002_具身人工智能的历史演进与关键里程碑：从理论构想到实际应用

timeline
    title 具身AI思想萌芽时期关键事件
    1943 : McCulloch和Pitts提出神经元模型
    1948 : 诺伯特·维纳发表《控制论》
    1950 : 图灵发表《计算机器与智能》
    1956 : 达特茅斯会议，AI正式诞生
    1959 : Unimate工业机器人诞生
    1966 : Shakey机器人项目启动
    1973 : WABOT-1人形机器人开发完成

# 强化学习在机器人控制中的应用示例

import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, models
import gym

class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = []
        self.gamma = 0.95    # 折扣因子
        self.epsilon = 1.0   # 探索率
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.model = self._build_model()
        self.target_model = self._build_model()
        self.update_target_model()

    def _build_model(self):
        # 构建Q网络模型
        model = models.Sequential()
        model.add(layers.Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(layers.Dense(24, activation='relu'))
        model.add(layers.Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=self.learning_rate))
        return model

    def update_target_model(self):
        # 更新目标网络
        self.target_model.set_weights(self.model.get_weights())

    def remember(self, state, action, reward, next_state, done):
        # 存储经验
        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):
        # 选择动作（ε-贪婪策略）
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])  # 返回Q值最大的动作

    def replay(self, batch_size):
        # 经验回放学习
        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                # 使用目标网络计算Q值
                target = reward + self.gamma * np.amax(self.target_model.predict(next_state)[0])
            target_f = self.model.predict(state)
            target_f[0][action] = target
            # 训练主网络
            self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

# 主函数示例（以CartPole环境为例）
import random

def train_agent(episodes=1000, batch_size=32):
    env = gym.make('CartPole-v1')
    state_size = env.observation_space.shape[0]
    action_size = env.action_space.n
    agent = DQNAgent(state_size, action_size)
    
    for e in range(episodes):
        state = env.reset()
        state = np.reshape(state, [1, state_size])
        for time in range(500):
            # 选择动作
            action = agent.act(state)
            # 执行动作
            next_state, reward, done, _ = env.step(action)
            reward = reward if not done else -10  # 失败时给予惩罚
            next_state = np.reshape(next_state, [1, state_size])
            # 存储经验
            agent.remember(state, action, reward, next_state, done)
            state = next_state
            if done:
                print(f"回合: {e}/{episodes}, 分数: {time}, 探索率: {agent.epsilon:.2}")
                break
        # 经验回放
        if len(agent.memory) > batch_size:
            agent.replay(batch_size)
        # 每100回合更新目标网络
        if e % 100 == 0:
            agent.update_target_model()
    
    # 保存模型
    agent.model.save("dqn_cartpole_model.h5")
    return agent

# 训练机器人控制策略
def train_robot_control():
    # 在实际应用中，这里会替换为真实机器人的状态和动作空间
    # 例如，使用ROS接口与物理机器人交互
    print("训练机器人控制策略...")
    # 以下为示例代码框架
    
    # 1. 初始化机器人接口
    # robot_interface = RobotInterface()
    
    # 2. 定义状态和动作空间
    # state_size = 机器人状态维度
    # action_size = 机器人动作维度
    
    # 3. 初始化DQN代理
    # agent = DQNAgent(state_size, action_size)
    
    # 4. 训练循环
    # for episode in range(num_episodes):
    #     state = robot_interface.reset()
    #     total_reward = 0
    #     for step in range(max_steps):
    #         action = agent.act(state)
    #         next_state, reward, done = robot_interface.step(action)
    #         agent.remember(state, action, reward, next_state, done)
    #         state = next_state
    #         total_reward += reward
    #         if done:
    #             break
    #     if len(agent.memory) > batch_size:
    #         agent.replay(batch_size)
    
    print("机器人控制策略训练框架示例")

if __name__ == "__main__":
    # 训练CartPole示例
    # agent = train_agent(episodes=1000, batch_size=32)
    
