NVIDIA Cosmos 3: The World's First Open-Source Physical AI World Model

# Traditional AI limitations example
class TraditionalAI:
    """Traditional AI lacks physical world understanding"""
    
    def __init__(self):
        self.capabilities = {
            "vision": "Can only identify image content",
            "reasoning": "Lacks physical intuition",
            "prediction": "Cannot predict object motion",
            "action": "Cannot generate coordinated actions"
        }
    
    def analyze_scene(self, image):
        """Traditional visual analysis - only recognizes static content"""
        objects = self.detect_objects(image)
        return {
            "what": objects,  # "There's a cup"
            "where": "on the table",  # Vague position
            # Missing: Will the cup fall? Will water spill?
        }
    
    def plan_action(self, goal):
        """Traditional planning - cannot consider physics"""
        # Can only execute predefined rules
        # Cannot handle unknown physical interactions
        pass

class CosmosWorldModel:
    """Cosmos 3 - Physical AI World Model"""
    
    def __init__(self):
        self.capabilities = {
            "vision": "Deep understanding of scene geometry",
            "reasoning": "Physical intuition reasoning",
            "prediction": "Predict future state sequences",
            "action": "Generate coordinated action sequences"
        }
    
    def analyze_scene(self, image):
        """Cosmos scene analysis - understanding physical dynamics"""
        physics = self.understand_physics(image)
        return {
            "what": "cup + water + tilted table",
            "physics": {
                "gravity": "9.8 m/s² downward",
                "water_volume": "approximately 200ml",
                "tilt_angle": "30 degrees",
                "prediction": "water will spill in 2.3 seconds"
            },
            "action_plan": ["stabilize cup", "wipe table"]
        }

# Cosmos 3 Training Data Configuration
TRAINING_CONFIG = {
    "multimodal_data": {
        "text_corpus": "billions of text-image pairs",
        "video_clips": "billions of minutes of video",
        "audio_samples": "billions of sound effect clips", 
        "action_trajectories": "millions of robot action trajectories",
        "sensor_readings": "sensor data points"
    },
    "data_sources": [
        "robotics_demos",      # Robot demonstration videos
        "autonomous_driving",  # Self-driving data
        "manipulation_tasks",  # Object manipulation tasks
        "human_activities",    # Human activity videos
        "physics_simulations"   # Physics simulation data
    ],
    "tokenization": {
        "video_tokenizer": "Cosmos Tokenizer",
        "text_tokenizer": "T5/Gemma",
        "action_tokenizer": "Discrete action representation"
    }
}

class CosmosDataPipeline:
    """Data processing pipeline"""
    
    def __init__(self, config):
        self.config = config
        self.tokenizers = self._init_tokenizers()
    
    def _init_tokenizers(self):
        """Initialize multimodal tokenizer"""
        return {
            "video": VideoTokenizer(
                architecture="Cosmos Tokenizer",
                compression_ratio=64,  # 64x compression
                spatial_tokens=256,
                temporal_tokens=32
            ),
            "text": TextTokenizer(
                model="T5-large",
                vocab_size=32000
            ),
            "action": ActionTokenizer(
                discretization_bins=256,
                action_dim=7  # Position + pose + gripper
            )
        }
    
    def process_batch(self, batch):
        """Batch process multimodal data"""
        return {
            "video_tokens": self.tokenizers["video"](batch["videos"]),
            "text_tokens": self.tokenizers["text"](batch["instructions"]),
            "action_tokens": self.tokenizers["action"](batch["actions"]),
            "timestamps": batch["timestamps"]
        }

import torch
import torch.nn as nn
from typing import Dict, Optional, Tuple

class CosmosTransformer(nn.Module):
    """
    Cosmos 3 Dual Transformer Core Architecture
    
    Features:
    1. Visual Encoder Transformer: Process visual input, extract world state
    2. Action Decoder Transformer: Generate action sequences based on world state
    3. Shared Latent Space: Two transformers communicate via shared representation
    """
    
    def __init__(
        self,
        visual_dim: int = 768,
        action_dim: int = 256,
        hidden_dim: int = 2048,
        num_heads: int = 16,
        num_layers: int = 24,
        dropout: float = 0.1
    ):
        super().__init__()
        
        # Visual Encoder: Visual input encoding
        self.visual_encoder = VisualEncoderTransformer(
            input_dim=visual_dim,
            hidden_dim=hidden_dim,
            num_heads=num_heads,
            num_layers=num_layers,
            dropout=dropout
        )
        
        # Action Decoder: Action sequence generation
        self.action_decoder = ActionDecoderTransformer(
            output_dim=action_dim,
            hidden_dim=hidden_dim,
            num_heads=num_heads,
            num_layers=num_layers,
            dropout=dropout
        )
        
        # Shared latent space projection
        self.world_state_proj = nn.Linear(hidden_dim, hidden_dim)
        self.physical_constraints = PhysicalConstraintModule(hidden_dim)
        
        # Output heads
        self.action_head = nn.Linear(hidden_dim, action_dim)
        self.state_prediction_head = nn.Linear(hidden_dim, visual_dim)
    
    def forward(
        self,
        visual_input: torch.Tensor,      # [B, T, C, H, W]
        text_instruction: torch.Tensor,  # [B, L]
        action_context: Optional[torch.Tensor] = None,  # [B, T, A]
    ) -> Dict[str, torch.Tensor]:
        """
        Forward propagation
        
        Args:
            visual_input: Video input [batch, sequence, channels, height, width]
            text_instruction: Text instruction [batch, seq_len]
            action_context: Historical action context (optional)
        
        Returns:
            Dictionary containing action predictions and state predictions
        """
        # 1. Visual encoding
        visual_features = self.visual_encoder(visual_input)
        
        # 2. World state understanding
        world_state = self.world_state_proj(visual_features)
        physics_knowledge = self.physical_constraints(world_state)
        
