from grid.model.perception.vla.openvla import OpenVLA
car = AirGenCar()

# We will be capturing an image from the AirGen simulator 
# and run model inference on it.

img =  car.getImage("front_center", "rgb").data

model = OpenVLA(use_local = True)
result = model.run(image=img, prompt = "Close the drawer")
print(result)
The OpenVLA class provides core functionality for this module.
​
class OpenVLA()
​
use_local
boolean
default:"False"
If True, inference call is run on the local VM, else offloaded onto GRID-Cortex. Defaults to True.
​
def run()
​
image
np.ndarray
required
The input RGB image of shape (M,N,3)(M,N,3).
​
prompt
str
required
Task instruction.
​
Returns
List[float]
Predicted action based on the query and image, represented as a 7-DoF vector.
from grid.model.perception.vla.openvla import OpenVLA
car = AirGenCar()

# We will be capturing an image from the AirGen simulator 
# and run model inference on it.

img =  car.getImage("front_center", "rgb").data

model = OpenVLA(use_local = True)
result = model.run(image=img, prompt = "Close the drawer")
print(result)
This code is licensed under the MIT License.