全球首个人形机器人通用小脑：银河通用AstraBrain-WBC 0.5深度解析

# 传统QP求解器形式的WBC（简化示意）
import numpy as np
from scipy.optimize import minimize

def traditional_wbc(target_joint_positions, current_joint_positions, 
                    joint_limits, dt=0.001):
    """
    传统基于优化的WBC求解器
    """
    n_joints = len(current_joint_positions)
    
    def objective(delta_q):
        # 目标：最小化位置误差 + 最小化控制力
        q_next = current_joint_positions + delta_q
        pos_error = np.sum((q_next - target_joint_positions) ** 2)
        control_effort = np.sum(delta_q ** 2) * 1e-3
        return pos_error + control_effort
    
    def constraint_feasibility(delta_q):
        # 关节限位约束
        q_next = current_joint_positions + delta_q
        return np.minimum(
            joint_limits[:, 1] - q_next,
            q_next - joint_limits[:, 0]
        )
    
    constraints = [{'type': 'ineq', 'fun': constraint_feasibility}]
    
    result = minimize(
        objective, 
        x0=np.zeros(n_joints),
        constraints=constraints,
        method='SLSQP',
        options={'maxiter': 100, 'ftol': 1e-6}
    )
    
    # QP求解平均耗时：约3-5ms，远低于实时控制要求
    return result.x / dt  # 返回关节速度指令

import math
from typing import Optional, Tuple

# 由于沙箱环境限制，以下为伪代码风格实现
# 实际推理使用TensorRT优化版本

class AstraBrainCausalTransformer:
    """
    AstraBrain-WBC 0.5 因果Transformer核心实现
    参数: 8040万
    输入: obs_dim=128 (关节角度/角速度/IMU/足底力)
           cmd_dim=7 (目标位置/朝向/抓取力)
    输出: act_dim=29 (关节角度指令)
    """
    
    def __init__(self, d_model: int = 512, nhead: int = 8, 
                 num_layers: int = 8, max_seq_len: int = 256):
        self.d_model = d_model
        self.nhead = nhead
        self.num_layers = num_layers
        
        # 输入投影层
        self.obs_proj = Linear(128 + 7, d_model)  # obs + cmd 拼接
        self.time_enc = SinusoidalTimeEncoding(d_model, max_seq_len)
        
        # 8层Transformer
        self.layers = nn.ModuleList([
            TransformerBlock(d_model, nhead, dropout=0.1)
            for _ in range(num_layers)
        ])
        
        # 输出头 - 直接回归29维关节控制量
        self.output_head = nn.Sequential(
            nn.Linear(d_model, 256),
            nn.GELU(),
            nn.Linear(256, 128),
            nn.GELU(),
            nn.Linear(128, 29),  # 29-DOF
        )
        
        # 初始化参数
        self._init_weights()
    
    def forward(self, obs: Tensor, cmd: Tensor, 
                timesteps: Tensor) -> Tensor:
        """
        Args:
            obs: (B, T, 128) 观测序列
            cmd: (B, T, 7)   指令序列  
            timesteps: (B, T) 时间戳
        Returns:
            actions: (B, T, 29) 关节控制量
        """
        B, T = obs.shape[:2]
        
        # 拼接观测和指令
        x = torch.cat([obs, cmd], dim=-1)  # (B, T, 135)
        x = self.obs_proj(x)                # (B, T, 512)
        
        # 加入时间编码
        time_encoding = self.time_enc(timesteps)
        x = x + time_encoding
        
        # 因果Transformer前向传播
        for layer in self.layers:
            x = layer(x, causal_mask=True)  # 关键：因果掩码
        
        # 输出关节控制量
        actions = self.output_head(x)       # (B, T, 29)
        return actions
    
    def _init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.xavier_uniform_(m.weight, gain=0.02)
                nn.init.zeros_(m.bias)


class SinusoidalTimeEncoding(nn.Module):
    """正弦时间编码 - 确保时间连续性感知"""
    
    def __init__(self, d_model: int, max_len: int = 1000):
        super().__init__()
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(
            torch.arange(0, d_model, 2, dtype=torch.float) * 
            -(math.log(10000.0) / d_model)
        )
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        self.register_buffer('pe', pe.unsqueeze(0))
    
    def forward(self, timesteps: Tensor) -> Tensor:
        return self.pe[:, timesteps, :]