        # 3. Action decoding
        action_output = self.action_decoder(
            encoded_state=world_state,
            text_instruction=text_instruction,
            action_context=action_context
        )
        
        # 4. Prediction and generation
        predicted_actions = self.action_head(action_output)
        predicted_states = self.state_prediction_head(visual_features)
        
        return {
            "actions": predicted_actions,           # Action sequence
            "state_predictions": predicted_states,  # Future state prediction
            "world_representation": world_state,    # World representation
            "physics_reasoning": physics_knowledge  # Physics reasoning results
        }


class PhysicalConstraintModule(nn.Module):
    """
    Physical Constraint Module
    
    Learns implicit rules of the physical world:
    - Gravity direction
    - Object inertia
    - Collision detection
    - Friction
    - Object permanence
    """
    
    def __init__(self, hidden_dim: int):
        super().__init__()
        
        self.physics_encoder = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.GELU(),
            nn.Linear(hidden_dim // 2, hidden_dim // 4),
            nn.Linear(hidden_dim // 4, 16)  # 16 basic physical quantities
        )
        
        # Predefined physical constraint priors
        self.register_buffer(
            "gravity_vector", 
            torch.tensor([0, -9.81, 0])
        )
        
    def forward(self, world_state: torch.Tensor) -> torch.Tensor:
        """
        Encode physical constraints into state representation
        """
        physics_features = self.physics_encoder(world_state)
        
        # Physical feature meanings:
        # [0]: gravity effect, [1]: velocity, [2-4]: inertia tensor
        # [5]: friction coefficient, [6]: elasticity, [7]: collision flag
        # ...
        
        return physics_features

class CosmosVisualReasoning:
    """Cosmos 3 Visual Reasoning Capability"""
    
    def __init__(self, model_path: str):
        self.model = self._load_model(model_path)
        self.scene_understanding = SceneUnderstandingModule()
    
    def understand_scene(self, video_frames: List[np.ndarray]) -> Dict:
        """
        Deep scene understanding
        
        Not only identifies "what exists" but understands "what will happen"
        """
        # Geometry understanding
        geometry = self.scene_understanding.extract_3d_geometry(video_frames)
        
        # Physical relations analysis
        physics_relations = self._analyze_physics(geometry)
        
        # Causal reasoning
        causality = self._infer_causality(video_frames)
        
        return {
            "objects": geometry["objects"],
            "spatial_relations": geometry["relations"],
            "physical_properties": physics_relations,
            "causal_chains": causality,
            "uncertainty_map": self._compute_uncertainty(geometry)
        }
    
    def predict_outcomes(self, scene: Dict, action: str) -> List[Dict]:
        """
        Predict action outcomes
        
        Given a scene and action, predict possible results
        """
        # Physics simulation prediction
        future_states = self._simulate_physics(scene, action, steps=50)
        
        # Likelihood scoring
        likelihoods = self._score_outcomes(future_states)
        
        return sorted(
            zip(future_states, likelihoods),
            key=lambda x: x[1],
            reverse=True
        )[:5]  # Return top 5 predictions


# Usage example
reasoning = CosmosVisualReasoning("cosmos3-visual")
scene = reasoning.understand_scene(video_frames)

# Scene understanding result example:
{
    "objects": [
        {"id": "cup_1", "position": [0.5, 0.3, 0.1], "shape": "cylinder"},
        {"id": "table_1", "position": [0.5, 0, 0], "shape": "box"},
        {"id": "water_1", "position": "in cup_1", "state": "liquid"}
    ],
    "spatial_relations": [
        "cup_1 on table_1",
        "water_1 inside cup_1"
    ],
    "physical_properties": {
        "cup_1": {"mass": 0.2, "material": "ceramic", "friction": 0.3},
        "table_1": {"surface": "smooth wood", "friction": 0.4},
        "water_1": {"viscosity": 0.001, "surface_tension": 0.07}
    },
    "causal_chains": [
        "table collision → cup vibration → water wave → potential spill"
    ]
}

class CosmosWorldGeneration:
    """Cosmos 3 World Generation Capability"""
    
    def __init__(self):
        self.video_diffusion = VideoDiffusionModel()
        self.world_consistency = WorldConsistencyChecker()
    
    def generate_world_states(
        self,
        initial_condition: Dict,
        num_frames: int = 120,
        conditioning: Optional[Dict] = None
    ) -> List[np.ndarray]:
        """
        Generate consistent world state sequences
        
        Core features:
        1. Physical consistency: Object motion follows physical laws
        2. Temporal consistency: State changes smoothly and continuously
        3. Causal consistency: Results have reasonable causes
        """
        # Tokenize initial condition
        initial_tokens = self._tokenize_condition(initial_condition)
        
        # Condition encoding
        if conditioning:
            condition_tokens = self._encode_conditions(conditioning)
        else:
            condition_tokens = None
        
        # Generate video frames
        generated_frames = self.video_diffusion.generate(
            initial_tokens=initial_tokens,
            condition_tokens=condition_tokens,
            num_steps=num_frames,
            guidance_scale=7.5
        )
        
        # Consistency post-processing
        validated_frames = self.world_consistency.validate(generated_frames)
        
        return validated_frames
    
    def generate_counterfactual(
        self,
        actual_scene: Dict,
        hypothetical_change: Dict
    ) -> np.ndarray:
        """
        Generate counterfactual scenarios
        
        "What would happen if we made a different choice?"
        """
        # Find divergence point
        divergence_point = self._find_divergence(hypothetical_change)
        
        # Generate alternate timeline
        alternate_timeline = self._generate_timeline(
            scene=actual_scene,
            divergence=divergence_point,
            alternative_action=hypothetical_change["action"]
        )
        
        return alternate_timeline


# Complete Physical AI World Model inference pipeline
class PhysicalAIInferencePipeline:
    """
    Complete Physical AI World Model Inference Pipeline
    
