一套基本的具身智能技术流程是如何实现的

本文目录

python

from transformers import CLIPProcessor,

CLIPModel import torch

# 初始化多模态模型
clip_model=CLIPModel.from_pretrained

("openai/clip-vit-base-patch32")
clip_processor=CLIPProcessor.

from_pretrained

("openai/clip-vit-base-patch32")

def process_multimodal(image, text):
    inputs = clip_processor(text=[text],

 images=image, return_tensors=

"pt", padding=True)
    outputs = clip_model(**inputs)
    logits_per_image =

outputs.logits_per_image
    probs =

logits_per_image.softmax(dim=1)
    return probs

# 示例调用
image = Image.open("example.jpg")
text = "这是一个红色的苹果。"
prob = process_multimodal(image, text)
print(prob)

python

def parse_instruction(instruction):
    # 使用LLM解析指令
    response = bedrock_client.invoke_model(
        ModelId='yi-large',
        Prompt=instruction,
        MaxTokens=100)
    action_plan = response['GeneratedText']
    return action_plan

instruction = "请将红色的物体拿起来。"
action = parse_instruction(instruction)
print(action)

python

from transformers import GPT4oProcessor,

GPT4oModel
from PIL import Image

# 初始化多模态处理器和模型
processor=

GPT4oProcessor.from_pretrained

("openai/gpt4o")
model=

GPT4oModel.from_pretrained("openai/gpt4o")

def multimodal_inference(image_path, text):
    image = Image.open(image_path)
    inputs = processor

(text=text, images=image,

 return_tensors="pt")
    outputs = model(**inputs)
    return outputs

# 示例调用
image_path="scene.jpg"
text="描述这张图片中的主要物体和动作。"
outputs=

multimodal_inference(image_path, text)
print(outputs)

python

def fuse_multimodal_data(image, audio, text):
    # 处理视觉信息      

visual_features=

clip_model.get_image_features(clip_processor

(images=image, return_tensors="pt")

["pixel_values"])

    
    # 处理音频信息
   audio_features=

audio_model.extract_features(audio)
    
    # 处理文本信息
    text_features = llm_model.encode(text)
    
    # 融合特征
    fused_features =

torch.cat((visual_features, audio_features,

 text_features), dim=1)
    return fused_features

# 示例调用
image = Image.open("object.jpg")
audio = load_audio("command.wav")
text = "请识别并抓取桌上的苹果。"
features =

fuse_multimodal_data(image, audio, text)
print(features)

python

import gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim

# 定义简单的策略网络
class PolicyNetwork(nn.Module):
    def __init__

(self, state_size, action_size):
        super

(PolicyNetwork, self).__init__()
        self.fc =

nn.Linear(state_size, 128)
        self.action_head =

nn.Linear(128, action_size)
    
    def forward(self, x):
        x=torch.relu(self.fc(x))
        action_probs=torch.softmax

(self.action_head(x), dim=-1)
        return action_probs

# 初始化环境和网络
env = gym.make('CartPole-v1')
state_size =

env.observation_space.shape[0]
action_size =

env.action_space.n
policy_net =

 PolicyNetwork(state_size, action_size)
optimizer =

optim.Adam(policy_net.parameters(),

 lr=1e-2)
eps = np.finfo(np.float32).eps.item()

# 训练过程
for episode in range(1000):
    state = env.reset()
    rewards = []
    log_probs = []
    done = False
    while not done:
        state =

torch.from_numpy(state).float()
        action_probs =

policy_net(state)
        m=

torch.distributions.Categorical

(action_probs)
        action = m.sample()
        log_prob = m.log_prob(action)
        state, reward, done,

 _ = env.step(action.item())
        log_probs.append(log_prob)
        rewards.append(reward)
        if done:
            break
    # 计算累计奖励
    cumulative_reward = 0
    policy_loss = []
    for log_prob,

 reward in zip(log_probs, rewards):
        cumulative_reward += reward
        policy_loss.append

(-log_prob * cumulative_reward)
    optimizer.zero_grad()
    policy_loss =

torch.cat(policy_loss).sum()
    policy_loss.backward()
    optimizer.step()
    if episode % 100 == 0:
       print

(f"Episode {episode}, Reward:

{cumulative_reward}")

python

import numpy as np

class BehaviorPlanner:
    def __init__(self, robot, environment):
        self.robot = robot
        self.environment = environment
    
    def plan_path(self, start, goal):
        # 使用A*算法进行路径规划
        open_set = set()
        open_set.add(start)
        came_from = {}
        g_score = {start: 0}
        f_score=

{start: self.heuristic(start, goal)}
        

       while open_set:      

         current=

min(open_set,key=lambdax:f_score.get

(x, np.inf))

(came_from, current)
            
         open_set.remove(current)
for neighbor in self.environment.

