Figure 03 Humanoid Robot and Helix End-to-End Control System: In-Depth Analysis of Embodied Intelligence Breakthrough

# System 0 Controller Implementation
import torch
import torch.nn as nn
from typing import Tuple, Dict

class System0Controller(nn.Module):
    """
    System 0: Whole-Body Controller
    Neural network controller based on human motion data
    Replaces traditional 109,504 lines of C++ code
    """
    
    def __init__(
        self,
        state_dim: int = 64,  # Whole-body state dimension
        action_dim: int = 41,  # 41 joints
        hidden_dim: int = 512,
        output_freq: int = 1000  # 1kHz output frequency
    ):
        super().__init__()
        
        # Motion encoder
        self.motion_encoder = nn.Sequential(
            nn.Linear(state_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim // 2)
        )
        
        # Balance controller
        self.balance_controller = nn.Sequential(
            nn.Linear(hidden_dim // 2, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim // 4),
            nn.ReLU(),
            nn.Linear(hidden_dim // 4, 6)  # 6D balance control (CoM position, orientation)
        )
        
        # Contact controller
        self.contact_controller = nn.Sequential(
            nn.Linear(hidden_dim // 2, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim // 4),
            nn.ReLU(),
            nn.Linear(hidden_dim // 4, 4)  # 4 end-effector contact forces
        )
        
        # Joint command generator
        self.joint_command_generator = nn.Sequential(
            nn.Linear(hidden_dim // 2 + 6 + 4, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, action_dim * 3)  # position + velocity + torque
        )
        
        self.output_freq = output_freq
        
    def forward(
        self, 
        full_body_state: torch.Tensor,
        base_motion: torch.Tensor
    ) -> Dict[str, torch.Tensor]:
        """
        Forward pass
        
        Args:
            full_body_state: [batch, state_dim] Whole-body joint state
            base_motion: [batch, 6] Base motion (position + orientation)
        
        Returns:
            Joint command dictionary
        """
        # Encode motion state
        encoded = self.motion_encoder(full_body_state)
        
        # Generate balance control
        balance_control = self.balance_controller(encoded)
        
        # Generate contact control
        contact_control = self.contact_controller(encoded)
        
        # Combine all control signals
        combined = torch.cat([encoded, balance_control, contact_control], dim=-1)
        
        # Generate joint commands
        joint_commands = self.joint_command_generator(combined)
        
        # Decompose into position, velocity, torque
        pos_cmd = joint_commands[..., :action_dim]
        vel_cmd = joint_commands[..., action_dim:2*action_dim]
        torque_cmd = joint_commands[..., 2*action_dim:]
        
        return {
            "position": pos_cmd,
            "velocity": vel_cmd,
            "torque": torque_cmd,
            "balance_control": balance_control,
            "contact_forces": contact_control
        }
    
    def compute_reward(
        self,
        predicted_state: torch.Tensor,
        target_state: torch.Tensor,
        balance_error: torch.Tensor,
        contact_error: torch.Tensor
    ) -> torch.Tensor:
        """
        Compute reinforcement learning reward
        
        Reward design principles:
        1. State tracking accuracy
        2. Balance stability
        3. Appropriate contact forces
        """
        # State tracking reward
        state_reward = -torch.norm(predicted_state - target_state, dim=-1)
        
        # Balance reward (lower is better)
        balance_reward = -balance_error
        
        # Contact reward (encourage stable contact)
        contact_reward = -contact_error
        
        # Total reward
        total_reward = (
            1.0 * state_reward + 
            0.5 * balance_reward + 
            0.3 * contact_reward
        )
        
        return total_reward


class WholeBodyController:
    """
    Whole-body controller wrapper
    Provides high-level interface to external systems
    """
    
    def __init__(
        self,
        model_path: str,
        device: str = "cuda"
    ):
        self.device = torch.device(device)
        self.model = System0Controller().to(self.device)
        
        # Load pretrained weights
        checkpoint = torch.load(model_path, map_location=self.device)
        self.model.load_state_dict(checkpoint["model_state"])
        self.model.eval()
        
