Galaxy General AstraBrain-WBC 0.5: Deep Technical Analysis of the World's First Humanoid Robot General-Purpose Cerebellum

# Traditional QP-based WBC solver (simplified)
import numpy as np
from scipy.optimize import minimize

def traditional_wbc(target_joint_positions, current_joint_positions, 
                    joint_limits, dt=0.001):
    """
    Traditional optimization-based WBC solver
    """
    n_joints = len(current_joint_positions)
    
    def objective(delta_q):
        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):
        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 solver average latency: 3-5ms, far from real-time requirements
    return result.x / dt  # return joint velocity commands

import math
from typing import Optional, Tuple

class AstraBrainCausalTransformer:
    """
    AstraBrain-WBC 0.5 Causal Transformer Core Implementation
    Parameters: 80.4M
    Input: obs_dim=128 (joint angles/velocities/IMU/foot forces)
           cmd_dim=7 (target position/orientation/grip force)
    Output: act_dim=29 (joint angle commands)
    """
    
    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
        
        # Input projection
        self.obs_proj = Linear(128 + 7, d_model)
        self.time_enc = SinusoidalTimeEncoding(d_model, max_seq_len)
        
        # 8-layer Transformer
        self.layers = nn.ModuleList([
            TransformerBlock(d_model, nhead, dropout=0.1)
            for _ in range(num_layers)
        ])
        
        # Output head
        self.output_head = nn.Sequential(
            nn.Linear(d_model, 256),
            nn.GELU(),
            nn.Linear(256, 128),
            nn.GELU(),
            nn.Linear(128, 29),
        )
        
        self._init_weights()
    
    def forward(self, obs, cmd, timesteps):
        B, T = obs.shape[:2]
        x = torch.cat([obs, cmd], dim=-1)
        x = self.obs_proj(x)
        x = x + self.time_enc(timesteps)
        
        for layer in self.layers:
            x = layer(x, causal_mask=True)
        
        actions = self.output_head(x)
        return actions


class SinusoidalTimeEncoding(nn.Module):
    """Sinusoidal time encoding for continuous temporal awareness"""
    
    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):
        return self.pe[:, timesteps, :]

class PPOExpertTrainer:
    """
    Parallel training of 384 PPO expert policies
    """
    
    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):
        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, batch, 
                      gamma=0.99, lam=0.95, clip_epsilon=0.2):
        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 advantage estimation
        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 clipped update
        old_log_probs = actor.get_log_prob(states, actions).detach()
        
        for _ in range(10):
            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

class DAggerDistillation:
    """
    DAgger: Interactive Imitation Learning Distillation
    Goal: Distill 384 PPO experts into a single student model
    """
    
    def __init__(self, experts, student, expert_selector, replay_buffer):
        self.experts = experts
        self.student = student
        self.selector = expert_selector
        self.buffer = replay_buffer
    
    def dagger_iteration(self, env, num_steps=10000):
        obs, info = env.reset()
        cmd = get_current_command(info)
        
        dataset_states, dataset_actions = [], []
        
        for step in range(num_steps):
            with torch.no_grad():
                student_action = self.student(obs, cmd)
            
            uncertainty = self.estimate_uncertainty(self.student, obs, cmd)
            
            if uncertainty > 0.3:
                expert_id = self.selector(obs, cmd)
                expert = self.experts[expert_id]
                expert_action = expert(obs.unsqueeze(0)).squeeze(0)
                
                dataset_states.append(torch.cat([obs, cmd]))
                dataset_actions.append(expert_action)
                
                next_obs, reward, done, truncated, info = env.step(expert_action)
            else:
                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)
        
        new_states = torch.stack(dataset_states)
        new_actions = torch.stack(dataset_actions)
        self.buffer.add(new_states, new_actions)
        self.train_student_on_buffer()

class DomainRandomizationWrapper:
    def randomize_physics(self, env):
        # Mass randomization (±50%)
        for body in env.bodies:
            body.mass *= np.random.uniform(0.5, 1.5)
        
        # Friction randomization (10x range)
        env.set_friction(np.random.uniform(0.1, 1.0))
        
        # Joint damping randomization
        for joint in env.joints:
            joint.damping *= np.random.uniform(0.5, 2.0)
        
        # Communication delay randomization (critical!)
        env.action_delay = np.random.randint(1, 5)
        
        # Sensor noise randomization
        env.obs_noise_std = np.random.uniform(0.0, 0.05)
        
        return env

// main_control_loop.go
package main

import (
    "log"
    "sync"
    "time"
    "unsafe"
)

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

type ControlConfig struct {
    ControlFreq   int
    ObsDim        int
    CmdDim        int
    ActDim        int
    SeqLen        int
}

type ControlLoop struct {
    cfg       ControlConfig
    state     *RobotState
    cmd       chan ControlCommand
    actionOut chan []float32
    seqBuffer [][]float32
    ticker    *time.Ticker
}

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),
    }
}

func (cl *ControlLoop) Start() {
    log.Printf("[AstraBrain] Control loop started @ %d Hz", cl.cfg.ControlFreq)
    
    for range cl.ticker.C {
        cl.readSensorData()
        
        var cmd ControlCommand
        select {
        case cmd = <-cl.cmd:
        default:
            cmd = cl.lastCmd
        }
        cl.lastCmd = cmd
        
        obs := cl.buildObservation(cmd)
        action := cl.modelInfer(obs)
        
        if !cl.safetyCheck(action) {
            action = cl.getFallbackAction()
        }
        
        cl.actionOut <- action
    }
}

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)
    
    ret := int(C.astrabrain_infer(
        (*C.float)(unsafe.Pointer(&flatObs[0])),
        nil,
        (*C.float)(unsafe.Pointer(&output[0])),
        C.int(seqLen),
    ))
    
    if ret != 0 {
        return make([]float32, cl.cfg.ActDim)
    }
    return output
}

func main() {
    cfg := ControlConfig{
        ControlFreq: 1000,
        ObsDim:      128,
        CmdDim:      7,
        ActDim:      29,
        SeqLen:      64,
    }
    
    loop := NewControlLoop(cfg)
    
    go func() {
        for action := range loop.actionOut {
            sendToActuators(action)
        }
    }()
    
    loop.Start()
}

class ForceControlHead(nn.Module):
    """
    Force control output head supporting position/torque hybrid control
    """
    
    def __init__(self, d_model=512):
        super().__init__()
        self.shared = nn.Sequential(
            nn.Linear(d_model, 256),
            nn.GELU(),
            nn.Dropout(0.1),
        )
        
        self.pos_head = nn.Linear(256, 14)
        self.torque_head = nn.Linear(256, 10)
        
        self.grip_head = nn.Sequential(
            nn.Linear(256, 32),
            nn.GELU(),
            nn.Linear(32, 1),
            nn.Sigmoid(),
        )
        
        self.compliance_head = nn.Sequential(
            nn.Linear(256, 16),
            nn.GELU(),
            nn.Linear(16, 4),
            nn.Sigmoid(),
        )
    
    def forward(self, x):
        feat = self.shared(x)
        feat_last = feat[:, -1, :]
        
        return {
            'position': self.pos_head(feat_last),
            'torque': self.torque_head(feat_last),
            'grip_force': self.grip_head(feat_last) * 20.0,
            'compliance': self.compliance_head(feat_last),
        }