    End-to-end processing: Video input → World understanding → Action prediction → Execution
    """
    
    def __init__(self, config: Dict):
        self.config = config
        self.cosmos = CosmosTransformer(**config["model"])
        self.tokenizers = self._init_tokenizers()
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        
    def _init_tokenizers(self):
        """Initialize tokenizers"""
        return {
            "video": CosmoVideoTokenizer(),
            "action": ActionTokenizer(),
            "text": TextTokenizer()
        }
    
    def run_inference(
        self,
        rgb_frames: torch.Tensor,
        depth_frames: torch.Tensor,
        instruction: str,
        num_prediction_steps: int = 10
    ) -> Dict[str, torch.Tensor]:
        """
        Complete inference pipeline
        
        Args:
            rgb_frames: RGB video frames [T, 3, H, W]
            depth_frames: Depth maps [T, 1, H, W]
            instruction: Text instruction "pick up the red cup"
            num_prediction_steps: Prediction steps
        
        Returns:
            Dictionary containing predicted actions and future states
        """
        # Data preprocessing
        rgb_tokens = self.tokenizers["video"](rgb_frames)
        depth_tokens = self.tokenizers["video"](depth_frames)
        text_tokens = self.tokenizers["text"](instruction)
        
        # Fuse multimodal input
        multimodal_input = self._fuse_inputs(rgb_tokens, depth_tokens, text_tokens)
        
        # World model inference
        with torch.no_grad():
            outputs = self.cosmos(
                visual_input=multimodal_input,
                text_instruction=text_tokens
            )
        
        # Action prediction decoding
        predicted_actions = self._decode_actions(outputs["actions"])
        
        # Future state prediction
        future_states = self._predict_future_states(
            current_state=outputs["world_representation"],
            actions=predicted_actions,
            steps=num_prediction_steps
        )
        
        # Physics feasibility validation
        feasibility = self._validate_physics(predicted_actions, future_states)
        
        return {
            "action_sequence": predicted_actions,
            "future_states": future_states,
            "feasibility_score": feasibility,
            "world_representation": outputs["world_representation"],
            "confidence": self._compute_confidence(outputs)
        }
    
    def _fuse_inputs(self, rgb, depth, text):
        """Multimodal feature fusion"""
        # Early fusion: Concatenate RGB and depth
        visual = torch.cat([rgb, depth], dim=-1)
        
        # Cross-attention fusion with text
        fused = self.cross_attention_fusion(visual, text)
        
        return fused

class CosmosActionPrediction:
    """Cosmos 3 Action Prediction Capability"""
    
    def __init__(self, model):
        self.model = model
        self.action_space = self._define_action_space()
    
    def _define_action_space(self):
        """
        Define robot action space
        
        Typical 7-DOF robotic arm action representation:
        - 6 joint angles (radians)
        - 1 gripper command (0=open, 1=close)
        """
        return {
            "joint_limits": [
                (-3.14, 3.14),  # Joint 1
                (-2.35, 2.35),  # Joint 2
                (-2.61, 2.61),  # Joint 3
                (-3.14, 3.14),  # Joint 4
                (-2.09, 2.09),  # Joint 5
                (-3.14, 3.14),  # Joint 6
            ],
            "gripper_range": (0.0, 1.0),
            "action_horizon": 10,  # Predict 10-step actions
            "temporal_horizon": 1.0  # 1 second time span
        }
    
    def predict_action_sequence(
        self,
        observation: Dict,
        goal: str,
        policy_type: str = "diffusion"
    ) -> np.ndarray:
        """
        Predict action sequence
        
        Supports multiple strategies:
        - diffusion: Diffusion model policy
        - autoregressive: Autoregressive policy
        - hierarchical: Hierarchical policy
        """
        # Encode current observation
        obs_encoding = self._encode_observation(observation)
        
        # Encode goal
        goal_encoding = self._encode_goal(goal)
        
        # Policy generation
        if policy_type == "diffusion":
            actions = self._diffusion_denoise(obs_encoding, goal_encoding)
        elif policy_type == "autoregressive":
            actions = self._autoregressive_generate(obs_encoding, goal_encoding)
        else:
            actions = self._hierarchical_plan(obs_encoding, goal_encoding)
        
        # Physics constraint post-processing
        actions = self._apply_physics_constraints(actions)
        
        return actions
    
    def _diffusion_denoise(self, obs, goal, num_steps=50):
        """Diffusion model action denoising"""
        # Start from noise
        action_sequence = torch.randn(
            self.action_space["action_horizon"],
            7  # 7D action space
        ).to(obs.device)
        
        # Iterative denoising
        for t in reversed(range(num_steps)):
            # Add conditioning
            conditioned = torch.cat([action_sequence, obs, goal], dim=-1)
            
            # Predict noise
            noise_pred = self.model.denoise(conditioned, t)
            
            # Denoise step
            action_sequence = self._ddpm_step(
                action_sequence, noise_pred, t
            )
        
        return action_sequence.cpu().numpy()
    
    def _apply_physics_constraints(self, actions: np.ndarray) -> np.ndarray:
        """
        Apply physics constraint post-processing
        
        Ensure actions:
        1. Don't exceed joint limits
        2. Satisfy velocity/acceleration constraints
        3. Avoid singular configurations
        """
        constrained = actions.copy()
        
        # Joint limit constraints
        for i, (min_j, max_j) in enumerate(self.action_space["joint_limits"]):
            constrained[:, i] = np.clip(constrained[:, i], min_j, max_j)
        
        # Velocity constraints (finite difference)
        velocities = np.diff(constrained[:, :6], axis=0)
        max_velocity = 0.5  # rad/s
        for i in range(len(velocities)):
            scale = np.linalg.norm(velocities[i]) / max_velocity
            if scale > 1:
                velocities[i] /= scale
        constrained[1:, :6] = np.cumsum(velocities, axis=0) + constrained[0, :6]
        
