OpenAI Robotics: The Next Frontier in Artificial Intelligence

# OpenAI Robotics Research Team Structure
class ResearchTeam:
    def __init__(self):
        self.leadership = {
            "CEO": "Sam Altman",
            "Robotics Lead": "Aditya Ramesh",
            "VP Engineering": "Recruiting...",
        }
        
        self.divisions = {
            "Perception": {
                "focus": "Visual understanding, object detection",
                "lead": "Hiring",
            },
            "Motion Planning": {
                "focus": "Pathfinding, trajectory optimization",
                "lead": "Hiring",
            },
            "Manipulation": {
                "focus": "Grasping, object interaction",
                "lead": "Hiring",
            },
            "Safety": {
                "focus": "Human-robot interaction, fail-safes",
                "lead": "Hiring",
            },
            "Hardware Integration": {
                "focus": "Sensor-actuator coordination",
                "lead": "Hiring",
            }
        }
        
        self.open_positions = [
            "Senior Robotics Engineer",
            "Motion Planning Specialist",
            "Computer Vision Engineer",
            "Reinforcement Learning Researcher",
            "Hardware Integration Engineer",
            "Safety Systems Engineer",
        ]

"""
OpenAI Robotics Data Flow
==========================
This module implements the data pipeline from perception to action.
"""

from dataclasses import dataclass
from typing import List, Optional, Tuple
import numpy as np

@dataclass
class RobotObservation:
    """Raw sensor observations from the robot."""
    rgb_image: np.ndarray          # Shape: (H, W, 3)
    depth_image: np.ndarray        # Shape: (H, W)
    proprioception: np.ndarray     # Joint angles, forces
    timestamp: float
    
@dataclass
class WorldState:
    """High-level world representation."""
    objects: List[Object3D]
    robot_pose: Pose3D
    task_progress: float
    confidence: float
    
@dataclass
class ActionCommand:
    """Action to be executed by the robot."""
    joint_targets: np.ndarray
    gripper_command: str  # "open", "close", "grasp"
    duration: float       # seconds
    safety_level: str    # "normal", "cautious", "emergency"


class DataPipeline:
    """
    Main data processing pipeline for robot perception and control.
    """
    
    def __init__(self, config: dict):
        self.vlm_model = self._load_vlm(config["vlm_checkpoint"])
        self.world_model = self._load_world_model(config["world_model_checkpoint"])
        self.policy_network = self._load_policy(config["policy_checkpoint"])
        
    def process_observation(self, obs: RobotObservation) -> WorldState:
        """
        Convert raw sensor data to high-level world state.
        
        Pipeline:
        1. Preprocess images (resize, normalize)
        2. Extract features using VLM encoder
        3. Query world model for state estimation
        4. Return structured world state
        """
        # Step 1: Preprocessing
        rgb = self._preprocess_image(obs.rgb_image)
        depth = self._preprocess_depth(obs.depth_image)
        
        # Step 2: Feature extraction via Vision Language Model
        visual_features = self.vlm_model.encode_vision(rgb)
        depth_features = self.vlm_model.encode_depth(depth)
        
        # Step 3: Proprioception encoding
        proprio_features = self._encode_proprioception(obs.proprioception)
        
        # Step 4: World state estimation
        world_state = self.world_model.estimate_state(
            visual_features=visual_features,
            depth_features=depth_features,
            proprio_features=proprio_features
        )
        
        return world_state
    
    def generate_action(
        self, 
        world_state: WorldState, 
        task_description: str
    ) -> ActionCommand:
        """
        Generate robot action from world state and task.
        
        Uses a Vision-Language-Action (VLA) model architecture.
        """
        # Encode task in natural language
        task_embedding = self.policy_network.encode_task(task_description)
        
        # Cross-attention between world state and task
        action_latent = self.policy_network.forward(
            state=world_state,
            task=task_embedding
        )
        
        # Decode action parameters
        joint_targets = self.policy_network.decode_joints(action_latent)
        gripper_cmd = self.policy_network.decode_gripper(action_latent)
        