        # Joint configuration
        self.joint_config = {
            "legs": 12,      # 6 joints per leg
            "torso": 3,      # 3 torso joints
            "arms": 16,      # 8 joints per arm
            "head": 2,       # 2 head joints
            "hands": 16      # 16 hand DOF
        }
        
    def compute_command(
        self,
        joint_positions: torch.Tensor,
        joint_velocities: torch.Tensor,
        base_pose: torch.Tensor,
        foot_contact: torch.Tensor
    ) -> Dict[str, torch.Tensor]:
        """
        Compute joint control commands
        
        Args:
            joint_positions: [41] Current joint positions
            joint_velocities: [41] Current joint velocities
            base_pose: [6] Base pose (x,y,z,roll,pitch,yaw)
            foot_contact: [4] Foot contact state
        
        Returns:
            Control commands
        """
        # Construct full state vector
        full_state = torch.cat([
            joint_positions,
            joint_velocities,
            base_pose,
            foot_contact
        ]).unsqueeze(0)  # Add batch dimension
        
        # Forward pass
        with torch.no_grad():
            commands = self.model(full_state, base_pose.unsqueeze(0))
        
        # Convert to numpy
        return {k: v.squeeze(0).cpu().numpy() for k, v in commands.items()}
    
    def compute_impedance_gains(
        self,
        joint_positions: torch.Tensor,
        target_positions: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Compute impedance control gains
        
        Implement compliant control for environmental interaction adaptability
        """
        position_error = target_positions - joint_positions
        
        # Impedance control: τ = Kp*e + Kd*ė
        Kp_scalar = 50.0
        Kd_scalar = 5.0
        
        return Kp_scalar, Kd_scalar

# System 1 Visuomotor Policy Implementation
import torch
import torch.nn as nn
from typing import Dict, List, Tuple

class VisionTransformer(nn.Module):
    """Vision Encoder - Process multi-view image input"""
    
    def __init__(
        self,
        image_size: int = 224,
        patch_size: int = 16,
        in_channels: int = 3,
        embed_dim: int = 768,
        num_heads: int = 12,
        num_layers: int = 12
    ):
        super().__init__()
        
        # Image patching
        self.num_patches = (image_size // patch_size) ** 2
        self.patch_embed = nn.Conv2d(
            in_channels, 
            embed_dim, 
            kernel_size=patch_size, 
            stride=patch_size
        )
        
        # Position embedding
        self.pos_embed = nn.Parameter(
            torch.zeros(1, self.num_patches, embed_dim)
        )
        
        # Transformer encoder
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=embed_dim,
            nhead=num_heads,
            dim_feedforward=embed_dim * 4,
            dropout=0.1,
            batch_first=True
        )
        self.transformer = nn.TransformerEncoder(
            encoder_layer, 
            num_layers=num_layers
        )
        
        # Output projection
        self.projection = nn.Linear(embed_dim, embed_dim)
        
    def forward(self, images: torch.Tensor) -> torch.Tensor:
        """
        Image feature extraction
        
        Args:
            images: [batch, num_cameras, 3, H, W] Multi-view images
        
        Returns:
            visual_features: [batch, feature_dim] Visual features
        """
        batch_size, num_cameras, C, H, W = images.shape
        
        # Merge all camera views
        images_flat = images.view(-1, C, H, W)
        
        # Extract patch features
        patches = self.patch_embed(images_flat)  # [B*C, embed_dim, H', W']
        patches = patches.flatten(2).transpose(1, 2)  # [B*C, H'*W', embed_dim]
        
        # Add position encoding
        patches = patches + self.pos_embed
        
        # Transformer encoding
        encoded = self.transformer(patches)  # [B*C, H'*W', embed_dim]
        
        # Global pooling
        visual_features = encoded.mean(dim=1)  # [B*C, embed_dim]
        
        # Restore batch dimension and aggregate
        visual_features = visual_features.view(batch_size, num_cameras, -1)
        visual_features = visual_features.mean(dim=1)  # [B, embed_dim]
        
        return self.projection(visual_features)


class System1VisuomotorPolicy(nn.Module):
    """
    System 1: Visuomotor Policy
    Maps pixels directly to whole-body joint targets
    """
    
    def __init__(
        self,
        num_joints: int = 41,
        proprio_dim: int = 64,
        latent_dim: int = 256,
        s2_latent_dim: int = 128
    ):
        super().__init__()
        
        # Vision encoder
        self.vision_encoder = VisionTransformer()
        
        # Proprioception encoder
        self.proprio_encoder = nn.Sequential(
            nn.Linear(proprio_dim, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, 256)
        )
        
