Gemini Robotics ER 2 可以指向对象、在视频中跟踪对象、使用边界框检测对象，以及生成运动轨迹。本页面上的所有示例均使用 generate_content 的自然语言提示。
如需查看完整的可运行代码，请参阅机器人技术实战指南。
指向物体
以下示例在图片中查找特定对象，并返回其归一化 [y, x] 坐标：
Python
from google import genai
from google.genai import types
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(
enterprise=True,
project="your-project-id",
location="global",
)
# Load your image
with open("my-image.png", 'rb') as f:
image_bytes = f.read()
image_response = client.models.generate_content(
model="gemini-robotics-er-2-preview",
contents=[
types.Part.from_bytes(
data=image_bytes,
mime_type='image/png',
),
PROMPT
],
config = types.GenerateContentConfig(
thinking_config=types.ThinkingConfig(
thinking_level=types.ThinkingLevel.HIGH
)
)
)
print(image_response.text)
REST
# First, ensure you have the image file locally.
# Encode the image to base64
IMAGE_BASE64=$(base64 -w 0 my-image.png)
# Set your Google Cloud project and location.
export PROJECT_ID="your-project-id"
export LOCATION="global"
export MODEL="gemini-robotics-er-2-preview"
curl -X POST \
"https://aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/publishers/google/models/${MODEL}:generateContent" \
-H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
-H "Content-Type: application/json" \
-d '{
"contents": [
{
"parts": [
{
"inlineData": {
"mimeType": "image/png",
"data": "'"${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."
}
]
}
],
"generationConfig": {
"thinkingConfig": {
"thinkingLevel": "HIGH"
}
}
}'
输出将是一个包含对象的 JSON 数组，每个对象都有一个 point（归一化 [y, x] 坐标）和一个用于标识对象的 label。
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"}
]
下图展示了这些点的显示方式：
跟踪视频中的对象
Gemini Robotics ER 2 还可以分析视频帧，以跟踪物体随时间的变化。
Python
from google import genai
from google.genai import types
client = genai.Client(
enterprise=True,
project="your-project-id",
location="global",
)
# Load your video
with open("my-video.mp4", 'rb') as f:
video_bytes = f.read()
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.models.generate_content(
model="gemini-robotics-er-2-preview",
contents=[
types.Part.from_bytes(
data=video_bytes,
mime_type='video/mp4',
),
prompt
],
config = types.GenerateContentConfig(
thinking_config=types.ThinkingConfig(
thinking_level=types.ThinkingLevel.HIGH
)
)
)
print(image_response.text)
对象检测和边界框
除了点之外，您还可以提示模型返回 2D 边界框，以便为检测到的对象提供更多空间细节。
Python
from google import genai
from google.genai import types
client = genai.Client(
enterprise=True,
project="your-project-id",
location="global",
)
with open("my-image.png", 'rb') as f:
image_bytes = f.read()
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.models.generate_content(
model="gemini-robotics-er-2-preview",
contents=[
types.Part.from_bytes(
data=image_bytes,
mime_type='image/png',
),
prompt
],
config = types.GenerateContentConfig(
thinking_config=types.ThinkingConfig(
thinking_level=types.ThinkingLevel.LOW
)
)
)
print(image_response.text)
轨迹
Gemini Robotics ER 2 可以生成定义轨迹的点序列，这对于引导机器人移动非常有用。
此示例请求生成一条轨迹，将一支红笔移动到收纳盒中，并估算中间航点。代码已缩减，仅显示提示。
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.
"""
为笔记本电脑腾出空间
此示例展示了 Gemini Robotics ER 2 如何对空间进行推理。提示会要求模型识别需要移动哪个对象，以便为另一个对象腾出空间。
Python
from google import genai
from google.genai import types
client = genai.Client(
enterprise=True,
project="your-project-id",
location="global",
)
# Load your image
with open('path/to/image-with-objects.jpg', 'rb') as f:
image_bytes = f.read()
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.models.generate_content(
model="gemini-robotics-er-2-preview",
contents=[
types.Part.from_bytes(
data=image_bytes,
mime_type='image/jpeg',
),
prompt
],
config = types.GenerateContentConfig(
thinking_config=types.ThinkingConfig(
thinking_level=types.ThinkingLevel.HIGH
)
)
)
print(image_response.text)
回答中包含回答用户问题的对象的 2D 坐标，在本例中，即应该移动以腾出笔记本电脑空间的对象。
[
{"point": [672, 301], "label": "The object that I need to remove to make room for my laptop"}
]
打包午餐
该模型还可以为多步骤任务提供说明，并指出每个步骤的相关对象。此示例展示了模型如何规划一系列步骤来打包午餐袋。
Python
from google import genai
from google.genai import types
client = genai.Client(
enterprise=True,
project="your-project-id",
location="global",
)
# Load your image and set up your prompt
with open('path/to/image-of-lunch.jpg', 'rb') as f:
image_bytes = f.read()
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.models.generate_content(
model="gemini-robotics-er-2-preview",
contents=[
types.Part.from_bytes(
data=image_bytes,
mime_type='image/jpeg',
),
prompt
],
config = types.GenerateContentConfig(
thinking_config=types.ThinkingConfig(
thinking_level=types.ThinkingLevel.HIGH
)
)
)
print(image_response.text)
此提示的回答是一组分步说明，介绍如何根据图片输入打包午餐袋。
输入图片
模型输出
Based on the image, here is a plan to pack the lunch box and lunch bag:
1. **Pack the fruit into the lunch box.** Place the apple, banana, red grapes, and green grapes into the blue lunch box.
2. **Add the spoon to the lunch box.** Put the blue spoon inside the lunch box as well.
3. **Close the lunch box.** Secure the lid on the blue lunch box.
4. **Place the lunch box inside the lunch bag.** Put the closed blue lunch box into the brown lunch bag.
5. **Pack the remaining items into the lunch bag.** Place the blue snack bar and the brown snack bar into the brown lunch bag.
Here is the list of objects and their locations:
* [{"point": [899, 440], "label": "apple"}]
* [{"point": [814, 363], "label": "banana"}]
* [{"point": [727, 470], "label": "red grapes"}]
* [{"point": [675, 608], "label": "green grapes"}]
* [{"point": [706, 529], "label": "blue lunch box"}]
* [{"point": [864, 517], "label": "blue spoon"}]
* [{"point": [499, 401], "label": "blue snack bar"}]
* [{"point": [614, 705], "label": "brown snack bar"}]
* [{"point": [448, 501], "label": "brown lunch bag"}]
后续步骤
如需详细了解 Gemini Robotics ER 2，请参阅以下资源：
- 智能体功能：代码执行、仪器读取和图片注解。
- 任务编排：使用自定义机器人 API 执行长跨度任务。
- 视频理解：时刻查找和进度分类。
- Gemini Robotics ER 2 概览：功能和快速入门。
- Gemini Robotics ER 2 模型信息：技术规格、词元限制和支持的功能。