        # Safety check
        if self._safety_violation_check(world_state, joint_targets):
            return ActionCommand(
                joint_targets=self._safe_joint_positions(),
                gripper_command="hold",
                duration=0.1,
                safety_level="emergency"
            )
        
        return ActionCommand(
            joint_targets=joint_targets,
            gripper_command=gripper_cmd,
            duration=0.5,
            safety_level="normal"
        )

"""
Vision-Language-Action (VLA) Model Implementation
==================================================
A unified model for robot perception, reasoning, and control.
"""

import torch
import torch.nn as nn
from transformers import AutoModel, AutoTokenizer

class VLAModel(nn.Module):
    """
    Vision-Language-Action Model for Robot Control
    
    Architecture:
    - Vision Encoder: Processes camera inputs
    - Language Encoder: Processes task descriptions  
    - Fusion Module: Combines vision and language features
    - Action Decoder: Generates motor commands
    """
    
    def __init__(
        self,
        vision_model_name: str = "openai/clip-vit-large-patch14",
        language_model_name: str = "gpt-4",
        action_dim: int = 14,  # 7 joints + 7 gripper
        hidden_dim: int = 512
    ):
        super().__init__()
        
        # Vision encoder
        self.vision_encoder = AutoModel.from_pretrained(vision_model_name)
        vision_dim = self.vision_encoder.config.hidden_size
        
        # Language encoder
        self.language_encoder = AutoModel.from_pretrained(language_model_name)
        self.tokenizer = AutoTokenizer.from_pretrained(language_model_name)
        language_dim = self.language_encoder.config.hidden_size
        
        # Projection layers to common space
        self.vision_projection = nn.Linear(vision_dim, hidden_dim)
        self.language_projection = nn.Linear(language_dim, hidden_dim)
        
        # Cross-attention fusion
        self.cross_attention = nn.MultiheadAttention(
            embed_dim=hidden_dim,
            num_heads=8,
            dropout=0.1
        )
        
        # Action decoder heads
        self.joint_head = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, action_dim),
            nn.Tanh()  # Normalize to [-1, 1]
        )
        
        self.gripper_head = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Linear(hidden_dim // 2, 1),
            nn.Sigmoid()  # Probability of closing
        )
        
    def forward(
        self,
        images: torch.Tensor,           # (B, 3, 224, 224)
        task_text: list[str],           # List of task descriptions
        return_hidden: bool = False
    ) -> dict[str, torch.Tensor]:
        """
        Forward pass of the VLA model.
        
        Args:
            images: Batch of RGB images from robot cameras
            task_text: Natural language task descriptions
            return_hidden: Whether to return intermediate features
            
        Returns:
            Dictionary containing:
            - joint_targets: Target joint angles (B, 7)
            - gripper_prob: Gripper closing probability (B, 1)
            - hidden_features: Intermediate representations (optional)
        """
        batch_size = images.shape[0]
        
        # Vision encoding
        vision_outputs = self.vision_encoder(images)
        vision_features = vision_outputs.last_hidden_state
        vision_features = self.vision_projection(vision_features)
        
        # Language encoding
        tokens = self.tokenizer(
            task_text,
            padding=True,
            truncation=True,
            max_length=128,
            return_tensors="pt"
        ).to(images.device)
        
        language_outputs = self.language_encoder(**tokens)
        language_features = language_outputs.last_hidden_state
        language_features = self.language_projection(language_features)
        
        # Cross-modal fusion via cross-attention
        # Query: vision features, Key/Value: language features
        fused_features, attention_weights = self.cross_attention(
            query=vision_features.mean(dim=1, keepdim=True),  # (B, 1, H)
            key=language_features,                            # (B, L, H)
            value=language_features
        )
        fused_features = fused_features.squeeze(1)  # (B, H)
        
        # Action decoding
        joint_targets = self.joint_head(fused_features)
        gripper_prob = self.gripper_head(fused_features)
        
        outputs = {
            "joint_targets": joint_targets,
            "gripper_prob": gripper_prob,
        }
        
        if return_hidden:
            outputs["hidden_features"] = fused_features
            outputs["attention_weights"] = attention_weights
            
        return outputs
    
    def compute_loss(
        self,
        predictions: dict,
        targets: dict,
        attention_mask: Optional[torch.Tensor] = None
    ) -> dict[str, float]:
        """
        Compute training loss for the VLA model.
        