        # Fusion layer
        self.fusion = nn.Sequential(
            nn.Linear(768 + 256, 512),
            nn.ReLU(),
            nn.Linear(512, latent_dim)
        )
        
        # S2 conditioning modulation
        self.s2_modulation = nn.Linear(s2_latent_dim, latent_dim)
        
        # Joint target decoder
        self.joint_decoder = nn.Sequential(
            nn.Linear(latent_dim, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, num_joints * 3)  # position + velocity + acceleration
        )
        
        # Action confidence (for uncertainty estimation)
        self.confidence_head = nn.Sequential(
            nn.Linear(latent_dim, 128),
            nn.ReLU(),
            nn.Linear(128, 1),
            nn.Sigmoid()
        )
        
    def forward(
        self,
        images: torch.Tensor,  # [B, 4, 3, 224, 224] head + hands + depth cameras
        proprioception: torch.Tensor,  # [B, 64] Proprioception
        s2_latents: torch.Tensor  # [B, 128] System 2 semantic output
    ) -> Dict[str, torch.Tensor]:
        """
        Forward pass
        
        Args:
            images: Multi-view images
            proprioception: Proprioception state
            s2_latents: System 2 semantic latent
        
        Returns:
            Joint targets and confidence
        """
        # Visual features
        visual_features = self.vision_encoder(images)
        
        # Proprioception features
        proprio_features = self.proprio_encoder(proprioception)
        
        # Feature fusion
        fused = torch.cat([visual_features, proprio_features], dim=-1)
        latent = self.fusion(fused)
        
        # S2 conditioning
        s2_conditioned = latent * (1 + torch.tanh(self.s2_modulation(s2_latents)))
        
        # Generate joint targets
        joint_targets = self.joint_decoder(s2_conditioned)
        num_joints = joint_targets.shape[-1] // 3
        
        # Decompose into position, velocity, acceleration
        positions = joint_targets[..., :num_joints]
        velocities = joint_targets[..., num_joints:2*num_joints]
        accelerations = joint_targets[..., 2*num_joints:]
        
        # Action confidence
        confidence = self.confidence_head(s2_conditioned)
        
        return {
            "positions": positions,
            "velocities": velocities,
            "accelerations": accelerations,
            "confidence": confidence,
            "latent": s2_conditioned
        }
    
    def compute_action(
        self,
        images: torch.Tensor,
        proprioception: torch.Tensor,
        s2_latents: torch.Tensor,
        current_joint_positions: torch.Tensor
    ) -> torch.Tensor:
        """
        Compute final action
        Consider current state and safety constraints
        """
        targets = self.forward(images, proprioception, s2_latents)
        
        # Apply safety constraints
        safe_positions = self.apply_safety_constraints(
            targets["positions"],
            current_joint_positions
        )
        
        # Blend target with safety constraints
        final_action = (
            0.7 * safe_positions + 
            0.3 * targets["positions"] * targets["confidence"]
        )
        
        return final_action
    
    def apply_safety_constraints(
        self,
        target_positions: torch.Tensor,
        current_positions: torch.Tensor
    ) -> torch.Tensor:
        """
        Apply safety constraints
        Prevent joint position sudden changes causing mechanical damage
        """
        # Joint position limits
        joint_limits = torch.tensor([
            # Format: (min, max) for each joint
            # Should be loaded from URDF or config file in actual use
        ] * 41, device=target_positions.device)
        
        # Limit maximum single-step displacement
        max_step = 0.1  # radians
        position_delta = target_positions - current_positions
        clamped_delta = torch.clamp(position_delta, -max_step, max_step)
        
        safe_positions = current_positions + clamped_delta
        
        # Apply joint limits
        safe_positions = torch.clamp(
            safe_positions,
            joint_limits[:, 0],
            joint_limits[:, 1]
        )
        
        return safe_positions


class VisuomotorRunner:
    """
    Visuomotor policy runner
    Responsible for hardware interface and execution frequency control
    """
    
    def __init__(
        self,
        policy: System1VisuomotorPolicy,
        control_freq: int = 200  # 200Hz
    ):
        self.policy = policy
        self.control_freq = control_freq
        self.dt = 1.0 / control_freq
        