        # Smoothing
        from scipy.ndimage import gaussian_filter1d
        constrained[:, :6] = gaussian_filter1d(constrained[:, :6], sigma=1, axis=0)
        
        return constrained

# cosmos3_deployment_config.yaml
# Cosmos 3 Multi-Version Deployment Configuration

cosmos3_super:
  # Cloud training version
  model:
    name: "Cosmos-3-Super-70B"
    architecture: "Dual-Transformer"
    parameters: 70000000000  # 70B parameters
    
  hardware:
    gpu: "NVIDIA H100 SXM"
    gpu_count: 8
    memory_per_gpu: "80GB HBM3"
    interconnect: "NVLink 900GB/s"
    
  training:
    batch_size: 2048
    learning_rate: 1e-4
    warmup_steps: 1000
    max_steps: 100000
    gradient_accumulation: 4
    
  optimization:
    precision: "bf16-mixed"
    optimizer: "AdamW"
    scheduler: "cosine"
    gradient_checkpointing: true
    tensor_parallelism: 8
    
  data:
    video_format: "mp4/h265"
    max_resolution: [1920, 1080]
    fps: 30
    tokenizer: "Cosmos-Tokenizer-64x"

cosmos3_nano:
  # Cloud inference version
  model:
    name: "Cosmos-3-Nano-14B"
    parameters: 14000000000  # 14B parameters
    
  hardware:
    gpu: "NVIDIA A100 40GB"
    gpu_count: 1
    memory: "40GB HBM2e"
    
  inference:
    batch_size: 1
    max_new_tokens: 512
    temperature: 0.7
    top_p: 0.9
    
  optimization:
    precision: "fp16"
    quantization: "awq"
    gpu_memory_fraction: 0.9
    
  api:
    endpoint: "/v1/cosmos/nano"
    max_concurrent_requests: 100
    timeout: 30

cosmos3_edge:
  # Edge deployment version
  model:
    name: "Cosmos-3-Edge-3B"
    parameters: 3000000000  # 3B parameters
    
  hardware:
    device: "NVIDIA Jetson AGX Orin"
    gpu_cores: 2048
    ram: "64GB LPDDR5"
    storage: "256GB NVMe"
    
  inference:
    batch_size: 1
    max_tokens: 256
    stream: true
    
  optimization:
    precision: "int8"
    tensor_rt: true
    cuda_graphs: true
    
  real_time:
    target_fps: 30
    latency_budget_ms: 50
    loop_rate_hz: 100

# cosmos3_deployment.py
# Cosmos 3 Multi-Version Deployment Code

import torch
from typing import Literal, Optional
from dataclasses import dataclass

@dataclass
class Cosmos3Config:
    """Cosmos 3 Configuration"""
    version: Literal["super", "nano", "edge"]
    precision: str = "fp16"
    device: str = "cuda"
    
    @classmethod
    def from_yaml(cls, config_path: str) -> "Cosmos3Config":
        """Load configuration from YAML"""
        import yaml
        with open(config_path) as f:
            config = yaml.safe_load(f)
        return cls(**config)


class Cosmos3ModelLoader:
    """Cosmos 3 Model Loader"""
    
    MODEL_REGISTRY = {
        "super": {
            "model_id": "nvidia/cosmos-3-super-70b",
            "min_gpus": 8,
            "min_memory_per_gpu": "80GB"
        },
        "nano": {
            "model_id": "nvidia/cosmos-3-nano-14b", 
            "min_gpus": 1,
            "min_memory_per_gpu": "40GB"
        },
        "edge": {
            "model_id": "nvidia/cosmos-3-edge-3b",
            "min_gpus": 1,
            "min_device_memory": "8GB"
        }
    }
    
    def __init__(self):
        self.loaded_models = {}
    
    def load_model(
        self, 
        version: str = "nano",
        precision: str = "fp16"
    ) -> torch.nn.Module:
        """
        Load specified Cosmos 3 model version
        
        Automatically handles:
        - Hardware check
        - Model sharding
        - Precision conversion
        - Quantization (if applicable)
        """
        cache_key = f"{version}_{precision}"
        
        if cache_key in self.loaded_models:
            print(f"Using cached model: {cache_key}")
            return self.loaded_models[cache_key]
        
        # Get model registry info
        registry = self.MODEL_REGISTRY[version]
        
        # Check hardware compatibility
        self._check_hardware(version)
        
        # Load model
        print(f"Loading Cosmos 3 {version}...")
        model = self._do_load_model(
            model_id=registry["model_id"],
            precision=precision,
            version=version
        )
        
        self.loaded_models[cache_key] = model
        return model
    
    def _check_hardware(self, version: str):
        """Hardware compatibility check"""
        if version == "super":
            assert torch.cuda.device_count() >= 8, \
                "Cosmos 3 Super requires at least 8 GPUs"
        elif version == "nano":
            assert torch.cuda.is_available(), \
                "Cosmos 3 Nano requires CUDA GPU"
        else:  # edge
            print("Running on edge device (Jetson Orin)")
    
    def _do_load_model(self, model_id: str, precision: str, version: str):
        """Actually load the model"""
        from transformers import AutoModelForCausalLM
        
        # Load base model
        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            torch_dtype=self._get_dtype(precision),
            device_map="auto" if version != "super" else None
        )
        
        # Precision conversion
        if precision == "int8":
            model = self._apply_int8_quantization(model)
        elif precision == "int4":
            model = self._apply_int4_quantization(model)
        
        return model
    
    def _get_dtype(self, precision: str):
        """Get PyTorch data type"""
        dtype_map = {
            "fp32": torch.float32,
            "fp16": torch.float16,
            "bf16": torch.bfloat16,
            "int8": torch.float16,  # int8 converted during inference
        }
        return dtype_map.get(precision, torch.float16)


# Edge device specialized inference engine
class Cosmos3EdgeEngine:
    """
    Cosmos 3 Edge Inference Engine
    
    Optimized specifically for edge devices like Jetson Orin
    """
    
    def __init__(self, model_path: str):
        self.model = self._load_optimized_model(model_path)
        self.preprocess = self._init_preprocessing()
        self.postprocess = self._init_postprocessing()
        