        Args:
            predictions: Model outputs from forward()
            targets: Ground truth labels
            
        Returns:
            Dictionary of losses and metrics
        """
        # Joint position loss (MSE)
        joint_loss = nn.functional.mse_loss(
            predictions["joint_targets"],
            targets["joint_targets"]
        )
        
        # Gripper loss (Binary cross-entropy)
        gripper_loss = nn.functional.binary_cross_entropy(
            predictions["gripper_prob"],
            targets["gripper_action"].float()
        )
        
        # Combined loss with weighting
        total_loss = joint_loss + 0.5 * gripper_loss
        
        # Additional metrics
        with torch.no_grad():
            joint_accuracy = (
                torch.abs(predictions["joint_targets"] - targets["joint_targets"]) < 0.1
            ).float().mean()
            
            gripper_accuracy = (
                (predictions["gripper_prob"] > 0.5).float() == targets["gripper_action"]
            ).float().mean()
        
        return {
            "total_loss": total_loss.item(),
            "joint_loss": joint_loss.item(),
            "gripper_loss": gripper_loss.item(),
            "joint_accuracy": joint_accuracy.item(),
            "gripper_accuracy": gripper_accuracy.item()
        }


class VLAInference:
    """
    Optimized inference wrapper for the VLA model.
    Supports batching, caching, and hardware acceleration.
    """
    
    def __init__(
        self,
        model: VLAModel,
        device: str = "cuda",
        batch_size: int = 1
    ):
        self.model = model.to(device).eval()
        self.device = device
        self.batch_size = batch_size
        
        # Preallocate buffers
        self.image_buffer = torch.zeros(
            (batch_size, 3, 224, 224),
            dtype=torch.float32,
            device=device
        )
        
    @torch.no_grad()
    def predict(
        self,
        images: np.ndarray,
        task_description: str
    ) -> Tuple[np.ndarray, float]:
        """
        Run inference on robot observations.
        
        Args:
            images: RGB image array (H, W, 3) or batch (B, H, W, 3)
            task_description: Natural language task
            
        Returns:
            joint_targets: Target joint angles
            inference_time: Time taken for inference (ms)
        """
        import time
        start_time = time.perf_counter()
        
        # Preprocess images
        if images.ndim == 3:
            images = images[np.newaxis, ...]
            
        # Normalize to [0, 1]
        images = images.astype(np.float32) / 255.0
        
        # Convert to tensor and normalize with ImageNet stats
        mean = np.array([0.485, 0.456, 0.406])
        std = np.array([0.229, 0.224, 0.225])
        images = (images - mean) / std
        
        # Transpose to (B, C, H, W)
        images = np.transpose(images, (0, 3, 1, 2))
        
        # Move to device
        images_tensor = torch.from_numpy(images).to(self.device)
        
        # Run model
        outputs = self.model(
            images=images_tensor,
            task_text=[task_description] * len(images)
        )
        
        # Convert back to numpy
        joint_targets = outputs["joint_targets"].cpu().numpy()
        
        inference_time = (time.perf_counter() - start_time) * 1000
        
        return joint_targets[0], inference_time

"""
World Model Implementation
===========================
Generative models for simulating physical environments.
"""

class WorldModel:
    """
    World model for physical environment simulation.
    
    Key capabilities:
    1. State encoding: Compress world state into latent representation
    2. Dynamics prediction: Predict next state given actions
    3. Observation generation: Render simulated observations
    4. Counterfactual reasoning: "What if I did X instead?"
    """
    
    def __init__(self, config: WorldModelConfig):
        self.state_encoder = StateEncoder(config.state_dim)
        self.dynamics_model = DynamicsNetwork(
            input_dim=config.state_dim + config.action_dim,
            hidden_dim=config.hidden_dim,
            output_dim=config.state_dim
        )
        self.observation_decoder = ObservationDecoder(
            input_dim=config.state_dim,
            output_channels=3  # RGB
        )
        
    def encode_state(self, observations: dict) -> torch.Tensor:
        """
        Encode multi-modal observations into latent state.
        """
        visual_features = self.state_encoder.encode_vision(observations["image"])
        proprio_features = self.state_encoder.encode_proprio(observations["proprio"])
        