"""PPO专家策略训练 - 简化实现示意"""

class PPOExpertTrainer:
    """
    并行训练384个PPO专家策略
    """
    
    def __init__(self, env_configs: list, num_envs: int = 384):
        self.num_experts = num_envs
        self.envs = [create_environment(cfg) for cfg in env_configs]
        self.actors = [ActorNetwork(obs_dim=128, act_dim=29) 
                      for _ in range(num_envs)]
        self.critics = [CriticNetwork(obs_dim=128) 
                       for _ in range(num_envs)]
        
    def collect_experiences(self, expert_id: int, steps: int = 2048) -> dict:
        """收集单个专家的轨迹数据"""
        env = self.envs[expert_id]
        actor = self.actors[expert_id]
        
        states = []
        actions = []
        rewards = []
        next_states = []
        dones = []
        
        obs, _ = env.reset()
        for _ in range(steps):
            action = actor(obs.unsqueeze(0)).squeeze(0)
            next_obs, reward, done, truncated, info = env.step(action)
            
            states.append(obs)
            actions.append(action)
            rewards.append(reward)
            next_states.append(next_obs)
            dones.append(done or truncated)
            
            obs = next_obs
            if done or truncated:
                obs, _ = env.reset()
        
        return {
            'states': torch.stack(states),
            'actions': torch.stack(actions),
            'rewards': torch.tensor(rewards),
            'next_states': torch.stack(next_states),
            'dones': torch.tensor(dones),
        }
    
    def update_expert(self, expert_id: int, batch: dict, 
                      gamma: float = 0.99, lam: float = 0.95,
                      clip_epsilon: float = 0.2):
        """PPO更新（GAE优势估计 + 裁剪目标）"""
        states = batch['states']
        actions = batch['actions']
        rewards = batch['rewards']
        next_states = batch['next_states']
        dones = batch['dones']
        
        actor = self.actors[expert_id]
        critic = self.critics[expert_id]
        
        # GAE优势估计
        with torch.no_grad():
            values = critic(states)
            next_values = critic(next_states)
            deltas = rewards + gamma * next_values * (1 - dones) - values
            advantages = torch.zeros_like(deltas)
            running_adv = 0
            for t in reversed(range(len(deltas))):
                running_adv = deltas[t] + gamma * lam * running_adv * (1 - dones[t])
                advantages[t] = running_adv
            returns = advantages + values
        
        # PPO裁剪更新
        old_log_probs = actor.get_log_prob(states, actions).detach()
        
        for _ in range(10):  # 多epoch更新
            log_probs = actor.get_log_prob(states, actions)
            ratio = torch.exp(log_probs - old_log_probs)
            
            surr1 = ratio * advantages
            surr2 = torch.clamp(ratio, 1 - clip_epsilon, 1 + clip_epsilon) * advantages
            actor_loss = -torch.min(surr1, surr2).mean()
            
            value_loss = F.mse_loss(critic(states), returns)
            
            total_loss = actor_loss + 0.5 * value_loss
            # ... 反向传播与优化器更新

"""DAgger交互式蒸馏 - 核心实现"""

class DAggerDistillation:
    """
    DAgger: 交互式模仿学习蒸馏
    目标: 将384个PPO专家蒸馏为单个学生模型
    """
    
    def __init__(self, experts: list, student: nn.Module,
                 expert_selector, replay_buffer: ReplayBuffer):
        self.experts = experts          # 384个PPO专家
        self.student = student          # AstraBrain学生模型
        self.selector = expert_selector # 专家选择器
        self.buffer = replay_buffer     # 经验回放池
        
    def select_best_expert(self, state: Tensor, cmd: Tensor) -> int:
        """
        基于当前状态和指令，选择最匹配的专家
        使用轻量级k-NN分类器在技能嵌入空间中检索
        """
        return self.selector(state, cmd)
    
    def dagger_iteration(self, env, num_steps: int = 10000):
        """
        单次DAgger迭代
        学生交互 → 专家纠正 → 扩充数据集 → 重新训练
        """
        obs, info = env.reset()
        cmd = get_current_command(info)
        
        dataset_states, dataset_actions = [], []
        
        for step in range(num_steps):
            # 学生模型推理
            with torch.no_grad():
                student_action = self.student(obs, cmd)
            