        # Image buffer
        self.image_buffer = []
        self.max_buffer_size = 2
        
        # Statistics
        self.inference_times = []
        
    @torch.no_grad()
    def step(
        self,
        images: torch.Tensor,
        proprioception: torch.Tensor,
        s2_latents: torch.Tensor,
        current_positions: torch.Tensor
    ) -> Dict[str, torch.Tensor]:
        """
        Single-step inference
        
        Returns:
            Control commands
        """
        import time
        
        start = time.perf_counter()
        
        # Execute inference
        action = self.policy.compute_action(
            images, proprioception, s2_latents, current_positions
        )
        
        # Record inference time
        inference_time = time.perf_counter() - start
        self.inference_times.append(inference_time)
        
        # Return control commands
        return {
            "joint_targets": action,
            "inference_time_ms": inference_time * 1000
        }

# System 2 Scene Understanding and Goal Planning Implementation
import torch
import torch.nn as nn
from typing import List, Tuple, Dict

class SceneUnderstanding(nn.Module):
    """
    System 2: Scene Understanding Module
    Understand environmental semantics and goals
    """
    
    def __init__(
        self,
        visual_dim: int = 768,
        language_dim: int = 512,
        hidden_dim: int = 1024,
        num_objects: int = 100
    ):
        super().__init__()
        
        # Visual encoder
        self.visual_encoder = nn.Sequential(
            nn.Linear(visual_dim, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim)
        )
        
        # Language encoder
        self.language_encoder = nn.Sequential(
            nn.Linear(language_dim, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim)
        )
        
        # Object detection
        self.object_detector = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, num_objects * 6)  # bbox + class + confidence
        )
        
        # Scene graph construction
        self.scene_graph = nn.MultiheadAttention(
            embed_dim=hidden_dim,
            num_heads=8,
            batch_first=True
        )
        
        # Goal reasoning
        self.goal_reasoner = nn.Sequential(
            nn.Linear(hidden_dim * 2, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 256)  # goal embedding
        )
        
    def forward(
        self,
        visual_features: torch.Tensor,
        language_features: torch.Tensor,
        task_description: str
    ) -> Dict[str, torch.Tensor]:
        """
        Scene understanding forward pass
        """
        # Encode vision and language
        vis_encoded = self.visual_encoder(visual_features)
        lang_encoded = self.language_encoder(language_features)
        
        # Object detection
        object_predictions = self.object_detector(vis_encoded)
        
        # Scene graph attention
        scene_context, _ = self.scene_graph(
            vis_encoded.unsqueeze(0),
            vis_encoded.unsqueeze(0),
            vis_encoded.unsqueeze(0)
        )
        
        # Goal reasoning
        goal_features = torch.cat([vis_encoded, lang_encoded], dim=-1)
        goal_embedding = self.goal_reasoner(goal_features)
        
        return {
            "visual_features": vis_encoded,
            "language_features": lang_encoded,
            "object_predictions": object_predictions,
            "scene_context": scene_context.squeeze(0),
            "goal_embedding": goal_embedding
        }


class GoalDecomposer(nn.Module):
    """
    Goal Decomposer
    Decomposes high-level goals into executable sub-goal sequences
    """
    
    def __init__(
        self,
        goal_dim: int = 256,
        latent_dim: int = 128,
        num_primitive_actions: int = 20
    ):
        super().__init__()
        
        # Goal encoder
        self.goal_encoder = nn.Sequential(
            nn.Linear(goal_dim, 512),
            nn.ReLU(),
            nn.Linear(512, 256),
            nn.ReLU(),
            nn.Linear(256, latent_dim)
        )
        
        # Action vocabulary
        self.action_vocabulary = nn.Parameter(
            torch.randn(num_primitive_actions, latent_dim)
        )
        
        # Sequence decoder
        self.sequence_decoder = nn.GRU(
            input_size=latent_dim,
            hidden_size=256,
            num_layers=2,
            batch_first=True
        )
        
        # Action prediction head
        self.action_head = nn.Sequential(
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, num_primitive_actions)
        )
        
        # Termination prediction head
        self.terminate_head = nn.Sequential(
            nn.Linear(256, 64),
            nn.ReLU(),
            nn.Linear(64, 1),
            nn.Sigmoid()
        )
        
    def forward(
        self,
        goal_embedding: torch.Tensor,
        max_steps: int = 20
    ) -> Tuple[List[int], torch.Tensor]:
        """
        Goal decomposition forward pass
        