        # Fusion
        fused = torch.cat([visual_features, proprio_features], dim=-1)
        state = self.state_encoder.fusion_layer(fused)
        
        return state
    
    def predict_next_state(
        self,
        current_state: torch.Tensor,
        action: torch.Tensor
    ) -> torch.Tensor:
        """
        Predict the next state given current state and action.
        """
        state_action = torch.cat([current_state, action], dim=-1)
        next_state = self.dynamics_model(state_action)
        return next_state
    
    def rollout(
        self,
        initial_state: torch.Tensor,
        action_sequence: torch.Tensor,
        horizon: int
    ) -> list[torch.Tensor]:
        """
        Simulate a sequence of actions and return all states.
        """
        states = [initial_state]
        current_state = initial_state
        
        for t in range(horizon):
            action = action_sequence[:, t]
            current_state = self.predict_next_state(current_state, action)
            states.append(current_state)
            
        return states

"""
Reinforcement Learning for Robot Control
========================================
Training pipeline using PPO with world model simulation.
"""

class RobotRLTrainer:
    """
    PPO-based trainer for robot manipulation tasks.
    """
    
    def __init__(
        self,
        policy: VLAModel,
        value_network: nn.Module,
        world_model: WorldModel,
        config: RLConfig
    ):
        self.policy = policy
        self.value_network = value_network
        self.world_model = world_model
        self.config = config
        
        self.optimizer = torch.optim.AdamW(
            list(policy.parameters()) + list(value_network.parameters()),
            lr=config.learning_rate
        )
        
    def collect_rollouts(
        self,
        env: RobotEnvironment,
        num_episodes: int
    ) -> list[dict]:
        """
        Collect experience using the current policy.
        Uses world model for imagination rollouts when real interaction is costly.
        """
        trajectories = []
        
        for episode in range(num_episodes):
            obs = env.reset()
            episode_data = {
                "observations": [],
                "actions": [],
                "rewards": [],
                "dones": [],
                "log_probs": []
            }
            
            done = False
            while not done:
                # Get action from policy
                with torch.no_grad():
                    action_dist = self.policy.get_action_distribution(obs)
                    action = action_dist.sample()
                    log_prob = action_dist.log_prob(action)
                
                # Environment step
                next_obs, reward, done, info = env.step(action)
                
                episode_data["observations"].append(obs)
                episode_data["actions"].append(action)
                episode_data["rewards"].append(reward)
                episode_data["dones"].append(done)
                episode_data["log_probs"].append(log_prob)
                
                obs = next_obs
                
            trajectories.append(episode_data)
            
        return trajectories
    
    def compute_advantages(
        self,
        rewards: list[float],
        dones: list[bool],
        values: list[float],
        gamma: float = 0.99,
        lambda_gae: float = 0.95
    ) -> tuple[list[float], list[float]]:
        """
        Compute Generalized Advantage Estimation (GAE).
        """
        advantages = []
        returns = []
        
        gae = 0
        next_value = 0
        
        for t in reversed(range(len(rewards))):
            if t == len(rewards) - 1:
                next_non_terminal = 1.0 - dones[t]
                next_value = 0
            else:
                next_non_terminal = 1.0 - dones[t]
                
            delta = rewards[t] + gamma * next_value * next_non_terminal - values[t]
            gae = delta + gamma * lambda_gae * next_non_terminal * gae
            
            advantages.insert(0, gae)
            returns.insert(0, gae + values[t])
            
            next_value = values[t]
            
        return advantages, returns
    
    def update_policy(self, trajectories: list[dict]):
        """
        PPO policy update with clipped surrogate objective.
        """
        all_observations = torch.cat([
            torch.stack(ep["observations"]) for ep in trajectories
        ])
        all_actions = torch.cat([
            torch.stack(ep["actions"]) for ep in trajectories
        ])
        all_old_log_probs = torch.cat([
            torch.stack(ep["log_probs"]) for ep in trajectories
        ])
        
        # Compute rewards and values
        all_rewards = [r for ep in trajectories for r in ep["rewards"]]
        all_dones = [d for ep in trajectories for d in ep["dones"]]
        
        with torch.no_grad():
            values = self.value_network(all_observations).squeeze(-1)
        
        advantages, returns = self.compute_advantages(
            all_rewards, all_dones, values.cpu().tolist()
        )
        