            # 专家评估：如果学生不确定或执行偏差过大
            uncertainty = self.estimate_uncertainty(
                self.student, obs, cmd
            )
            
            if uncertainty > 0.3:  # 不确定阈值
                # 查询最佳专家
                expert_id = self.select_best_expert(obs, cmd)
                expert = self.experts[expert_id]
                
                # 专家给出正确动作
                expert_action = expert(obs.unsqueeze(0)).squeeze(0)
                
                # 存入数据集（状态+专家动作）
                dataset_states.append(torch.cat([obs, cmd]))
                dataset_actions.append(expert_action)
                
                # 执行专家动作
                next_obs, reward, done, truncated, info = env.step(expert_action)
            else:
                # 学生自信，执行学生动作
                next_obs, reward, done, truncated, info = env.step(student_action)
            
            obs = next_obs
            cmd = get_current_command(info)
            
            if done or truncated:
                obs, info = env.reset()
                cmd = get_current_command(info)
        
        # 将新数据加入回放池
        new_states = torch.stack(dataset_states)
        new_actions = torch.stack(dataset_actions)
        self.buffer.add(new_states, new_actions)
        
        # 在扩充后的数据集上微调学生模型
        self.train_student_on_buffer()
    
    def estimate_uncertainty(self, model, obs, cmd) -> float:
        """
        估计模型输出的不确定性
        使用MC Dropout或Ensemble方法
        """
        # 简化实现：基于输出分布熵估计
        with torch.no_grad():
            # 多次前向传播（开启dropout）
            outputs = []
            for _ in range(10):
                outputs.append(model(obs.unsqueeze(0), cmd.unsqueeze(0)))
            outputs = torch.stack(outputs)  # (10, 1, 29)
            
            # 计算动作分布的方差作为不确定性
            variance = outputs.var(dim=0).mean().item()
            return min(variance * 10.0, 1.0)  # 归一化到[0,1]

class DomainRandomizationWrapper:
    """仿真环境领域随机化"""
    
    def randomize_physics(self, env):
        """在每次reset时随机化物理参数"""
        # 随机化质量 (±50%)
        for body in env.bodies:
            body.mass *= np.random.uniform(0.5, 1.5)
        
        # 随机化摩擦力 (10倍范围)
        env.set_friction(np.random.uniform(0.1, 1.0))
        
        # 随机化关节阻尼
        for joint in env.joints:
            joint.damping *= np.random.uniform(0.5, 2.0)
        
        # 随机化延迟 (关键！模拟通信延迟)
        env.action_delay = np.random.randint(1, 5)  # 1-4帧延迟
        
        # 随机化传感器噪声
        env.obs_noise_std = np.random.uniform(0.0, 0.05)
        
        return env

// main_control_loop.go
// AstraBrain-WBC Go运行时主控制循环

package main

import (
    "encoding/binary"
    "fmt"
    "log"
    "os"
    "sync"
    "time"
    "unsafe"
)

/*
#include <stdint.h>
// CGO FFI接口声明
extern int astrabrain_infer(float* obs, float* cmd, float* output, int seq_len);
*/
import "C"

// ControlConfig 控制循环配置
type ControlConfig struct {
    ControlFreq    int     // 控制频率 (Hz)
    ObsDim         int     // 观测维度
    CmdDim         int     // 指令维度
    ActDim         int     // 动作维度
    SeqLen         int     // 时序长度
    SafetyTimeout  float64 // 安全超时 (秒)
}

// RobotState 机器人实时状态
type RobotState struct {
    mu           sync.RWMutex
    JointAngles  []float32 // 29-DOF关节角度
    JointVelocities []float32
    IMUData      [6]float32 // 加速度计+陀螺仪
    FootForces   [4]float32 // 四足底力传感器
    Timestamp    int64
}

// ControlCommand 上层指令 (来自VLA大脑)
type ControlCommand struct {
    TargetPos    [3]float32  // 目标位置 xyz
    TargetOri    [4]float32  // 目标朝向 (四元数)
    GripForce    float32     // 抓取力
}