    # 展示机器人控制训练框架
    train_robot_control()

# 零信任安全架构在具身AI系统中的应用示例

import hashlib
import time
import json
from cryptography.fernet import Fernet

class ZeroTrustSecurityManager:
    def __init__(self):
        # 初始化安全管理器
        self.key = Fernet.generate_key()
        self.cipher = Fernet(self.key)
        self.access_control_policies = {}
        self.authentication_logs = []
        self.activity_monitor = {}
    
    def register_component(self, component_id, component_type, required_privileges):
        """注册具身AI系统组件"""
        print(f"注册组件: {component_id} (类型: {component_type})")
        # 为组件分配唯一标识符和证书
        component_certificate = self._generate_certificate(component_id)
        
        # 定义访问控制策略
        self.access_control_policies[component_id] = {
            'type': component_type,
            'privileges': required_privileges,
            'certificate': component_certificate,
            'last_authenticated': None,
            'trust_score': 100.0  # 初始信任分数
        }
        
        # 初始化活动监控
        self.activity_monitor[component_id] = {
            'last_activity_time': time.time(),
            'action_count': 0,
            'anomaly_count': 0
        }
        
        return component_certificate
    
    def _generate_certificate(self, component_id):
        """生成组件证书"""
        timestamp = str(time.time())
        data = f"{component_id}:{timestamp}:secure_component"
        certificate = hashlib.sha256(data.encode()).hexdigest()
        return certificate
    
    def authenticate_request(self, component_id, certificate, request_type, target_resource):
        """验证请求并实施零信任策略"""
        # 1. 身份验证
        if component_id not in self.access_control_policies:
            print(f"认证失败: 未知组件 {component_id}")
            return False, "未知组件"
        
        if self.access_control_policies[component_id]['certificate'] != certificate:
            print(f"认证失败: 证书不匹配 {component_id}")
            # 降低信任分数
            self.access_control_policies[component_id]['trust_score'] -= 20
            return False, "证书不匹配"
        
        # 2. 权限验证
        required_privilege = self._get_required_privilege(request_type, target_resource)
        if required_privilege not in self.access_control_policies[component_id]['privileges']:
            print(f"认证失败: 权限不足 {component_id} 请求 {request_type} 资源 {target_resource}")
            return False, "权限不足"
        
        # 3. 行为分析
        if not self._analyze_component_behavior(component_id, request_type, target_resource):
            print(f"认证失败: 异常行为检测 {component_id}")
            return False, "异常行为"
        
        # 4. 更新信任状态
        self.access_control_policies[component_id]['last_authenticated'] = time.time()
        # 小幅提高信任分数
        if self.access_control_policies[component_id]['trust_score'] < 100:
            self.access_control_policies[component_id]['trust_score'] += 5
        
        # 记录认证日志
        self.authentication_logs.append({
            'timestamp': time.time(),
            'component_id': component_id,
            'request_type': request_type,
            'target_resource': target_resource,
            'status': 'success'
        })
        
        # 更新活动监控
        self.activity_monitor[component_id]['last_activity_time'] = time.time()
        self.activity_monitor[component_id]['action_count'] += 1
        
        print(f"认证成功: {component_id} 访问 {target_resource}")
        return True, "认证成功"
    
    def _get_required_privilege(self, request_type, target_resource):
        """获取请求所需的权限"""
        # 简化示例，实际系统中可能更复杂
        privilege_map = {
            'read': f'read_{target_resource}',
            'write': f'write_{target_resource}',
            'execute': f'execute_{target_resource}'
        }
        return privilege_map.get(request_type, 'unknown_privilege')
    
    def _analyze_component_behavior(self, component_id, request_type, target_resource):
        """分析组件行为是否存在异常"""
        # 简化的行为分析逻辑
        # 实际系统中可能使用机器学习进行异常检测
        current_time = time.time()
        last_activity = self.activity_monitor[component_id]['last_activity_time']
        
        # 检查活动频率是否异常高
        if current_time - last_activity < 0.01:  # 10ms内多次请求
            self.activity_monitor[component_id]['anomaly_count'] += 1
            if self.activity_monitor[component_id]['anomaly_count'] > 5:
                return False
        