        # Normalize advantages
        advantages = torch.tensor(advantages)
        advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
        
        # PPO update
        for epoch in range(self.config.ppo_epochs):
            # New action distribution
            action_dist = self.policy.get_action_distribution(all_observations)
            new_log_probs = action_dist.log_prob(all_actions)
            entropy = action_dist.entropy()
            
            # Ratio for importance sampling
            ratio = torch.exp(new_log_probs - all_old_log_probs)
            
            # Clipped surrogate objective
            surr1 = ratio * advantages
            surr2 = torch.clamp(
                ratio,
                1 - self.config.clip_epsilon,
                1 + self.config.clip_epsilon
            ) * advantages
            
            # Policy loss
            policy_loss = -torch.min(surr1, surr2).mean()
            
            # Value loss
            new_values = self.value_network(all_observations).squeeze(-1)
            value_loss = nn.functional.mse_loss(new_values, returns)
            
            # Entropy bonus (for exploration)
            entropy_loss = -entropy.mean()
            
            # Total loss
            total_loss = (
                policy_loss +
                self.config.value_coef * value_loss +
                self.config.entropy_coef * entropy_loss
            )
            
            # Gradient step
            self.optimizer.zero_grad()
            total_loss.backward()
            nn.utils.clip_grad_norm_(self.policy.parameters(), 0.5)
            self.optimizer.step()

"""
Physics Simulation Wrapper
==========================
Interface to Isaac Gym / MuJoCo / PyBullet for robot simulation.
"""

class RobotSimulator:
    """
    Unified interface for robot physics simulation.
    """
    
    def __init__(
        self,
        backend: str = "mujoco",  # mujoco, isaac, pybullet
        config: dict = None
    ):
        self.backend = backend
        self.config = config or {}
        
        if backend == "mujoco":
            self._init_mujoco()
        elif backend == "isaac":
            self._init_isaac()
        elif backend == "pybullet":
            self._init_pybullet()
            
    def _init_mujoco(self):
        """Initialize MuJoCo physics engine."""
        import mujoco
        
        # Create model from XML
        self.model = mujoco.MjModel.from_xml_string(self._get_robot_xml())
        self.data = mujoco.MjData(self.model)
        
        # Renderer for visualization
        self.renderer = mujoco.viewer.Callback()
        
    def step(self, action: np.ndarray):
        """
        Execute one simulation step.
        
        Args:
            action: Joint position targets or torques
        """
        if self.backend == "mujoco":
            import mujoco
            self.data.ctrl[:] = action
            mujoco.mj_step(self.model, self.data)
            
    def reset(self) -> dict:
        """Reset simulation to initial state."""
        import mujoco
        mujoco.mj_resetData(self.model, self.data)
        return self._get_observation()
    
    def _get_observation(self) -> dict:
        """Extract sensor readings from simulation."""
        return {
            "qpos": self.data.qpos.copy(),      # Joint positions
            "qvel": self.data.qvel.copy(),      # Joint velocities
            "rgb": self._render_image(),        # RGB camera
            "depth": self._render_depth(),       # Depth image
        }

# Required skills for OpenAI Robotics positions
SKILLS_REQUIRED = {
    "robotics_engineer": {
        "required": [
            "ROS/ROS2 development",
            "C++ and Python proficiency",
            "Kinematics and dynamics",
            "Control systems theory"
        ],
        "preferred": [
            "Reinforcement learning",
            "Computer vision",
            "Simulation experience",
            "Real robot deployment"
        ]
    },
    "motion_planning": {
        "required": [
            "Path planning algorithms (RRT, PRM, A*)",
            "Trajectory optimization",
            "Collision detection",
            "Optimization solvers"
        ],
        "preferred": [
            "Whole-body control",
            "Manipulation planning",
            "Learning-based planning"
        ]
    },
    "computer_vision": {
        "required": [
            "Deep learning for vision",
            "Object detection/segmentation",
            "3D reconstruction",
            "SLAM"
        ],
        "preferred": [
            "Multi-view geometry",
            "Point cloud processing",
            "Novel view synthesis"
        ]
    }
}