        Returns:
            action_sequence: Sub-goal sequence
            terminate_probs: Termination probability at each step
        """
        goal_encoded = self.goal_encoder(goal_embedding)
        
        # Initialize decoder state
        batch_size = goal_embedding.shape[0]
        decoder_hidden = torch.zeros(2, batch_size, 256, device=goal_embedding.device)
        
        # Decode action sequence
        action_sequence = []
        terminate_probs = []
        current_input = goal_encoded.unsqueeze(1)
        
        for step in range(max_steps):
            output, decoder_hidden = self.sequence_decoder(
                current_input, decoder_hidden
            )
            
            # Predict action
            action_logits = self.action_head(output.squeeze(1))
            action_probs = torch.softmax(action_logits, dim=-1)
            action_id = action_probs.argmax(dim=-1)
            action_sequence.append(action_id.item())
            
            # Predict termination
            terminate_prob = self.terminate_head(output.squeeze(1))
            terminate_probs.append(terminate_prob.item())
            
            # Stop decoding if termination probability is high
            if terminate_prob.item() > 0.9:
                break
            
            # Update input using predicted action
            current_input = self.action_vocabulary[action_id].unsqueeze(0)
        
        return action_sequence, torch.tensor(terminate_probs)


class System2Module(nn.Module):
    """
    System 2: Complete Module
    Integrates scene understanding and goal decomposition
    """
    
    def __init__(self):
        super().__init__()
        
        self.scene_understanding = SceneUnderstanding()
        self.goal_decomposer = GoalDecomposer()
        
        # Latent space projection (passed to System 1)
        self.latent_projection = nn.Sequential(
            nn.Linear(256 + 128, 256),
            nn.ReLU(),
            nn.Linear(256, 128)
        )
        
    def forward(
        self,
        visual_features: torch.Tensor,
        language_features: torch.Tensor,
        task_description: str
    ) -> Tuple[torch.Tensor, List[int]]:
        """
        System 2 forward pass
        """
        # Scene understanding
        scene_output = self.scene_understanding(
            visual_features, language_features, task_description
        )
        
        # Goal decomposition
        action_sequence, terminate_probs = self.goal_decomposer(
            scene_output["goal_embedding"]
        )
        
        # Generate latent to pass to System 1
        latent = self.latent_projection(
            torch.cat([
                scene_output["goal_embedding"],
                scene_output["scene_context"]
            ], dim=-1)
        )
        
        return latent, action_sequence
    
    def get_semantic_latents(
        self,
        visual_features: torch.Tensor,
        language_features: torch.Tensor,
        task: str
    ) -> torch.Tensor:
        """
        Get semantic latents (for System 1 conditioning)
        """
        latent, _ = self.forward(visual_features, language_features, task)
        return latent

# Figure 03 Hardware Configuration Class
from dataclasses import dataclass
from typing import Dict, List

@dataclass
class Figure03Specs:
    """Figure 03 Hardware Specifications"""
    
    # Size and Weight
    height_cm: float = 168.0
    weight_kg: float = 60.0
    mass_reduction_percent: float = 9.0  # 9% reduction vs 02
    
    # DOF Configuration
    total_dof: int = 41
    joint_config: Dict[str, int] = None
    
    # Dexterous Hand Specifications
    hand_dof: int = 16
    tactile_sensitivity_grams: float = 3.0  # Can detect 3-gram pressure
    
    # Battery System
    battery_capacity_kwh: float = 2.3
    runtime_hours: float = 5.0
    charging_power_kw: float = 2.0  # Wireless charging power
    battery_swap_seconds: float = 30.0  # Hot-swap time
    
    # Sensor Configuration
    head_cameras: int = 2
    head_fov_increase_percent: float = 60.0
    frame_rate_improvement: float = 2.0
    latency_reduction_percent: float = 75.0
    