        # 检查请求模式是否异常
        if request_type == 'execute' and target_resource == 'control_system':
            # 高敏感度操作，需要额外验证
            trust_score = self.access_control_policies[component_id]['trust_score']
            if trust_score < 80:
                return False
        
        return True
    
    def encrypt_communication(self, message, sender_id, receiver_id):
        """加密组件间通信"""
        # 添加元数据
        message_data = {
            'timestamp': time.time(),
            'sender': sender_id,
            'receiver': receiver_id,
            'message': message
        }
        
        # 序列化为JSON
        json_data = json.dumps(message_data)
        
        # 加密数据
        encrypted_data = self.cipher.encrypt(json_data.encode())
        
        return encrypted_data
    
    def decrypt_communication(self, encrypted_data, receiver_id):
        """解密组件间通信"""
        try:
            # 解密数据
            decrypted_data = self.cipher.decrypt(encrypted_data)
            
            # 反序列化为JSON
            message_data = json.loads(decrypted_data.decode())
            
            # 验证接收者
            if message_data['receiver'] != receiver_id:
                print(f"解密失败: 接收者不匹配 {receiver_id}")
                return None
            
            # 验证消息时效性（简化示例）
            current_time = time.time()
            if current_time - message_data['timestamp'] > 60:  # 消息超过60秒视为过期
                print(f"解密失败: 消息已过期")
                return None
            
            return message_data
        except Exception as e:
            print(f"解密失败: {str(e)}")
            return None
    
    def monitor_system_health(self):
        """监控系统健康状态"""
        current_time = time.time()
        issues = []
        
        for component_id, info in self.activity_monitor.items():
            # 检查组件是否活跃
            if current_time - info['last_activity_time'] > 300:  # 5分钟无活动
                issues.append(f"组件 {component_id} 可能离线")
            
            # 检查信任分数
            trust_score = self.access_control_policies[component_id]['trust_score']
            if trust_score < 50:
                issues.append(f"组件 {component_id} 信任分数过低: {trust_score}")
        
        return issues

# 使用示例
def demonstrate_zero_trust_architecture():
    # 初始化零信任安全管理器
    security_manager = ZeroTrustSecurityManager()
    
    # 注册具身AI系统组件
    print("===== 注册系统组件 =====")
    perception_cert = security_manager.register_component(
        'perception_module', 
        'sensor_processor', 
        ['read_camera', 'read_lidar', 'write_perception_data']
    )
    
    decision_cert = security_manager.register_component(
        'decision_module', 
        'ai_processor', 
        ['read_perception_data', 'write_action_plan', 'execute_control_system']
    )
    
    actuation_cert = security_manager.register_component(
        'actuation_module', 
        'controller', 
        ['read_action_plan', 'execute_motors']
    )
    
    # 演示正常认证流程
    print("\n===== 演示正常认证流程 =====")
    auth_success, msg = security_manager.authenticate_request(
        'perception_module', 
        perception_cert, 
        'read', 
        'camera'
    )
    print(f"认证结果: {auth_success}, 消息: {msg}")
    
    # 演示权限不足情况
    print("\n===== 演示权限不足情况 =====")
    auth_fail, msg = security_manager.authenticate_request(
        'perception_module', 
        perception_cert, 
        'execute', 
        'control_system'
    )
    print(f"认证结果: {auth_fail}, 消息: {msg}")
    
    # 演示加密通信
    print("\n===== 演示加密通信 =====")
    message = "检测到障碍物，距离2.5米"
    encrypted_msg = security_manager.encrypt_communication(
        message, 
        'perception_module', 
        'decision_module'
    )
    print(f"加密消息: {encrypted_msg}")
    
    # 解密通信
    decrypted_data = security_manager.decrypt_communication(
        encrypted_msg, 
        'decision_module'
    )
    if decrypted_data:
        print(f"解密成功，原始消息: {decrypted_data['message']}")
    
    # 演示系统健康监控
    print("\n===== 演示系统健康监控 =====")
    issues = security_manager.monitor_system_health()
    if issues:
        print("检测到问题:")
        for issue in issues:
            print(f"- {issue}")
    else:
        print("系统健康状态良好")

if __name__ == "__main__":
    demonstrate_zero_trust_architecture()