"""
Complete Robot Control Loop
============================
End-to-end implementation of a robot manipulation task.
"""

import numpy as np
import torch
from typing import Optional
import time

class RobotController:
    """
    High-level robot controller integrating perception, planning, and control.
    """
    
    def __init__(
        self,
        vla_model: VLAModel,
        world_model: WorldModel,
        simulator: RobotSimulator,
        config: dict
    ):
        self.vla = vla_model
        self.world_model = world_model
        self.sim = simulator
        self.config = config
        
        # State tracking
        self.current_state: Optional[dict] = None
        self.task_queue: list[str] = []
        self.execution_history: list[dict] = []
        
    def initialize(self):
        """Initialize the robot controller."""
        # Reset simulation
        self.current_state = self.sim.reset()
        
        # Run initial perception
        self._update_state()
        
        print("Robot controller initialized")
        print(f"Initial joint positions: {self.current_state['qpos']}")
        
    def execute_task(self, task_description: str) -> dict:
        """
        Execute a manipulation task described in natural language.
        
        Args:
            task_description: e.g., "Pick up the red cube and place it in the box"
            
        Returns:
            Execution result with success status and metrics
        """
        print(f"\nExecuting task: {task_description}")
        
        start_time = time.time()
        max_steps = self.config.get("max_steps", 100)
        
        for step in range(max_steps):
            # 1. Perception: Get current observation
            obs = self.sim._get_observation()
            
            # 2. Action generation using VLA model
            with torch.no_grad():
                joint_targets, inference_time = self._generate_action(
                    obs, task_description
                )
            
            # 3. Safety check
            if not self._check_safety(joint_targets):
                print("Safety constraint violated! Stopping.")
                return self._create_result(
                    success=False,
                    reason="Safety violation",
                    steps=step
                )
            
            # 4. Execute action in simulation
            self.sim.step(joint_targets)
            
            # 5. Check task completion
            task_complete = self._check_task_complete(task_description)
            
            if task_complete:
                execution_time = time.time() - start_time
                print(f"Task completed in {execution_time:.2f}s ({step} steps)")
                
                return self._create_result(
                    success=True,
                    execution_time=execution_time,
                    steps=step,
                    avg_inference_time=inference_time
                )
        
        # Task not completed within step limit
        return self._create_result(
            success=False,
            reason="Step limit exceeded",
            steps=max_steps
        )
    
    def _generate_action(
        self,
        obs: dict,
        task: str
    ) -> tuple[np.ndarray, float]:
        """
        Generate action using the VLA model.
        """
        # Convert observation to tensor
        rgb = obs["rgb"]
        rgb_tensor = self._preprocess_image(rgb)
        
        # VLA inference
        joint_targets, inference_time = self.vla.predict(
            images=rgb_tensor,
            task_description=task
        )
        
        # Scale joint targets to valid range
        joint_targets = np.clip(joint_targets, -1, 1)
        
        return joint_targets, inference_time
    
    def _preprocess_image(self, image: np.ndarray) -> np.ndarray:
        """Preprocess RGB image for VLA model."""
        import cv2
        
        # Resize to model input size
        image = cv2.resize(image, (224, 224))
        
        # Normalize
        image = image.astype(np.float32) / 255.0
        
        return image
    
    def _check_safety(self, action: np.ndarray) -> bool:
        """
        Check if the proposed action is safe.
        
        Safety checks:
        - Joint limits
        - Collision avoidance
        - Velocity limits
        - Force limits
        """
        # Joint limit check
        joint_limits = np.array(self.config.get("joint_limits", []))
        if joint_limits.any():
            if np.any(action < joint_limits[:, 0]) or \
               np.any(action > joint_limits[:, 1]):
                return False
        
        # Velocity limit check
        velocity_limit = self.config.get("velocity_limit", 1.0)
        if np.abs(action).max() > velocity_limit:
            return False
            
        return True
    
    def _check_task_complete(self, task: str) -> bool:
        """
        Check if the task has been completed.
        