        # TensorRT optimization
        self._setup_tensorrt()
        
        # CUDA graphs optimization
        self._enable_cuda_graphs()
    
    def _load_optimized_model(self, model_path: str):
        """Load optimized model"""
        # Load with TensorRT
        from tensorrt import runtime
        
        # Check for optimized TensorRT engine
        trt_engine_path = f"{model_path}/model.engine"
        
        if os.path.exists(trt_engine_path):
            # Load existing TensorRT engine
            return self._load_trt_engine(trt_engine_path)
        else:
            # Convert from PyTorch model
            return self._convert_to_tensorrt(model_path)
    
    def _setup_tensorrt(self):
        """Configure TensorRT optimization"""
        self.tensorrt_config = {
            "workspace_size": 1 << 30,  # 1GB
            "max_batch_size": 1,
            "op_precision": "FP16",
            "enable_cublas": True,
            "enable_cudnn": True,
        }
    
    def _enable_cuda_graphs(self):
        """Enable CUDA Graphs to reduce CPU overhead"""
        # CUDA Graphs can significantly reduce CPU overhead for small-batch inference
        self.use_cuda_graphs = True
    
    @torch.no_grad()
    def infer(
        self, 
        video_frames: torch.Tensor,
        instruction: str
    ) -> Dict[str, torch.Tensor]:
        """
        Edge inference (optimized)
        
        Target: Complete inference within 50ms latency
        """
        # Preprocess
        input_ids, attention_mask = self.preprocess(instruction)
        
        # Execute with CUDA Graph (if enabled)
        if self.use_cuda_graphs:
            # Warm up
            if not hasattr(self, '_cuda_graph_warmed'):
                self._cuda_graph_warmed = True
            
            # Execute captured graph
            return self._run_cuda_graph(input_ids, attention_mask)
        
        # Normal inference
        outputs = self.model(
            input_ids=input_ids.cuda(),
            attention_mask=attention_mask.cuda()
        )
        
        return self.postprocess(outputs)

# Cosmos 3 Evaluation Results
EVALUATION_RESULTS = {
    "Artificial Analysis": {
        "description": "Comprehensive capability evaluation (reasoning, coding, QA)",
        "score": 92.4,
        "rank": "#1 (Open-source)",
        "metrics": {
            "reasoning": 91.2,
            "coding": 89.7,
            "qa": 94.1,
            "math": 90.3
        }
    },
    "Physics-IQ": {
        "description": "Physical intelligence specialized evaluation",
        "score": 88.6,
        "rank": "#1 (Open-source)",
        "metrics": {
            "object_interaction": 91.2,
            "trajectory_prediction": 87.4,
            "physical_reasoning": 86.9,
            "collision_understanding": 89.1
        }
    },
    "RoboLab": {
        "description": "Robot task evaluation",
        "score": 85.3,
        "rank": "#1 (Open-source)",
        "subtasks": {
            "pick_and_place": 92.1,
            "tool_use": 83.4,
            "navigation": 87.6,
            "manipulation": 84.2
        }
    }
}

class EvaluationBenchmark:
    """Evaluation benchmark"""
    
    def __init__(self, model, benchmark_name: str):
        self.model = model
        self.benchmark_name = benchmark_name
        self.results = {}
    
    def run_full_benchmark(self) -> Dict:
        """Run complete benchmark"""
        self.results = {
            "overall_score": 0,
            "sub_scores": {},
            "metadata": {
                "model": self.model.name,
                "timestamp": datetime.now().isoformat(),
                "hardware": self._get_hardware_info()
            }
        }
        
        # Execute by benchmark type
        if self.benchmark_name == "Physics-IQ":
            self._run_physics_iq()
        elif self.benchmark_name == "RoboLab":
            self._run_robo_lab()
        elif self.benchmark_name == "Artificial Analysis":
            self._run_artificial_analysis()
        
        return self.results
    
    def _run_physics_iq(self):
        """Run Physics-IQ evaluation"""
        physics_tasks = [
            "rigid_body_simulation",
            "fluid_dynamics",
            "collision_detection",
            "trajectory_prediction",
            "object_stacking",
            "tool_manipulation"
        ]
        
        scores = {}
        for task in physics_tasks:
            task_score = self._evaluate_physics_task(task)
            scores[task] = task_score
        
        self.results["sub_scores"] = scores
        self.results["overall_score"] = sum(scores.values()) / len(scores)
    
    def _run_robo_lab(self):
        """Run robot task evaluation"""
        robot_tasks = [
            "pick_and_place",
            "door_opening",
            "drawer_manipulation",
            "tool_picking",
            "obstacle_navigation",
            "human_handoff"
        ]
        
        scores = {}
        for task in robot_tasks:
            success_rate = self._evaluate_robot_task(task)
            scores[task] = success_rate * 100
        
        self.results["sub_scores"] = scores
        self.results["overall_score"] = sum(scores.values()) / len(scores)

# isaac_gr00t_control.py
# Isaac GR00T Robot Control Framework

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

class JointGroup(Enum):
    """Joint group definitions"""
    HEAD = "head"
    TORSO = "torso"
    LEFT_ARM = "left_arm"
    RIGHT_ARM = "right_arm"
    LEFT_LEG = "left_leg"
    RIGHT_LEG = "right_leg"
    LEFT_HAND = "left_hand"
    RIGHT_HAND = "right_hand"


@dataclass
class RobotConfig:
    """Robot configuration"""
    # Joint count configuration
    joint_counts: Dict[JointGroup, int] = None
    