// ControlLoop AstraBrain主控制循环
type ControlLoop struct {
    cfg          ControlConfig
    state        *RobotState
    cmd         chan ControlCommand
    actionOut   chan []float32
    seqBuffer   [][]float32 // 历史序列缓冲区
    ticker      *time.Ticker
    done        chan struct{}
}

// NewControlLoop 创建控制循环
func NewControlLoop(cfg ControlConfig) *ControlLoop {
    return &ControlLoop{
        cfg:        cfg,
        state:      &RobotState{},
        cmd:        make(chan ControlCommand, 10),
        actionOut:  make(chan []float32, 10),
        seqBuffer:  make([][]float32, 0, cfg.SeqLen),
        ticker:     time.NewTicker(time.Duration(1000000/cfg.ControlFreq) * time.Microsecond),
        done:       make(chan struct{}),
    }
}

// Start 启动控制循环
func (cl *ControlLoop) Start() {
    log.Printf("[AstraBrain] 控制循环启动 @ %d Hz", cl.cfg.ControlFreq)
    
    for {
        select {
        case <-cl.ticker.C:
            // 1. 读取传感器状态 (非阻塞)
            cl.readSensorData()
            
            // 2. 获取最新指令
            var cmd ControlCommand
            select {
            case cmd = <-cl.cmd:
            default:
                cmd = cl.lastCmd  // 保持上一指令
            }
            cl.lastCmd = cmd
            
            // 3. 构建观测序列
            obs := cl.buildObservation(cmd)
            
            // 4. 模型推理 (CGO调用)
            action := cl.modelInfer(obs)
            
            // 5. 安全检查
            if !cl.safetyCheck(action) {
                action = cl.getFallbackAction()
                log.Warn("[AstraBrain] 安全门限触发，使用回退策略")
            }
            
            // 6. 发送动作指令到执行器
            cl.actionOut <- action
            
        case <-cl.done:
            log.Printf("[AstraBrain] 控制循环终止")
            return
        }
    }
}

// buildObservation 构建模型输入观测
func (cl *ControlLoop) buildObservation(cmd ControlCommand) [][]float32 {
    cl.state.mu.RLock()
    defer cl.state.mu.RUnlock()
    
    // 拼接: [joint_angles(29) + joint_velocities(29) + imu(6) + 
    //        foot_forces(4) + cmd_pos(3) + cmd_ori(4) + cmd_grip(1)]
    obs := make([]float32, 0, cl.cfg.ObsDim+cl.cfg.CmdDim)
    obs = append(obs, cl.state.JointAngles...)
    obs = append(obs, cl.state.JointVelocities...)
    obs = append(obs, cl.state.IMUData[:]...)
    obs = append(obs, cl.state.FootForces[:]...)
    obs = append(obs, cmd.TargetPos[:]...)
    obs = append(obs, cmd.TargetOri[:]...)
    obs = append(obs, cmd.GripForce)
    
    // 维护历史序列
    cl.seqBuffer = append(cl.seqBuffer, obs)
    if len(cl.seqBuffer) > cl.cfg.SeqLen {
        cl.seqBuffer = cl.seqBuffer[1:]
    }
    
    // 填充至固定长度
    padded := make([][]float32, cl.cfg.SeqLen)
    copy(padded[cl.cfg.SeqLen-len(cl.seqBuffer):], cl.seqBuffer)
    for i := 0; i < cl.cfg.SeqLen-len(cl.seqBuffer); i++ {
        padded[i] = padded[cl.cfg.SeqLen-len(cl.seqBuffer)] // 首帧填充
    }
    
    return padded
}

// modelInfer CGO调用模型推理
func (cl *ControlLoop) modelInfer(obs [][]float32) []float32 {
    seqLen := len(obs)
    flatObs := make([]float32, 0, seqLen*(cl.cfg.ObsDim+cl.cfg.CmdDim))
    for _, frame := range obs {
        flatObs = append(flatObs, frame...)
    }
    
    output := make([]float32, cl.cfg.ActDim)
    