        In a real system, this would use:
        - Vision-based state comparison
        - Object detection
        - Goal condition evaluation
        """
        # Simplified: check if robot has moved significantly
        qpos = self.sim.data.qpos
        return np.std(qpos) > 0.5  # Placeholder condition
    
    def _update_state(self):
        """Update internal state representation."""
        self.current_state = self.sim._get_observation()
    
    def _create_result(
        self,
        success: bool,
        reason: str = None,
        steps: int = 0,
        execution_time: float = 0,
        avg_inference_time: float = 0
    ) -> dict:
        """Create standardized result dictionary."""
        return {
            "success": success,
            "reason": reason,
            "steps": steps,
            "execution_time": execution_time,
            "avg_inference_time": avg_inference_time,
            "timestamp": time.time()
        }


# Example usage
def main():
    """Example robot control session."""
    
    # Configuration
    config = {
        "max_steps": 100,
        "velocity_limit": 0.5,
        "joint_limits": np.array([
            [-1.5, 1.5],  # Joint 1
            [-1.5, 1.5],  # Joint 2
            [-1.5, 1.5],  # Joint 3
            [-1.5, 1.5],  # Joint 4
            [-1.5, 1.5],  # Joint 5
            [-1.5, 1.5],  # Joint 6
            [-1.5, 1.5],  # Joint 7
        ]),
        "vlm_checkpoint": "openai/vla-v1",
        "world_model_checkpoint": "openai/world-model-v1",
    }
    
    # Initialize components
    print("Loading models...")
    
    # Create models (in real deployment, load from checkpoints)
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    vla_model = VLAModel().to(device)
    world_model = WorldModel(config)
    simulator = RobotSimulator(backend="mujoco", config=config)
    
    # Create controller
    controller = RobotController(
        vla_model=vla_model,
        world_model=world_model,
        simulator=simulator,
        config=config
    )
    
    # Initialize
    controller.initialize()
    
    # Execute tasks
    tasks = [
        "Move to neutral position",
        "Pick up the blue cube",
        "Place the cube in the container",
    ]
    
    for task in tasks:
        result = controller.execute_task(task)
        print(f"Task result: {result}\n")
    
    print("Control session complete!")


if __name__ == "__main__":
    main()

"""
Robot Data Collection Pipeline
==============================
Efficient system for collecting demonstration data.
"""

import os
import json
import numpy as np
from dataclasses import dataclass, asdict
from typing import Iterator
import h5py
from datetime import datetime


@dataclass
class RobotDemonstration:
    """A single robot demonstration episode."""
    episode_id: str
    task_description: str
    observations: list
    actions: list
    rewards: list
    success: bool
    duration: float
    metadata: dict


class DataCollector:
    """
    System for collecting and storing robot demonstration data.
    """
    
    def __init__(
        self,
        output_dir: str = "./robot_data",
        buffer_size: int = 100,
        compression: str = "gzip"
    ):
        self.output_dir = output_dir
        self.buffer_size = buffer_size
        self.compression = compression
        
        # Create output directory
        os.makedirs(output_dir, exist_ok=True)
        
        # Episode buffer
        self.buffer: list[RobotDemonstration] = []
        self.episode_counter = 0
        
        # HDF5 file for efficient storage
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        self.hdf5_path = os.path.join(
            output_dir, 
            f"demonstrations_{timestamp}.hdf5"
        )
        self.hdf5_file = h5py.File(self.hdf5_path, "w")
        
        # Dataset groups
        self._create_datasets()
        
    def _create_datasets(self):
        """Initialize HDF5 datasets."""
        # Create groups
        self.observations_grp = self.hdf5_file.create_group("observations")
        self.actions_grp = self.hdf5_file.create_group("actions")
        self.metadata_grp = self.hdf5_file.create_group("metadata")
        