get_neighbors(current):
        tentative_g =

 g_score[current] + self.distance

(current, neighbor)
        if tentative_g < g_score.get

(neighbor, np.inf):
        came_from[neighbor] = current
        g_score[neighbor] = tentative_g
        f_score[neighbor] =

tentative_g + self.heuristic

(neighbor, goal)
        open_set.add(neighbor)
   return []

    def heuristic(self, a, b):
        return np.linalg.norm(np.array(a) - 

np.array(b))
    
    def distance(self, a, b):
        return self.heuristic(a, b)
    
    def reconstruct_path(self, came_from, current):
        path = [current]
        while current in came_from:
            current = came_from[current]
            path.append(current)
        path.reverse()
        return path

# 示例调用
robot = MyRobot()
environment = EnvironmentGrid()
planner =

 BehaviorPlanner(robot, environment)
start = (0, 0)
goal = (5, 5)
path = planner.plan_path(start, goal)
print(f"规划路径: {path}")

python

import paddlespeech as ps

# 语音识别
def recognize_speech(audio_file):
    recognizer=ps.AsrModel.from_pretrained

("appbuilder-sdk-short")
    result = recognizer.recognize

(audio_file)
    return result

# 语音合成
def synthesize_speech(text, output_file):
    tts=ps.TtsModel.from_pretrained

("appbuilder-sdk-tts")
    tts.synthesize(text, output_file)

# 示例调用
audio_input = "command.wav"
recognized_text=

recognize_speech(audio_input)
print(f"识别结果:{recognized_text}")

text_output = "任务已完成。"
synthesize_speech(text_output, "output.wav")

python

import pika
import json

# 发送消息

def send_message(queue, message):      

  connection=

ika.BlockingConnection

(pika.ConnectionP

arameters('localhost'))

(exchange='', routing_key=queue, body=

json.dumps(message))
    connection.close()

# 接收消息
def receive_message(queue, callback):

     connection=

pika.BlockingConnection(pika.

ConnectionParameters('localhost'))

(queue=queue, on_message_callback

=lambda ch,

 method, properties, body:

 callback(json.loads(body)), auto_ack=True)
    channel.start_consuming()

# 示例调用
def action_callback(message):
    print(f"接收到消息: {message}")

# 发送
send_message('action_queue',

{'action': 'move', 'parameters':

{'x': 100, 'y': 200}})

# 接收
receive_message

('action_queue', action_callback)

python

from kafka import KafkaProducer,

 KafkaConsumer

# 生产者发送消息
producer = KafkaProducer

(bootstrap_servers='localhost:9092')
producer.send('sensor_topic', b'新传感器数据')
producer.flush()

# 消费者接收消息
consumer=KafkaConsume

r('sensor_topic', bootstrap_servers=

'localhost:9092')
for message in consumer:
    print(f"接收到消息: {message.value}")

bash

# 更新系统
sudo apt update
sudo apt upgrade -y

# 安装必要的软件包
sudo apt install -y python3-pip git

# 克隆项目代码
git clone https://github.com/TommyZihao

/vlm_arm.git
cd vlm_arm

# 创建虚拟环境
python3 -m venv venv
source venv/bin/activate

# 安装依赖
pip install -r requirements.txt

python

import torch
import torch.nn.utils.prune as prune

# 模型剪枝示例
def prune_model(model, amount=0.3):
    for name, module in model.

named_modules():
        if isinstance

(module, torch.nn.Linear):
            prune.l1_unstructured

(module, name='weight', amount=amount)
    return model

# 示例调用
pruned_model = prune_model

(policy_net, amount=0.2)

yaml

# Prometheus配置示例
global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'robot_system'
    static_configs:
      - targets: ['localhost:9090']
yaml

复制代码
# Grafana数据源配置
apiVersion: 1
datasources:
  - name: Prometheus
    type: prometheus
    url: http://localhost:9090

python

from PIL import Image
import torch

def detect_object(image, target_color):
    # 使用多模态模型进行颜色识别
    response = multimodal_inference

(image, f"找到一个{target_color}的物体。")
    # 假设返回物体的坐标
    object_coords =

 extract_coordinates(response)
    return object_coords

# 示例调用
frame = camera.get_frame()
target_color = "红色"
object_coords = detect_object

(frame, target_color)
print(f"目标物体坐标: {object_coords}")

python

# 规划路径
path=planner.plan_path

(current_position, object_coords)

# 执行路径
for point in path:
    move_to(point[0], point[1], point[2])
    # 添加必要的延时和状态检查

python

def main():
    # 获取并解析语音指令
    voice_command = get_voice_command()
    action_plan = parse_command

(voice_command)
    
    if action_plan['action']=='抓取'and

'颜色' in action_plan['parameters']:
        target_color =

action_plan['parameters']['颜色']
        # 视觉识别
        frame=camera.get_frame()
    object_coords=detect_object

(frame, target_color)
        
        if object_coords:

           # 路径规划             

          path=planner.plan_path

(current_position,object_coords)

 point[2])
            # 抓取物体
            mc.set_gripper_state(True)
            move_to(grasp_position)
            mc.set_gripper_state(False)
            print("抓取任务完成。")
        else:
            print

(f"未检测到{target_color}的物体。")
    else:
        print("不支持的指令。")

# 运行主函数
if __name__ == "__main__":
    main()