Find out what your robot can do

connect() returns a RemoteRobot. Its attributes are the robot’s own methods, discovered live when you connect — so the fastest way to see the surface is to ask it:

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

Those methods come from the Robot interface, which is the same whether the robot is physical or simulated. A quadruped gets Quadruped, an arm gets Arm, and every robot gets the shared Robot methods like getState() and getImage().

3. Drive it

State, motion, and camera all work directly from notebook cells:

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.

Sensors, arms, and end effectors nest as attributes, so you can reach into a subcomponent the same way:

image = robot.getImage("front")
wrist = robot.left_arm.wrist_cam.getImage()

4. Add perception

Anything you capture can go straight to a hosted Cortex model. This finds a water bottle in the robot’s camera frame:

from grid_cortex_client import CortexClient

cortex = CortexClient()  # reads GRID_CORTEX_API_KEY from the environment
image = robot.getImage("front")
detections = cortex.run("owlv2", image_input=image, prompt="water bottle")
print(detections["scores"])

From there it’s ordinary Python — loop over frames, check a score threshold, and command the robot when you see what you’re looking for:

import time

robot.standUp()
while True:
    image = robot.getImage("front")
    detections = cortex.run("owlv2", image_input=image, prompt="water bottle")
    scores = detections.get("scores", [])
    if len(scores) > 0 and max(scores) > 0.3:
        robot.stop()
        break
    robot.moveByVelocity((0.0, 0.0, 0.0), (0.0, 0.0, 0.4))
    time.sleep(0.5)

Where next

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

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Your first skill

1. Open a dev session

2. Find out what your robot can do

3. Drive it

4. Add perception

Where next

​1. Open a dev session

​2. Find out what your robot can do

​3. Drive it

​4. Add perception

​Where next

1. Open a dev session

2. Find out what your robot can do

3. Drive it

4. Add perception

Where next