        # Episode metadata dataset
        self.hdf5_file.create_dataset(
            "episodes",
            shape=(0,),
            maxshape=(None,),
            dtype=h5py.special_dtype(vlen=str)
        )
        
    def record_step(
        self,
        observation: dict,
        action: np.ndarray,
        reward: float
    ):
        """Record a single timestep."""
        self.current_observations.append(observation)
        self.current_actions.append(action)
        self.current_rewards.append(reward)
        
    def start_episode(self, task_description: str):
        """Start a new demonstration episode."""
        self.current_task = task_description
        self.current_observations = []
        self.current_actions = []
        self.current_rewards = []
        self.episode_start_time = datetime.now()
        
        print(f"Starting episode: {task_description}")
        
    def end_episode(self, success: bool):
        """Complete and store an episode."""
        duration = (datetime.now() - self.episode_start_time).total_seconds()
        
        episode = RobotDemonstration(
            episode_id=f"ep_{self.episode_counter:06d}",
            task_description=self.current_task,
            observations=self.current_observations,
            actions=self.current_actions,
            rewards=self.current_rewards,
            success=success,
            duration=duration,
            metadata={
                "num_steps": len(self.current_observations),
                "collect_timestamp": self.episode_start_time.isoformat()
            }
        )
        
        # Add to buffer
        self.buffer.append(episode)
        self.episode_counter += 1
        
        # Flush if buffer is full
        if len(self.buffer) >= self.buffer_size:
            self._flush_buffer()
            
        print(f"Episode complete: success={success}, duration={duration:.2f}s")
        
    def _flush_buffer(self):
        """Write buffered episodes to HDF5."""
        if not self.buffer:
            return
            
        for ep in self.buffer:
            # Create subgroup for episode
            grp = self.observations_grp.create_group(ep.episode_id)
            
            # Store observations
            obs_data = self._stack_observations(ep.observations)
            grp.create_dataset(
                "rgb", 
                data=obs_data["rgb"],
                compression=self.compression
            )
            grp.create_dataset(
                "depth",
                data=obs_data["depth"],
                compression=self.compression
            )
            grp.create_dataset(
                "proprio",
                data=np.array(obs_data["proprio"]),
                compression=self.compression
            )
            
            # Store actions
            self.actions_grp.create_dataset(
                f"{ep.episode_id}/joints",
                data=np.array(ep.actions),
                compression=self.compression
            )
            
            # Store metadata
            self.metadata_grp.create_dataset(
                f"{ep.episode_id}/task",
                data=ep.task_description
            )
            self.metadata_grp.create_dataset(
                f"{ep.episode_id}/success",
                data=1 if ep.success else 0
            )
            
        self.buffer.clear()
        self.hdf5_file.flush()
        
        print(f"Flushed {len(self.buffer)} episodes to disk")
        
    def _stack_observations(self, observations: list) -> dict:
        """Stack observation arrays."""
        rgb = np.stack([o["rgb"] for o in observations])
        depth = np.stack([o["depth"] for o in observations])
        proprio = [o["proprio"] for o in observations]
        
        return {"rgb": rgb, "depth": depth, "proprio": proprio}
    
    def close(self):
        """Finalize and close the data collector."""
        # Flush remaining episodes
        self._flush_buffer()
        
        # Store summary
        summary = {
            "total_episodes": self.episode_counter,
            "output_path": self.hdf5_path,
            "timestamp": datetime.now().isoformat()
        }
        
        with open(
            os.path.join(self.output_dir, "collection_summary.json"),
            "w"
        ) as f:
            json.dump(summary, f, indent=2)
            
        self.hdf5_file.close()
        print(f"Data collection complete. Saved to {self.hdf5_path}")


# Utility functions for data processing
def load_demonstrations(hdf5_path: str) -> Iterator[dict]:
    """
    Load demonstrations from HDF5 file.
    
    Usage:
        for ep in load_demonstrations("demonstrations.hdf5"):
            process(ep)
    """
    with h5py.File(hdf5_path, "r") as f:
        for ep_id in f["observations"].keys():
            yield {
                "episode_id": ep_id,
                "rgb": f[f"observations/{ep_id}/rgb"][:],
                "depth": f[f"observations/{ep_id}/depth"][:],
                "proprio": f[f"observations/{ep_id}/proprio"][:],
                "actions": f[f"actions/{ep_id}/joints"][:],
                "task": f[f"metadata/{ep_id}/task"][()],
                "success": bool(f[f"metadata/{ep_id}/success"][()])
            }


def compute_success_rate(demonstrations: list[dict]) -> float:
    """Compute success rate from demonstration list."""
    if not demonstrations:
        return 0.0
    successes = sum(1 for ep in demonstrations if ep["success"])
    return successes / len(demonstrations)