    # Data Transmission
    wireless_bandwidth_gbps: float = 10.0  # mmWave
    
    def __post_init__(self):
        if self.joint_config is None:
            self.joint_config = {
                "left_leg": 6,
                "right_leg": 6,
                "torso": 3,
                "left_arm": 8,
                "right_arm": 8,
                "head": 2,
                "left_hand": 4,  # thumb + 4 fingers
                "right_hand": 4
            }
    
    def get_total_mass_breakdown(self) -> Dict[str, float]:
        """Get mass distribution"""
        return {
            "body": 25.0,  # Main body
            "arms": 8.0 * 2,  # Both arms
            "legs": 7.0 * 2,  # Both legs
            "head": 3.0,  # Head
            "battery": 8.0,  # Battery
            "electronics": 4.0  # Electronics
        }


class ActuatorSystem:
    """
    Actuator System
    Figure 03 proprietary actuator configuration
    """
    
    def __init__(self):
        self.actuator_config = {
            # Leg actuators (high torque)
            "leg_joint": {
                "max_torque_nm": 150,
                "max_velocity_rpm": 60,
                "weight_kg": 1.2,
                "reduction_ratio": 100
            },
            # Arm actuators (medium torque, high speed)
            "arm_joint": {
                "max_torque_nm": 80,
                "max_velocity_rpm": 120,
                "weight_kg": 0.8,
                "reduction_ratio": 80
            },
            # Dexterous hand actuators (fine control)
            "hand_joint": {
                "max_torque_nm": 5,
                "max_velocity_rpm": 300,
                "weight_kg": 0.1,
                "reduction_ratio": 50
            }
        }
        
    def get_speed_improvement(self) -> float:
        """Speed improvement vs previous generation"""
        return 2.0  # 2x speed improvement
        
    def get_torque_density(self) -> float:
        """Torque density (Nm/kg)"""
        return 125 / 1.2  # ~104 Nm/kg

# Sim-to-Real Training Framework
import numpy as np
from typing import Dict, List, Tuple
import mujoco
import torch
import torch.nn as nn

class SimToRealTrainer:
    """
    Simulation-to-Reality Transfer Trainer
    Train policies using large-scale simulation environments
    """
    
    def __init__(
        self,
        num_envs: int = 200000,
        sim_time_step: float = 0.001,
        control_time_step: float = 0.01
    ):
        self.num_envs = num_envs
        self.sim_dt = sim_time_step
        self.control_dt = control_time_step
        
        # Create simulation environment
        self.sim_env = self._create_sim_env()
        
        # Domain randomization parameters
        self.domain_randomization = {
            "friction": [0.5, 1.5],
            "mass": [0.8, 1.2],
            "delay": [0.0, 0.05],
            "noise": [0.0, 0.1]
        }
        
    def _create_sim_env(self):
        """Create MuJoCo simulation environment"""
        # Load Figure 03 model
        model = mujoco.MjModel.from_xml_path("figure_03.xml")
        data = mujoco.MjData(model)
        
        return {"model": model, "data": data}
    
    def randomize_domain(self):
        """Apply domain randomization"""
        # Friction randomization
        friction = np.random.uniform(*self.domain_randomization["friction"])
        self.sim_env["model"].geom_friction[:] = friction
        
        # Mass randomization
        mass_scale = np.random.uniform(*self.domain_randomization["mass"])
        for i in range(1, self.sim_env["model"].nbody):
            self.sim_env["model"].body_mass[i] *= mass_scale
        
    def collect_rollout(
        self,
        policy: nn.Module,
        num_steps: int = 1000
    ) -> Dict[str, np.ndarray]:
        """
        Collect simulation data rollout
        
        Returns:
            Experience data dictionary
        """
        observations = []
        actions = []
        rewards = []
        dones = []
        
        # Initialize environment
        self.sim_env["data"].reset()
        self.randomize_domain()
        
        for step in range(num_steps):
            # Get observation
            obs = self._get_observation()
            observations.append(obs)
            
            # Policy inference
            with torch.no_grad():
                action = policy(torch.tensor(obs).float().unsqueeze(0))
                action = action.squeeze(0).numpy()
            
            # Apply action
            self.sim_env["data"].ctrl[:] = action
            mujoco.mj_step(
                self.sim_env["model"], 
                self.sim_env["data"], 
                int(self.control_dt / self.sim_dt)
            )
            actions.append(action)
            
            # Compute reward
            reward = self._compute_reward()
            rewards.append(reward)
            
            # Check termination
            done = self._check_termination()
            dones.append(done)
            
            if done:
                self.sim_env["data"].reset()
                self.randomize_domain()
        
        return {
            "observations": np.array(observations),
            "actions": np.array(actions),
            "rewards": np.array(rewards),
            "dones": np.array(dones)
        }
    
    def _get_observation(self) -> np.ndarray:
        """Get current observation"""
        data = self.sim_env["data"]
        
