机器人控制器开发(人形机器人产品设计)

我想做一个单机版的自主学习和自动部署模型的智能双臂机器人，
产品的硬件部分包括 主机械臂、从机械臂、含GPU的x86工控机，安卓人机交互一体屏。
需要开发linux系统中的软件和android App。

作为单机版的智能双臂机器人，需要用户能够进行傻瓜操作和快速部署落地。
机器人内部基于Lerobot开源框架，实现了训练服务、数据采集服务、模型推理服务、语音识别交互服务等。

用户开发安卓APP，点击创建新任务，输入任务的名称和数据集相关信息，并点击开始采集数据，可以不断采集数据，数据的视频会实时显示在安卓屏幕上。进行多轮数据采集后，点击结束采集。数据采集服务会对数据进行实时处理和存储。
用户点击训练按钮，设置训练的策略和轮次，开始训练，APP会实时显示训练的进度条，训练完成后会自动将模型保存并自动完成一个智能任务的部署。
用户可以点击应用该模型，便可马上启用这个技能，同时通过语言对话服务实现多模态交互操作。

🎯 产品架构总览

📋 详细实现流程

阶段1：硬件集成与LeRobot适配

1.1 创建自定义机器人配置

# src/lerobot/robots/custom_dual_arm/robot.py
class CustomDualArmRobot(Robot):
    def __init__(self, config):
        super().__init__(config)
        self.master_arm = self._init_master_arm()
        self.follower_arm = self._init_follower_arm()
        
    def connect(self):
        # 连接主从机械臂
        pass
        
    def teleop_step(self, action):
        # 遥操作步骤
        pass

1.2 电机和传感器集成

# 基于LeRobot的电机总线系统
from lerobot.motors.motors_bus import MotorsBus
from lerobot.cameras.camera import Camera

class CustomMotorsBus(MotorsBus):
    # 适配自定义电机协议
    pass

class CustomCamera(Camera):
    # 适配自定义相机
    pass

阶段2：Linux系统软件开发

2.1 核心服务架构

# services/robot_service.py
class RobotService:
    def __init__(self):
        self.robot = None
        self.data_collector = DataCollectionService()
        self.trainer = TrainingService()
        self.inference = InferenceService()
        self.task_manager = TaskManager()
        
    async def initialize_robot(self):
        # 初始化机器人硬件
        pass
        
    async def start_data_collection(self, task_id):
        # 开始数据采集
        pass

2.2 数据采集服务

# services/data_collection_service.py
class DataCollectionService:
    def __init__(self):
        self.buffer = OnlineBuffer()
        self.recording = False
        
    async def start_recording(self, task_name):
        """开始记录演示数据"""
        self.recording = True
        # 基于LeRobot的数据采集流程
        dataset = self._create_dataset(task_name)
        
        while self.recording:
            # 获取机器人状态和相机图像
            observation = await self._get_observation()
            action = await self._get_action()
            
            # 保存到数据集
            dataset.add_frame(observation, action)
            
    def stop_recording(self):
        self.recording = False
        return self._save_dataset()

2.3 模型训练服务

# services/training_service.py
class TrainingService:
    def __init__(self):
        self.training_process = None
        
    async def train_model(self, dataset_path, task_config):
        """基于LeRobot训练模型"""
        # 使用LeRobot的训练配置
        train_config = TrainPipelineConfig(
            policy_name="act",  # 或其他策略
            dataset_repo_id=dataset_path,
            training_steps=10000,
            # ... 其他配置
        )
        
        # 启动训练进程
        self.training_process = await self._start_training_process(train_config)
        
    def get_training_progress(self):
        """获取训练进度"""
        if self.training_process:
            return self._parse_training_logs()

2.4 推理执行服务

# services/inference_service.py
class InferenceService:
    def __init__(self):
        self.loaded_models = {}
        
    async def load_model(self, model_path, task_name):
        """加载训练好的模型"""
        policy = PreTrainedPolicy.from_pretrained(model_path)
        self.loaded_models[task_name] = policy
        
    async def execute_task(self, task_name, duration=None):
        """执行指定任务"""
        if task_name not in self.loaded_models:
            raise ValueError(f"Model for task {task_name} not loaded")
            
        policy = self.loaded_models[task_name]
        
        # 执行推理循环
        for step in range(duration or 1000):
            observation = await self._get_current_observation()
            action = policy.select_action(observation)
            await self._execute_action(action)

2.5 语音识别与NLP服务

# services/voice_service.py
class VoiceService:
    def __init__(self):
        self.asr_model = self._load_asr_model()
        self.nlp_processor = self._load_nlp_model()
        
    async def process_voice_command(self, audio_data):
        """处理语音指令"""
        # 语音转文字
        text = self.asr_model.transcribe(audio_data)
        