    // 调用CGO FFI -> Python/TensorRT推理
    ret := int(C.astrabrain_infer(
        (*C.float)(unsafe.Pointer(&flatObs[0])),
        nil, // cmd已嵌入obs
        (*C.float)(unsafe.Pointer(&output[0])),
        C.int(seqLen),
    ))
    
    if ret != 0 {
        log.Printf("[AstraBrain] 推理失败, code=%d, 使用零动作", ret)
        return make([]float32, cl.cfg.ActDim)
    }
    
    return output
}

// safetyCheck 安全检查 - 防止输出超出关节限制
func (cl *ControlLoop) safetyCheck(action []float32) bool {
    // 检查关节位置限制
    for i, a := range action {
        if a < cl.jointLimits[i][0] || a > cl.jointLimits[i][1] {
            return false
        }
    }
    // 检查加速度限制
    for i, a := range action {
        diff := abs(a - cl.prevAction[i])
        if diff > cl.maxAccel[i]*float32(cl.controlInterval) {
            return false
        }
    }
    return true
}

func main() {
    cfg := ControlConfig{
        ControlFreq:   1000,  // 1kHz控制频率
        ObsDim:        128,
        CmdDim:        7,
        ActDim:        29,
        SeqLen:        64,
        SafetyTimeout: 0.1,
    }
    
    loop := NewControlLoop(cfg)
    
    // 启动遥测日志
    go func() {
        for action := range loop.actionOut {
            sendToActuators(action)
            logTelemetry(action)
        }
    }()
    
    // 启动主循环
    loop.Start()
}

// tensorrt_bridge.go
// TensorRT推理引擎的Go封装

/*
#include <stdint.h>
// C接口声明: TensorRT推理
extern int trt_infer(float* input, float* output, int seq_len);
*/
import "C"

type TensorRTEngine struct {
    handle unsafe.Pointer
    inputSize  int
    outputSize int
}

func NewTensorRTEngine(enginePath string, inputSize, outputSize int) (*TensorRTEngine, error) {
    // TensorRT引擎加载（C++侧实现）
    // 返回引擎句柄
    return &TensorRTEngine{
        inputSize:  inputSize,
        outputSize: outputSize,
    }, nil
}

func (e *TensorRTEngine) Infer(input []float32) ([]float32, error) {
    output := make([]float32, e.outputSize)
    // CGO调用TensorRT推理
    ret := C.trt_infer(
        (*C.float)(unsafe.Pointer(&input[0])),
        (*C.float)(unsafe.Pointer(&output[0])),
        C.int(len(input)/e.inputSize),
    )
    if ret != 0 {
        return nil, fmt.Errorf("TRT infer failed: %d", ret)
    }
    return output, nil
}

class ForceControlHead(nn.Module):
    """
    力控输出头 - 支持位置/力矩混合控制
    
    输出:
    - joint_positions: 位置控制关节的目标角度 (14维: 双臂+头部+躯干)
    - joint_torques: 力矩控制关节的目标力矩 (10维: 双腿)
    - grip_force: 手部抓取力 (1维)
    - compliance_gain: 自适应柔顺增益 (4维: 每肢一个)
    """
    
    def __init__(self, d_model: int = 512):
        super().__init__()
        self.shared = nn.Sequential(
            nn.Linear(d_model, 256),
            nn.GELU(),
            nn.Dropout(0.1),
        )
        
        # 位置控制分支
        self.pos_head = nn.Linear(256, 14)
        
        # 力矩控制分支
        self.torque_head = nn.Linear(256, 10)
        
        # 抓取力分支
        self.grip_head = nn.Sequential(
            nn.Linear(256, 32),
            nn.GELU(),
            nn.Linear(32, 1),
            nn.Sigmoid(),  # 归一化到[0,1]
        )
        
        # 自适应柔顺增益
        self.compliance_head = nn.Sequential(
            nn.Linear(256, 16),
            nn.GELU(),
            nn.Linear(16, 4),
            nn.Sigmoid(),  # 归一化到[0,1]
        )
    
    def forward(self, x: Tensor) -> dict:
        feat = self.shared(x)
        
        # 获取最后一帧的输出
        feat_last = feat[:, -1, :]  # (B, 256)
        
        pos = self.pos_head(feat_last)      # (B, 14)
        torque = self.torque_head(feat_last) # (B, 10)
        grip = self.grip_head(feat_last)     # (B, 1)
        comp = self.compliance_head(feat_last) # (B, 4)
        
        return {
            'position': pos,
            'torque': torque,
            'grip_force': grip * 20.0,  # 映射到0-20N
            'compliance': comp,
        }