AI人工智能领域里Open AI的智能机器人研发

    import torch
    import torch.nn as nn
    
    class MultiModalTransformer(nn.Module):
    def __init__(self, text_dim, image_dim, joint_dim, num_heads=8):
        super().__init__()
        self.text_proj = nn.Linear(text_dim, joint_dim)
        self.image_proj = nn.Linear(image_dim, joint_dim)
        self.transformer = nn.TransformerEncoderLayer(
            d_model=joint_dim,
            nhead=num_heads
        )
    
    def forward(self, text_emb, image_emb):
        # 投影到共同空间
        text_proj = self.text_proj(text_emb)
        image_proj = self.image_proj(image_emb)
    
        # 拼接多模态输入
        combined = torch.cat([text_proj, image_proj], dim=1)
    
        # Transformer处理
        output = self.transformer(combined)
    
        return output
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/Zqwv8bLhiVz0sXxfluB9OjaTySnP.png)

    import numpy as np
    import torch
    import torch.optim as optim
    from torch.distributions import Normal
    
    class PPOPolicy:
    def __init__(self, state_dim, action_dim, hidden_dim=256):
        self.actor = nn.Sequential(
            nn.Linear(state_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, action_dim)
        )
    
        self.critic = nn.Sequential(
            nn.Linear(state_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 1)
        )
    
        self.optimizer = optim.Adam(list(self.actor.parameters()) +
                                   list(self.critic.parameters()), lr=3e-4)
    
    def act(self, state):
        state = torch.FloatTensor(state).unsqueeze(0)
        action_mean = self.actor(state)
        dist = Normal(action_mean, torch.ones_like(action_mean))
        action = dist.sample()
        return action.detach().numpy()[0]
    
    def update(self, states, actions, rewards, next_states, dones, gamma=0.99, clip_param=0.2):
        # 转换为张量
        states = torch.FloatTensor(states)
        actions = torch.FloatTensor(actions)
        rewards = torch.FloatTensor(rewards).unsqueeze(1)
        next_states = torch.FloatTensor(next_states)
        dones = torch.FloatTensor(dones).unsqueeze(1)
    
        # 计算优势函数
        values = self.critic(states)
        next_values = self.critic(next_states)
        returns = rewards + gamma * next_values * (1 - dones)
        advantages = returns - values
    
        # 计算新旧策略概率比
        old_action_mean = self.actor(states).detach()
        old_dist = Normal(old_action_mean, torch.ones_like(old_action_mean))
        old_log_probs = old_dist.log_prob(actions).sum(-1, keepdim=True)
    
        new_action_mean = self.actor(states)
        new_dist = Normal(new_action_mean, torch.ones_like(new_action_mean))
        new_log_probs = new_dist.log_prob(actions).sum(-1, keepdim=True)
    
        ratio = (new_log_probs - old_log_probs).exp()
    
        # PPO损失函数
        surr1 = ratio * advantages
        surr2 = torch.clamp(ratio, 1-clip_param, 1+clip_param) * advantages
        actor_loss = -torch.min(surr1, surr2).mean()
    
        critic_loss = (returns - values).pow(2).mean()
    
        # 总损失
        loss = actor_loss + 0.5 * critic_loss
    
        # 反向传播
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/oWF5xHlRQ8MaryJEgL1q9PCmvUdS.png)

    class LanguageGuidedPolicy:
    def __init__(self, llm, control_policy):
        self.llm = llm  # 预加载的语言模型
        self.control_policy = control_policy  # 底层控制策略
    
    def process_instruction(self, instruction, visual_observation):
        # 生成语言提示
        prompt = f"""
        Instruction: {instruction}
        Observation: {visual_observation}
    
        Based on the instruction and observation, what should the robot do next?
        Provide a detailed step-by-step plan.
        """
    
        # 调用语言模型生成计划
        plan = self.llm.generate(prompt, max_length=200)
    
        # 解析计划为可执行步骤
        steps = self._parse_plan(plan)
    
        return steps
    
    def execute_step(self, step, current_state):
        # 将高级步骤转换为控制命令
        action = self.control_policy.map_to_action(step, current_state)
    
        # 执行动作
        return action
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/4eG13jZO7xuXrQwgNq52tDTiBJ8L.png)

    # 创建conda环境
    conda create -n openai-robot python=3.9
    conda activate openai-robot
    
    # 安装核心依赖
    pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html
    pip install gym==0.26.2 mujoco==2.3.3 openai-baselines==0.1.6
    pip install transformers==4.25.1 openai==0.27.0
    
    # 安装机器人模拟器
    pip install pybullet==3.2.5 robosuite==1.4.0
    
    
    bash
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/K7nSrXzfTOZAPb5FJ2sI3p6yvRL9.png)

    import openai
    import numpy as np
    from robosuite import load_controller_config
    from robosuite.wrappers import GymWrapper
    
    class LanguageGuidedGrasping:
    def __init__(self):
        # 初始化机器人环境
        controller_config = load_controller_config(default_controller="OSC_POSE")
        self.env = GymWrapper(
            suite.make(
                "Lift",
                robots="Panda",
                controller_configs=controller_config,
                has_renderer=True,
                use_camera_obs=True,
            )
        )
    
        # 初始化语言模型
        openai.api_key = 'your-api-key'
        self.llm_model = "text-davinci-003"
    
    def generate_grasp_plan(self, instruction):
        # 获取当前视觉观察
        obs = self.env._get_observations()
        image = obs["agentview_image"]
    
        # 生成语言提示
        prompt = f"""
        You are a robot arm trying to {instruction}.
        Here is the current view from your camera:
        {image}
    
        Please provide a detailed step-by-step plan to accomplish this task.
        """
    
        # 调用OpenAI API
        response = openai.Completion.create(
            engine=self.llm_model,
            prompt=prompt,
            max_tokens=300,
            temperature=0.7
        )
    
        return response.choices[0].text
    
    def execute_plan(self, plan):
        # 解析计划为可执行动作
        steps = self._parse_plan(plan)
    
        for step in steps:
            action = self._step_to_action(step)
            obs, reward, done, info = self.env.step(action)
    
            if done:
                break
    
        return reward
    
    def _parse_plan(self, plan_text):
        # 简化的计划解析逻辑
        steps = []
        for line in plan_text.split('\n'):
            if "step" in line.lower() or "move" in line.lower():
                steps.append(line.strip())
        return steps
    
    def _step_to_action(self, step_description):
        # 简化的动作映射逻辑
        if "reach" in step_description.lower():
            return np.array([0.1, 0, 0, 0, 0, 0, 1])  # 向前移动
        elif "grasp" in step_description.lower():
            return np.array([0, 0, 0, 0, 0, 0, -1])  # 闭合夹爪
        else:
            return np.zeros(7)  # 默认动作
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/paAFgqo8riv6VzQfyXSM3nxd5cGK.png)

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