    # Total degrees of freedom
    total_dof: int = 75
    
    # Body parameters
    height_m: float = 1.8
    weight_kg: float = 68.0
    
    # Joint limits (rad)
    joint_limits: Dict[str, Tuple[float, float]] = None
    
    # Joint velocity limits (rad/s)
    velocity_limits: Dict[str, float] = None
    
    def __post_init__(self):
        if self.joint_counts is None:
            self.joint_counts = {
                JointGroup.HEAD: 2,
                JointGroup.TORSO: 3,
                JointGroup.LEFT_ARM: 7,
                JointGroup.RIGHT_ARM: 7,
                JointGroup.LEFT_LEG: 6,
                JointGroup.RIGHT_LEG: 6,
                JointGroup.LEFT_HAND: 22,
                JointGroup.RIGHT_HAND: 22
            }
        
        if self.joint_limits is None:
            self.joint_limits = self._default_joint_limits()
        
        if self.velocity_limits is None:
            self.velocity_limits = self._default_velocity_limits()
    
    def _default_joint_limits(self) -> Dict[str, Tuple[float, float]]:
        """Default joint limits"""
        return {
            "head_pitch": (-0.5, 0.5),
            "head_yaw": (-1.0, 1.0),
            "torso_roll": (-0.3, 0.3),
            "torso_pitch": (-0.5, 0.5),
            "torso_yaw": (-1.5, 1.5),
            # Arm joints (7 DOF × 2)
            "shoulder_pitch": (-2.35, 2.35),
            "shoulder_roll": (-3.14, 3.14),
            "shoulder_yaw": (-2.09, 2.09),
            "elbow": (-2.61, 2.61),
            "wrist_roll": (-3.14, 3.14),
            "wrist_pitch": (-2.09, 2.09),
            "wrist_yaw": (-3.14, 3.14),
            # Leg joints (6 DOF × 2)
            "hip_yaw": (-1.22, 1.22),
            "hip_roll": (-1.57, 1.57),
            "hip_pitch": (-2.44, 2.44),
            "knee": (-2.53, -0.05),
            "ankle_pitch": (-0.52, 0.52),
            "ankle_roll": (-0.52, 0.52),
            # Hands (22 DOF × 2)
            "finger_*": (-1.57, 1.57),
        }
    
    def _default_velocity_limits(self) -> Dict[str, float]:
        """Default velocity limits"""
        return {
            "head": 2.0,
            "torso": 1.5,
            "arm": 3.0,
            "leg": 2.5,
            "hand": 5.0,
            "default": 2.0
        }


class IsaacGR00TController:
    """
    Isaac GR00T Controller
    
    Robot control framework based on Cosmos 3 world model
    """
    
    def __init__(self, config: RobotConfig):
        self.config = config
        self.cosmos_model = self._init_cosmos_model()
        self.state_estimator = self._init_state_estimator()
        self.motion_planner = self._init_motion_planner()
        self.low_level_controller = self._init_low_level_controller()
        
        # State machine
        self.current_state = "idle"
        self.target_pose = None
        
    def _init_cosmos_model(self):
        """Initialize Cosmos 3 world model"""
        from cosmos3_edge import Cosmos3EdgeEngine
        
        model = Cosmos3EdgeEngine(
            model_path="nvidia/cosmos-3-edge-3b",
            device="cuda"
        )
        return model
    
    def _init_state_estimator(self):
        """Initialize state estimator"""
        return StateEstimator(
            sensors=["camera", "imu", "ft_sensor"],
            update_rate_hz=100
        )
    
    def _init_motion_planner(self):
        """Initialize motion planner"""
        return MotionPlanner(
            algorithm="mpc",
            horizon_steps=20,
            dt=0.01  # 10ms control cycle
        )
    
    def _init_low_level_controller(self):
        """Initialize low-level controller"""
        return LowLevelController(
            control_mode="joint_torque",
            control_rate_hz=1000  # 1kHz
        )
    
    @torch.no_grad()
    def perceive_and_plan(
        self,
        rgb_images: List[np.ndarray],
        depth_images: List[np.ndarray],
        instruction: str
    ) -> Dict[str, np.ndarray]:
        """
        Perception and planning
        
        Core flow:
        1. Perceive current environment state
        2. Understand task instruction
        3. Use Cosmos 3 to predict future states
        4. Generate motion trajectory
        """
        # 1. State estimation
        current_state = self.state_estimator.estimate(
            rgb=rgb_images,
            depth=depth_images,
            imu=self.read_imu(),
            ft=self.read_force_torque()
        )
        
        # 2. World model inference
        world_prediction = self.cosmos_model.infer(
            video_frames=torch.from_numpy(np.stack(rgb_images)),
            instruction=instruction
        )
        
        # 3. Action sequence generation
        action_sequence = self._generate_action_sequence(
            current_state=current_state,
            world_prediction=world_prediction,
            goal=self._parse_goal(instruction)
        )
        
        # 4. Motion planning
        trajectory = self.motion_planner.plan(
            start_state=current_state,
            goal_action=action_sequence[0],
            horizon=20
        )
        
        return {
            "current_state": current_state,
            "predicted_states": world_prediction["future_states"],
            "action_sequence": action_sequence,
            "trajectory": trajectory
        }
    
    def execute(
        self, 
        trajectory: np.ndarray,
        duration_s: float = 5.0
    ):
        """
        Execute motion trajectory
        
        Args:
            trajectory: Target joint angle sequence [N, 75]
            duration_s: Execution time
        """
        # Safety check
        self._safety_check(trajectory)
        
        # Interpolate trajectory
        t = np.linspace(0, duration_s, len(trajectory))
        target_positions = trajectory
        
        # Execute control loop
        for i, t_i in enumerate(t):
            # Get current state
            current_joints = self.read_joint_positions()
            
            # Compute control command
            target = target_positions[i]
            torque_cmd = self._compute_torque(current_joints, target)
            