        # Joint positions
        qpos = data.qpos[:self.sim_env["model"].nq]
        
        # Joint velocities
        qvel = data.qvel[:self.sim_env["model"].nv]
        
        # Tactile sensors (simulated)
        tactiles = data.sensordata[:16]  # 16 fingertip sensors
        
        # Merge observation
        obs = np.concatenate([qpos, qvel, tactiles])
        
        # Add noise
        noise_scale = np.random.uniform(*self.domain_randomization["noise"])
        obs += np.random.randn(len(obs)) * noise_scale
        
        return obs
    
    def _compute_reward(self) -> float:
        """Compute reward function"""
        data = self.sim_env["data"]
        
        # Task completion reward
        task_reward = 0.0
        
        # Balance reward
        balance_reward = -0.1 * np.sum(data.qacc[:]**2)
        
        # Energy penalty
        energy_penalty = -0.01 * np.sum(data.ctrl[:]**2)
        
        # Task-specific reward (sorting scenario)
        if hasattr(data, 'object_placed'):
            task_reward = 1.0 if data.object_placed else 0.0
        
        return task_reward + balance_reward + energy_penalty
    
    def _check_termination(self) -> bool:
        """Check if terminated"""
        data = self.sim_env["data"]
        
        # Fall detection
        if data.body("torso").xpos[2] < 0.5:
            return True
        
        # Joint limit violation
        if np.any(np.abs(data.qpos[:]) > np.pi * 2):
            return True
        
        return False
    
    def train_ppo(
        self,
        policy: nn.Module,
        value_net: nn.Module,
        num_iterations: int = 1000,
        steps_per_iteration: int = 10000
    ):
        """
        PPO algorithm training
        """
        optimizer_policy = torch.optim.Adam(policy.parameters(), lr=3e-4)
        optimizer_value = torch.optim.Adam(value_net.parameters(), lr=1e-3)
        
        for iteration in range(num_iterations):
            # Collect data
            rollout = self.collect_rollout(policy, steps_per_iteration)
            
            # Compute advantage function
            advantages, returns = self._compute_gae(
                rollout["rewards"], 
                rollout["dones"]
            )
            
            # PPO update
            for epoch in range(10):  # PPO epochs
                # Policy loss
                actions = torch.tensor(rollout["actions"])
                obs = torch.tensor(rollout["observations"])
                log_probs_old = policy.get_log_prob(obs, actions)
                
                # New policy
                log_probs_new = policy.get_log_prob(obs, actions)
                
                # PPO loss
                ratio = torch.exp(log_probs_new - log_probs_old)
                surr1 = ratio * torch.tensor(advantages)
                surr2 = torch.clamp(
                    ratio, 
                    1 - 0.2, 
                    1 + 0.2
                ) * torch.tensor(advantages)
                
                policy_loss = -torch.min(surr1, surr2).mean()
                
                # Value function loss
                values_pred = value_net(obs).squeeze()
                value_loss = nn.MSELoss()(values_pred, torch.tensor(returns))
                
                # Update
                optimizer_policy.zero_grad()
                policy_loss.backward()
                optimizer_policy.step()
                
                optimizer_value.zero_grad()
                value_loss.backward()
                optimizer_value.step()
            
            print(f"Iteration {iteration}, Policy Loss: {policy_loss.item():.4f}")

# Logistics Sorting Application Implementation
class LogisticsSortingTask:
    """
    Logistics Sorting Task
    Figure 03's core commercial application scenario
    """
    
    def __init__(self, figure_robot):
        self.robot = figure_robot
        self.current_zone = "inbound"
        self.sorting_stats = {
            "total": 0,
            "success": 0,
            "failed": 0,
            "items_per_hour": []
        }
    
    def scan_package(self) -> Dict:
        """
        Scan and identify package
        """
        # Get images
        images = self.robot.get_head_cameras()
        
        # Use vision model to identify
        package_info = self.vision_model.detect_package(images)
        
        return {
            "id": package_info["tracking_id"],
            "destination": package_info["destination_zone"],
            "weight": package_info["weight_kg"],
            "dimensions": package_info["bbox_3d"],
            "fragile": package_info["fragile_flag"]
        }
    
    def grasp_package(self, package_info: Dict) -> bool:
        """
        Grasp package
        Adjust grasping strategy based on package characteristics
        """
        # Adjust grasping force based on weight
        if package_info["weight"] > 10:
            # Heavy items: two-hand grasp, larger contact area
            grasp_force = 50  # Newtons
        elif package_info["fragile"]:
            # Fragile items: minimum force, precise control
            grasp_force = 5  # Newtons (just enough to lift)
        else:
            # Normal packages
            grasp_force = 20  # Newtons
        
