Gemini Robotics ER models can point to objects, track them in video, detect them with bounding boxes, and generate movement trajectories.
For full runnable code, see the Robotics cookbook.
Point to objects
The following example finds specific objects in an image and returns their
normalized [y, x] coordinates:
Python
from google import genai
PROMPT = """
Point to no more than 10 items in the image. The label returned
should be an identifying name for the object detected.
The answer should follow the json format: [{"point": <point>,
"label": <label1>}, ...]. The points are in [y, x] format
normalized to 0-1000.
"""
client = genai.Client()
uploaded_file = client.files.upload(file="my-image.png")
image_response = client.interactions.create(
model="gemini-robotics-er-2-preview",
input=[
{
"type": "image",
"uri": uploaded_file.uri,
"mime_type": uploaded_file.mime_type
},
{"type": "text", "text": PROMPT}
],
generation_config={"thinking_level": "high"},
)
print(image_response.output_text)
REST
# First, ensure you have the image file locally.
# Encode the image to base64
IMAGE_BASE64=$(base64 -w 0 my-image.png)
curl -X POST \
"https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gemini-robotics-er-2-preview",
"input": {
"parts": [
{
"inlineData": {
"mimeType": "image/png",
"data": "'"${IMAGE_BASE64}"'"
}
},
{
"text": "Point to no more than 10 items in the image. The label returned should be an identifying name for the object detected. The answer should follow the json format: [{\"point\": [y, x], \"label\": <label1>}, ...]. The points are in [y, x] format normalized to 0-1000."
}
]
},
"generation_config": {
"thinking_config": {
"thinking_level": "high"
}
}
}'
The output will be a JSON array containing objects, each with a point
(normalized [y, x] coordinates) and a label identifying the object.
JSON
[
{"point": [376, 508], "label": "small banana"},
{"point": [287, 609], "label": "larger banana"},
{"point": [223, 303], "label": "pink starfruit"},
{"point": [435, 172], "label": "paper bag"},
{"point": [270, 786], "label": "green plastic bowl"},
{"point": [488, 775], "label": "metal measuring cup"},
{"point": [673, 580], "label": "dark blue bowl"},
{"point": [471, 353], "label": "light blue bowl"},
{"point": [492, 497], "label": "bread"},
{"point": [525, 429], "label": "lime"}
]
The following image is an example of how these points can be displayed:
Tracking objects in a video
Gemini Robotics ER 2 can also analyze video frames to track objects over time. See Video inputs for a list of supported video formats.
Python
from google import genai
client = genai.Client()
uploaded_file = client.files.upload(file="my-video.mp4")
prompt = """
Point to the red ball in every frame where it appears.
The answer should follow the json format: [{"point": [y, x],
"label": <label>}, ...]. The points are in [y, x] format
normalized to 0-1000. Return one entry per frame that contains
the object.
"""
image_response = client.interactions.create(
model="gemini-robotics-er-2-preview",
input=[
{
"type": "video",
"uri": uploaded_file.uri,
"mime_type": uploaded_file.mime_type
},
{"type": "text", "text": prompt}
],
)
print(image_response.output_text)
Object detection and bounding boxes
In addition to points, you can prompt the model to return 2D bounding boxes, which provide more spatial detail for detected objects.
Python
from google import genai
client = genai.Client()
uploaded_file = client.files.upload(file="my-image.png")
prompt = """
Detect all objects in this image and return bounding boxes.
The answer should follow the JSON format:
[{"label": <label>, "y": <y_min>, "x": <x_min>,
"y2": <y_max>, "x2": <x_max>}, ...]
where coordinates are normalized to 0-1000.
"""
image_response = client.interactions.create(
model="gemini-robotics-er-2-preview",
input=[
{
"type": "image",
"uri": uploaded_file.uri,
"mime_type": uploaded_file.mime_type
},
{"type": "text", "text": prompt}
],
)
print(image_response.output_text)
Trajectories
Gemini Robotics ER 2 can generate sequences of points that define a trajectory, useful for guiding robot movement.
This example requests a trajectory to move a red pen to an organizer, including an estimate of the intermediate waypoints. The code has been reduced to show only the prompt.
Python
prompt = """
Generate a trajectory for the robotic arm to pick up the red pen
and place it in the organizer. Return a list of waypoints as JSON:
[{"step": <int>, "point": [y, x], "action": <description>}, ...]
where coordinates are normalized to 0-1000.
"""
Making room for a laptop
This example shows how Gemini Robotics ER can reason about a space. The prompt asks the model to identify which object needs to be moved to create space for another item.
Python
from google import genai
client = genai.Client()
uploaded_file = client.files.upload(file="path/to/image-with-objects.jpg")
prompt = """
Point to the object that I need to remove to make room for my laptop
The answer should follow the JSON format: [{"point": <point>,
"label": <label1>}, ...]. The points are in [y, x] format normalized to 0-1000.
"""
image_response = client.interactions.create(
model="gemini-robotics-er-2-preview",
input=[
{
"type": "image",
"uri": uploaded_file.uri,
"mime_type": uploaded_file.mime_type
},
{"type": "text", "text": prompt}
],
)
print(image_response.output_text)
The response contains a 2D coordinate of the object that answers the user's question, in this case, the object that should move to make room for a laptop.
[
{"point": [672, 301], "label": "The object that I need to remove to make room for my laptop"}
]
Packing a lunch
The model can also provide instructions for multi-step tasks and point to relevant objects for each step. This example shows how the model plans a series of steps to pack a lunch bag.
Python
from google import genai
client = genai.Client()
uploaded_file = client.files.upload(file="path/to/image-of-lunch.jpg")
prompt = """
Explain how to pack the lunch box and lunch bag. Point to each
object that you refer to. Each point should be in the format:
[{"point": [y, x], "label": }], where the coordinates are
normalized between 0-1000.
"""
image_response = client.interactions.create(
model="gemini-robotics-er-2-preview",
input=[
{
"type"