2025年具身智能黑客松：Physical AI的最前沿探索

flowchart TD
    subgraph 感知层
        A[视觉传感器
        摄像头/RGB-D] --> B[视觉处理模块
        目标检测/分割
        场景理解] 
        C[触觉传感器
        力觉/压力] --> D[触觉处理模块
        接触识别
        力反馈处理] 
        E[惯性传感器
        IMU/编码器] --> F[运动感知模块
        姿态估计
        运动跟踪] 
    end
    
    subgraph 决策层
        B --> G[多模态融合
        特征整合] 
        D --> G 
        F --> G 
        G --> H[任务规划器
        高层任务分解
        动作序列生成] 
        H --> I[运动控制器
        轨迹生成
        力矩控制] 
    end
    
    subgraph 执行层
        I --> J[机器人本体
        机械臂/底盘] 
        J --> K[环境交互
        物理操作] 
        K --> L[反馈环路
        状态更新] 
    end
    
    L --> G

class ImitationLearningSystem:
    def __init__(self, config):
        # 初始化模仿学习组件
        self.demonstration_encoder = DemonstrationEncoder(config)
        self.policy_network = PolicyNetwork(config)
        self.critic_network = CriticNetwork(config)
        self.optimizer = torch.optim.Adam(
            list(self.policy_network.parameters()) + 
            list(self.critic_network.parameters()),
            lr=config.learning_rate
        )
    
    def load_demonstrations(self, demonstration_data):
        # 加载示范数据
        self.demonstrations = demonstration_data
        # 预处理示范数据
        self.preprocessed_demos = self.demonstration_encoder.preprocess(demonstration_data)
    
    def train_policy(self):
        # 从示范数据中学习策略
        for epoch in range(self.config.num_epochs):
            # 采样批次数据
            batch = self.sample_batch(self.preprocessed_demos, self.config.batch_size)
            
            # 前向传播
            states, actions, rewards, next_states, dones = batch
            
            # 计算策略输出
            action_logits = self.policy_network(states)
            
            # 计算价值估计
            value_estimates = self.critic_network(states)
            next_value_estimates = self.critic_network(next_states)
            
            # 计算优势函数
            advantages = self.compute_advantages(
                rewards, value_estimates, next_value_estimates, dones,
                self.config.gamma, self.config.lam
            )
            
            # 计算损失函数
            policy_loss = self.compute_policy_loss(action_logits, actions, advantages)
            value_loss = self.compute_value_loss(value_estimates, rewards, next_value_estimates, dones, self.config.gamma)
            
            # 总损失
            total_loss = policy_loss + self.config.value_coef * value_loss
            
            # 反向传播和优化
            self.optimizer.zero_grad()
            total_loss.backward()
            self.optimizer.step()
    
    def inference(self, state):
        # 在实际环境中进行推理
        with torch.no_grad():
            action_logits = self.policy_network(state)
            action = torch.argmax(action_logits, dim=-1).item()
        return action

class MultimodalFusion:
    def __init__(self, config):
        # 初始化各模态编码器
        self.visual_encoder = VisualEncoder(config)
        self.tactile_encoder = TactileEncoder(config)
        self.motion_encoder = MotionEncoder(config)
        
        # 初始化融合模块
        self.fusion_module = AdaptiveFusionModule(config)
    
    def forward(self, visual_input, tactile_input, motion_input):
        # 提取各模态特征
        visual_features = self.visual_encoder(visual_input)
        tactile_features = self.tactile_encoder(tactile_input)
        motion_features = self.motion_encoder(motion_input)
        
        # 融合多模态特征
        fused_features = self.fusion_module(
            visual_features, tactile_features, motion_features
        )
        
        return fused_features

import torch
import lerobot
from lerobot import Robot, Environment, Task
from lerobot.common.policies import ImitationLearningPolicy
from lerobot.common.sensors import Camera, ForceSensor, IMU
from lerobot.common.controllers import MotionController

class EmbodiedIntelligenceSystem:
    def __init__(self):
        # 初始化机器人平台
        self.robot = Robot("xarm6")  # 使用优傲机械臂作为示例
        
        # 配置传感器系统
        self.camera = Camera("realsense_d435")
        self.force_sensor = ForceSensor("robotiq_ft300")
        self.imu = IMU("mpu6050")
        
        # 添加传感器到机器人
        self.robot.add_sensor(self.camera)
        self.robot.add_sensor(self.force_sensor)
        self.robot.add_sensor(self.imu)
        
        # 初始化控制器
        self.controller = MotionController(self.robot)
        
        # 初始化策略网络
        self.policy = ImitationLearningPolicy(
            observation_space=self.robot.observation_space,
            action_space=self.robot.action_space,
            hidden_dims=[256, 128, 64]
        )
    
    def load_demonstrations(self, demo_path):
        # 加载示范数据
        self.demonstrations = lerobot.load_demonstrations(demo_path)
        print(f"加载了{len(self.demonstrations)}条示范数据")
    
    def train(self, num_epochs=100, batch_size=32, learning_rate=1e-4):
        # 配置训练参数
        optimizer = torch.optim.Adam(self.policy.parameters(), lr=learning_rate)
        loss_fn = torch.nn.MSELoss()
        
        # 创建数据加载器
        train_loader = torch.utils.data.DataLoader(
            self.demonstrations,
            batch_size=batch_size,
            shuffle=True
        )
        
        # 训练循环
        for epoch in range(num_epochs):
            total_loss = 0.0
            for batch in train_loader:
                observations, actions = batch
                
                # 前向传播
                predicted_actions = self.policy(observations)
                
                # 计算损失
                loss = loss_fn(predicted_actions, actions)
                
                # 反向传播和优化
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
                
                total_loss += loss.item()
            
            # 打印训练进度
            avg_loss = total_loss / len(train_loader)
            print(f"Epoch {epoch+1}/{num_epochs}, Loss: {avg_loss:.6f}")
    
    def deploy(self):
        # 部署到机器人平台
        self.robot.set_policy(self.policy)
        print("策略已部署到机器人")
    
    def run(self):
        # 运行机器人
        self.robot.run()
        print("机器人开始运行")

# 使用示例
if __name__ == "__main__":
    # 创建具身智能系统
    embodied_ai = EmbodiedIntelligenceSystem()
    
    # 加载示范数据
    embodied_ai.load_demonstrations("path/to/demonstrations")
    
    # 训练策略
    embodied_ai.train(num_epochs=50, batch_size=16, learning_rate=5e-5)
    
    # 保存模型
    torch.save(embodied_ai.policy.state_dict(), "embodied_ai_policy.pt")
    
    # 部署到机器人
    embodied_ai.deploy()
    
    # 运行机器人
    embodied_ai.run()