        # Execute grasp
        success = self.robot.grasp(
            target_pose=package_info["position"],
            force=grasp_force,
            approach_height=0.1
        )
        
        return success
    
    def sort_to_destination(self, destination: str) -> bool:
        """
        Sort package to destination
        """
        # Navigate to destination zone
        self.robot.navigate_to_zone(destination)
        
        # Place package
        success = self.robot.place(
            target_zone=destination,
            release_force=5  # Gentle placement
        )
        
        return success
    
    def run_sorting_loop(self):
        """
        Run sorting loop
        """
        while True:
            # 1. Scan package
            package = self.scan_package()
            
            # 2. Grasp
            grasp_success = self.grasp_package(package)
            if not grasp_success:
                self.sorting_stats["failed"] += 1
                continue
            
            # 3. Sort
            sort_success = self.sort_to_destination(package["destination"])
            
            # 4. Record statistics
            self.sorting_stats["total"] += 1
            if sort_success:
                self.sorting_stats["success"] += 1
            else:
                self.sorting_stats["failed"] += 1
            
            # 5. Periodic report
            if self.sorting_stats["total"] % 100 == 0:
                self.report_stats()
    
    def report_stats(self):
        """Report sorting statistics"""
        total = self.sorting_stats["total"]
        success = self.sorting_stats["success"]
        
        print(f"""
        === Sorting Statistics ===
        Total: {total}
        Success: {success}
        Failed: {total - success}
        Success Rate: {100 * success / total if total > 0 else 0:.2f}%
        """)

# Industrial Assembly Application
class IndustrialAssemblyTask:
    """
    Industrial Assembly Task
    Figure 03's application in manufacturing
    """
    
    def __init__(self, figure_robot):
        self.robot = figure_robot
        self.assembly_sequence = []
    
    def visual_servoing(
        self, 
        target_pose: np.ndarray
    ) -> np.ndarray:
        """
        Visual servoing control
        Vision-based feedback for precise pose control
        """
        # Get current image
        images = self.robot.get_palm_cameras()
        
        # Detect target
        target_in_image = self.vision_model.detect_target(images, target_pose)
        
        # Compute image Jacobian
        jacobian = self.compute_image_jacobian(target_in_image)
        
        # Compute control increment
        position_error = target_pose - self.robot.get_end_effector_pose()
        velocity = position_error * 0.5  # Proportional control
        
        return velocity
    
    def precision_insertion(
        self,
        peg: Dict,
        hole: Dict
    ) -> bool:
        """
        Precision insertion
        Typical assembly task
        """
        # 1. Align
        aligned = self.align_peg_hole(peg, hole)
        if not aligned:
            return False
        
        # 2. Approach
        approach_pose = hole["position"] + np.array([0, 0, 0.05])
        self.robot.move_to(approach_pose, velocity=0.01)
        
        # 3. Insert (use force control)
        insertion_force = self.robot.force_control(
            direction=np.array([0, 0, -1]),
            target_force=2.0,  # 2 Newtons
            max_displacement=0.1
        )
        
        # 4. Verify
        force_sensor = self.robot.get_force_sensor()
        if np.linalg.norm(insertion_force) > 10:
            return False  # Insertion resistance too high
        
        return True
    
    def dual_arm_coordination(
        self,
        left_task: Dict,
        right_task: Dict
    ):
        """
        Dual-arm coordination task
        Complex assembly requires both arms to work together
        """
        # Define coordination frame
        relative_pose = self.compute_relative_pose(left_task, right_task)
        
        # Synchronized control
        left_vel = self.visual_servoing(left_task["target"])
        right_vel = self.visual_servoing(right_task["target"])
        
        # Add coordination constraint
        coordinated_left_vel = left_vel + 0.5 * relative_pose
        coordinated_right_vel = right_vel - 0.5 * relative_pose
        
        # Execute coordinated motion
        self.robot.left_arm.set_velocity(coordinated_left_vel)
        self.robot.right_arm.set_velocity(coordinated_right_vel)