            # Send low-level control
            self.low_level_controller.send_torque(torque_cmd)
            
            # Wait for next cycle
            self._wait_for_next_cycle()
    
    def _safety_check(self, trajectory: np.ndarray):
        """Safety check"""
        # Check joint limits
        for joint_idx in range(trajectory.shape[1]):
            joint_values = trajectory[:, joint_idx]
            min_val, max_val = self.config.joint_limits.get(
                f"joint_{joint_idx}",
                (-3.14, 3.14)
            )
            
            if np.any(joint_values < min_val) or np.any(joint_values > max_val):
                raise SafetyError(f"Joint {joint_idx} limits violated")
        
        # Check velocity discontinuities
        velocities = np.diff(trajectory, axis=0)
        max_velocity = self.config.velocity_limits["default"]
        if np.any(np.abs(velocities) > max_velocity):
            raise SafetyError("Velocity limits violated")


class LowLevelController:
    """
    Low-Level Controller
    
    Runs at 1kHz, responsible for:
    - Joint torque control
    - Current control
    - Emergency stop
    """
    
    def __init__(self, control_mode: str, control_rate_hz: int):
        self.control_mode = control_mode
        self.rate_hz = control_rate_hz
        self.dt = 1.0 / control_rate_hz
        
        # PID parameters (torque control mode)
        self.kp = np.diag([50] * 75)
        self.ki = np.diag([0.1] * 75)
        self.kd = np.diag([10] * 75)
        
        self.integral_error = np.zeros(75)
        
    def _compute_torque(
        self,
        current_position: np.ndarray,
        target_position: np.ndarray,
        current_velocity: Optional[np.ndarray] = None
    ) -> np.ndarray:
        """
        Compute joint torque command
        
        Using PID control law:
        τ = Kp * e + Ki * ∫e dt + Kd * ė
        """
        error = target_position - current_position
        
        # Integral term
        self.integral_error += error * self.dt
        
        # Derivative term
        if current_velocity is not None:
            derivative = current_velocity
        else:
            derivative = (error - self.prev_error) / self.dt
        self.prev_error = error
        
        # PID control
        torque = (
            self.kp @ error +
            self.ki @ self.integral_error +
            self.kd @ derivative
        )
        
        return torque
    
    def send_torque(self, torque: np.ndarray):
        """Send torque command to actuators"""
        # Actual implementation needs to send via CAN bus
        pass


# Complete control loop example
def robot_control_loop():
    """
    Robot control main loop
    
    Typical running frequency: 100Hz
    """
    # Initialize
    config = RobotConfig()
    controller = IsaacGR00TController(config)
    
    # Wait for system ready
    controller.wait_for_ready()
    
    # Main loop
    while True:
        # 1. Read sensor data
        rgb = controller.read_camera_rgb()
        depth = controller.read_camera_depth()
        imu = controller.read_imu()
        
        # 2. Perception and planning (runs in separate thread)
        plan = controller.perceive_and_plan(
            rgb_images=rgb,
            depth_images=depth,
            instruction="Pick up the red cup on the table"
        )
        
        # 3. Execute trajectory
        controller.execute(
            trajectory=plan["trajectory"],
            duration_s=5.0
        )
        
        # 4. Monitor status
        if controller.check_fault():
            controller.emergency_stop()
            break
        
        # 5. Wait for next control cycle
        controller.sleep_until_next_cycle()

# Embodied AI training acceleration pipeline

class EmbodiedAITrainingPipeline:
    """
    Cosmos 3-based embodied AI training acceleration pipeline
    
    Core optimizations:
    1. Pretrained world model knowledge transfer
    2. Synthetic data generation
    3. Distributed training
    4. Mixed precision training
    """
    
    def __init__(self, config: Dict):
        self.config = config
        self.cosmos_model = self._load_cosmos_pretrain()
        self.data_generator = SyntheticDataGenerator()
        self.distributed_trainer = self._init_distributed()
    
    def train_embodied_model(
        self,
        robot_type: str,
        task_spec: Dict,
        num_epochs: int = 100
    ) -> Dict:
        """
        Train embodied intelligence model
        
        Compared to training from scratch, speedup可达 10-50x
        """
        print(f"Training embodied model for {robot_type}")
        
        # 1. Transfer learning initialization
        self._init_from_cosmos()
        
        # 2. Generate synthetic demo data
        synthetic_demos = self._generate_synthetic_data(task_spec)
        
        # 3. Real robot data fine-tuning
        real_demos = self._load_real_robot_data(robot_type)
        
        # 4. Mixed training
        combined_demos = self._mix_data(synthetic_demos, real_demos)
        
        # 5. Distributed training
        trained_model = self._distributed_train(
            data=combined_demos,
            epochs=num_epochs
        )
        
        return {
            "model": trained_model,
            "training_time": self._get_training_time(),
            "data_efficiency": self._compute_efficiency()
        }
    
    def _init_from_cosmos(self):
        """
        Transfer learning from Cosmos 3
        
        Freeze visual encoder and world model layers
        Only fine-tune action heads
        """
        # Load Cosmos 3 pretrained weights
        cosmos_weights = self._load_cosmos_weights()
        
        # Transfer visual encoder
        self.model.visual_encoder.load_state_dict(
            cosmos_weights["visual_encoder"],
            strict=False
        )
        
        # Transfer world model layers
        self.model.world_model.load_state_dict(
            cosmos_weights["world_model"],
            strict=False
        )
        
        # Freeze transferred layers
        for param in self.model.visual_encoder.parameters():
            param.requires_grad = False
        for param in self.model.world_model.parameters():
            param.requires_grad = False
        
        print("Loaded pretrained weights from Cosmos 3")
        print(f"Trainable parameters: {self._count_trainable_params()}")
    
    def _generate_synthetic_data(self, task_spec: Dict) -> List[Dict]:
        """
        Generate synthetic demo data
        
        Use Cosmos 3 world model to generate diverse training data
        """
        print("Generating synthetic demonstrations...")
        
        synthetic_data = []
        
        for _ in range(self.config.get("num_synthetic", 10000)):
            # Randomize scene
            initial_scene = self._randomize_scene(task_spec)
            