        # 意图识别和任务映射
        intent = self.nlp_processor.extract_intent(text)
        task_name = self._map_intent_to_task(intent)
        
        return {
            "text": text,
            "intent": intent,
            "task_name": task_name
        }

2.6 WebSocket API服务

# api/websocket_server.py
from fastapi import FastAPI, WebSocket
import asyncio

app = FastAPI()

class WebSocketManager:
    def __init__(self):
        self.active_connections = []
        self.robot_service = RobotService()
        
    async def connect(self, websocket: WebSocket):
        await websocket.accept()
        self.active_connections.append(websocket)
        
    async def handle_message(self, websocket: WebSocket, message: dict):
        command = message.get("command")
        
        if command == "start_data_collection":
            task_id = message.get("task_id")
            await self.robot_service.start_data_collection(task_id)
            
        elif command == "stop_data_collection":
            result = await self.robot_service.stop_data_collection()
            await websocket.send_json({"type": "data_saved", "data": result})
            
        elif command == "start_training":
            config = message.get("config")
            await self.robot_service.start_training(config)
            
        elif command == "execute_task":
            task_name = message.get("task_name")
            await self.robot_service.execute_task(task_name)

@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
    manager = WebSocketManager()
    await manager.connect(websocket)
    
    try:
        while True:
            data = await websocket.receive_json()
            await manager.handle_message(websocket, data)
    except Exception as e:
        print(f"Connection error: {e}")

阶段3：Android App开发

3.1 主界面架构

// MainActivity.kt
class MainActivity : AppCompatActivity() {
    private lateinit var webSocketClient: WebSocketClient
    private lateinit var taskManager: TaskManager
    
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        setContentView(R.layout.activity_main)
        
        initializeWebSocket()
        setupUI()
    }
    
    private fun initializeWebSocket() {
        webSocketClient = WebSocketClient("ws://localhost:8000/ws")
        webSocketClient.connect()
    }
}

3.2 任务管理界面

// TaskManagementFragment.kt
class TaskManagementFragment : Fragment() {
    private lateinit var taskAdapter: TaskAdapter
    private val tasks = mutableListOf<Task>()
    
    override fun onCreateView(inflater: LayoutInflater, container: ViewGroup?, savedInstanceState: Bundle?): View? {
        val view = inflater.inflate(R.layout.fragment_task_management, container, false)
        
        setupRecyclerView(view)
        setupButtons(view)
        
        return view
    }
    
    private fun createNewTask() {
        val dialog = CreateTaskDialog()
        dialog.setOnTaskCreatedListener { taskName ->
            val newTask = Task(taskName, TaskStatus.CREATED)
            tasks.add(newTask)
            taskAdapter.notifyDataSetChanged()
        }
        dialog.show(parentFragmentManager, "CreateTaskDialog")
    }
}

3.3 数据采集界面

// DataCollectionFragment.kt
class DataCollectionFragment : Fragment() {
    private var isRecording = false
    private lateinit var recordButton: Button
    private lateinit var statusText: TextView
    
    override fun onCreateView(inflater: LayoutInflater, container: ViewGroup?, savedInstanceState: Bundle?): View? {
        val view = inflater.inflate(R.layout.fragment_data_collection, container, false)
        
        recordButton = view.findViewById(R.id.record_button)
        statusText = view.findViewById(R.id.status_text)
        
        recordButton.setOnClickListener {
            if (isRecording) {
                stopRecording()
            } else {
                startRecording()
            }
        }
        
        return view
    }
    
    private fun startRecording() {
        isRecording = true
        recordButton.text = "停止录制"
        statusText.text = "正在录制演示数据..."
        
        // 发送开始录制命令
        val message = JSONObject().apply {
            put("command", "start_data_collection")
            put("task_id", getCurrentTaskId())
        }
        webSocketClient.send(message.toString())
    }
    
    private fun stopRecording() {
        isRecording = false
        recordButton.text = "开始录制"
        statusText.text = "录制完成"
        
        // 发送停止录制命令
        val message = JSONObject().apply {
            put("command", "stop_data_collection")
        }
        webSocketClient.send(message.toString())
    }
}

3.4 训练监控界面

// TrainingMonitorFragment.kt
class TrainingMonitorFragment : Fragment() {
    private lateinit var progressBar: ProgressBar
    private lateinit var lossChart: LineChart
    private lateinit var startTrainingButton: Button
    
    private fun startTraining() {
        val config = TrainingConfig(
            policyName = "act",
            trainingSteps = 10000,
            batchSize = 8,
            learningRate = 1e-4
        )
        
        val message = JSONObject().apply {
            put("command", "start_training")
            put("config", config.toJson())
        }
        webSocketClient.send(message.toString())
        
        // 开始监控训练进度
        startProgressMonitoring()
    }
    
    private fun startProgressMonitoring() {
        val handler = Handler(Looper.getMainLooper())
        val runnable = object : Runnable {
            override fun run() {
                requestTrainingProgress()
                handler.postDelayed(this, 1000) // 每秒更新一次
            }
        }
        handler.post(runnable)
    }
}

3.5 语音交互界面

// VoiceInteractionFragment.kt
class VoiceInteractionFragment : Fragment() {
    private lateinit var speechRecognizer: SpeechRecognizer
    private lateinit var voiceButton: ImageButton
    private lateinit var responseText: TextView
    
    private fun startVoiceRecognition() {
        val intent = Intent(RecognizerIntent.ACTION_RECOGNIZE_SPEECH).apply {
            putExtra(RecognizerIntent.EXTRA_LANGUAGE_MODEL, RecognizerIntent.LANGUAGE_MODEL_FREE_FORM)
            putExtra(RecognizerIntent.EXTRA_LANGUAGE, Locale.getDefault())
            putExtra(RecognizerIntent.EXTRA_PROMPT, "请说出您的指令...")
        }
        
        speechRecognizer.startListening(intent)
    }
    
    private val recognitionListener = object : RecognitionListener {
        override fun onResults(results: Bundle?) {
            val matches = results?.getStringArrayList(SpeechRecognizer.RESULTS_RECOGNITION)
            if (matches != null && matches.isNotEmpty()) {
                val spokenText = matches[0]
                processVoiceCommand(spokenText)
            }
        }
    }
    
    private fun processVoiceCommand(command: String) {
        val message = JSONObject().apply {
            put("command", "process_voice")
            put("text", command)
        }
        webSocketClient.send(message.toString())
    }
}

阶段4：系统集成与优化

4.1 系统启动脚本

#!/bin/bash
# start_robot_system.sh

# 启动LeRobot核心服务
cd /opt/lerobot
python -m lerobot.services.robot_service &

# 启动WebSocket API服务
python -m lerobot.api.websocket_server &

# 启动语音服务
python -m lerobot.services.voice_service &

# 等待所有服务启动
sleep 5

echo "机器人系统已启动"

4.2 配置管理

# config/robot_config.yaml
robot:
  type: "custom_dual_arm"
  master_arm:
    port: "/dev/ttyUSB0"
    baudrate: 1000000
  follower_arm:
    port: "/dev/ttyUSB1"
    baudrate: 1000000
    
cameras:
  - name: "wrist_cam"
    type: "realsense"
    resolution: [640, 480]
  - name: "top_cam"
    type: "opencv"
    device_id: 0
    
training:
  default_policy: "act"
  default_steps: 10000
  checkpoint_frequency: 1000
  
api:
  websocket_port: 8000
  max_connections: 10

4.3 数据流优化

# utils/data_pipeline.py
class OptimizedDataPipeline:
    def __init__(self):
        self.frame_queue = asyncio.Queue(maxsize=100)
        self.compression_enabled = True
        
    async def process_frame(self, frame_data):
        """优化的帧处理"""
        if self.compression_enabled:
            frame_data = self._compress_frame(frame_data)
            
        await self.frame_queue.put(frame_data)
        
    async def batch_save_frames(self):
        """批量保存帧数据"""
        batch = []
        while len(batch) < 32:  # 批量大小
            try:
                frame = await asyncio.wait_for(self.frame_queue.get(), timeout=0.1)
                batch.append(frame)
            except asyncio.TimeoutError:
                break
                
        if batch:
            await self._save_batch_to_disk(batch)

🚀 部署与运维

系统部署流程

1. 硬件连接：连接主从机械臂、相机、工控机
2. 系统安装：安装Ubuntu + LeRobot + 自定义服务
3. 硬件标定：使用LeRobot标定工具标定电机和相机
4. 服务启动：启动所有后台服务
5. App安装：在安卓屏上安装并配置App
6. 系统测试：端到端功能测试

用户操作流程

1. 创建任务 → Android App创建新任务
2. 数据录入 → 用户演示操作，系统记录数据
3. 数据训练 → 系统自动训练模型
4. 任务执行 → 语音指令或手动触发执行
5. 持续学习 → 根据执行结果优化模型

这个架构充分利用了LeRobot的模块化设计，通过标准化的接口集成自定义硬件，实现了一个完整的商业化智能机械臂产品。