            # Use world model to generate successful trajectories
            trajectories = self.cosmos_model.generate_trajectories(
                task=task_spec["task"],
                initial_scene=initial_scene,
                num_variations=5
            )
            
            synthetic_data.extend(trajectories)
        
        print(f"Generated {len(synthetic_data)} synthetic demos")
        return synthetic_data
    
    def _distributed_train(
        self,
        data: List[Dict],
        epochs: int
    ) -> nn.Module:
        """
        Distributed training
        
        Use FSDP (Fully Sharded Data Parallel) optimization
        """
        from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
        from torch.distributed.fsdp import ShardingStrategy
        
        # Initialize distributed environment
        self.distributed_trainer.init_distributed()
        
        # Create data loader
        sampler = DistributedSampler(data)
        dataloader = DataLoader(
            data,
            batch_size=self.config["batch_size"],
            sampler=sampler,
            num_workers=4,
            pin_memory=True
        )
        
        # Wrap model
        model = FSDP(
            self.model,
            sharding_strategy=ShardingStrategy.FULL_SHARD,
            device_id=torch.cuda.current_device()
        )
        
        # Optimizer
        optimizer = torch.optim.AdamW(
            filter(lambda p: p.requires_grad, model.parameters()),
            lr=self.config["learning_rate"]
        )
        
        # Training loop
        for epoch in range(epochs):
            for batch in dataloader:
                loss = self._compute_loss(model, batch)
                
                loss.backward()
                optimizer.step()
                optimizer.zero_grad()
        
        return model
    
    def _compute_loss(self, model: nn.Module, batch: Dict) -> torch.Tensor:
        """Compute training loss"""
        # Action prediction loss
        pred_actions = model(batch["observations"])
        action_loss = nn.functional.mse_loss(
            pred_actions, batch["actions"]
        )
        
        # World model consistency loss
        world_loss = self._world_model_loss(model, batch)
        
        # Total loss
        total_loss = action_loss + 0.1 * world_loss
        
        return total_loss


# CUDA acceleration example
class CUDAAcceleratedOperations:
    """CUDA accelerated operations example"""
    
    @staticmethod
    @torch.cuda.amp.autocast()  # Mixed precision
    def batch_kinematics(
        joint_angles: torch.Tensor,  # [B, N_DOF]
        robot_model: dict
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Batch forward kinematics computation
        
        Execute on GPU with mixed precision
        """
        B, N = joint_angles.shape
        
        # Forward kinematics
        transforms = []
        for joint_idx in range(N):
            # Get DH parameters
            dh = robot_model["dh_params"][joint_idx]
            
            # GPU matrix operations
            angle = joint_angles[:, joint_idx]
            
            # Compute transformation matrix
            T = CUDAMatrixOps.homogeneous_transform(
                angle, dh["d"], dh["a"], dh["alpha"]
            )
            transforms.append(T)
        
        # Chain multiplication for end-effector pose
        end_effector_pose = transforms[0]
        for T in transforms[1:]:
            end_effector_pose = torch.matmul(end_effector_pose, T)
        
        # Jacobian matrix
        jacobian = CUDAMatrixOps.compute_jacobian(
            joint_angles, robot_model
        )
        
        return end_effector_pose, jacobian

# Environment installation
pip install cosmos3-sdk torch>=2.0.0 transformers>=4.30.0
pip install nvidia-cosmos3  # Official SDK

# Verify installation
python -c "import cosmos3; print(cosmos3.__version__)"

# cosmos3_quickstart.py
# Cosmos 3 Quick Start Examples

from cosmos3 import CosmosPipeline, CosmosConfig
import torch

# Initialize pipeline
config = CosmosConfig(
    version="nano",      # Options: super, nano, edge
    precision="fp16",    # Options: fp32, fp16, int8
    device="cuda"
)

pipeline = CosmosPipeline.from_pretrained(
    "nvidia/cosmos-3-nano-14b",
    config=config
)

# Example 1: World state understanding
def understand_world_state():
    """Understand current world state"""
    
    # Load video input
    video = pipeline.read_video("robot_workspace.mp4")
    
    # World state understanding
    world_state = pipeline.understand(
        video_frames=video,
        query="Describe the spatial relationships and physical properties of objects in this scene"
    )
    
    print(world_state)
    # {
    #     "objects": ["red cup", "wooden table", "blue box"],
    #     "spatial_relations": ["cup on table", "box next to cup"],
    #     "physical_properties": {
    #         "cup": "movable, mass 0.2kg",
    #         "table": "fixed, smooth surface"
    #     }
    # }


# Example 2: Action prediction
def predict_actions():
    """Predict actions based on current state"""
    
    video = pipeline.read_video("scene.mp4")
    
    # Action prediction
    actions = pipeline.predict_action(
        video_frames=video,
        instruction="Put the red cup on the blue box",
        num_candidates=5
    )
    
    for i, action in enumerate(actions):
        print(f"Plan {i+1}: {action['description']}")
        print(f"  Confidence: {action['confidence']:.2%}")
        print(f"  Predicted trajectory: {action['trajectory'][:3]}...")


# Example 3: World generation
def generate_world():
    """Generate consistent virtual scenes"""
    
    initial_condition = {
        "objects": [
            {"type": "box", "position": [0.5, 0, 0], "size": [0.1, 0.1, 0.1]},
            {"type": "table", "position": [0.5, 0, 0], "size": [0.5, 0.8, 0.02]}
        ],
        "camera": {"position": [1, 0.5, 1], "fov": 60}
    }
    
    # Generate video sequence
    video = pipeline.generate_world_states(
        initial_condition=initial_condition,
        num_frames=120,  # 4 seconds @ 30fps
        physics_consistent=True
    )
    
    # Save generated video
    pipeline.save_video(video, "generated_scene.mp4")


# Example 4: Robot control
def robot_control():
    """Control robot using Cosmos 3"""
    
    # Initialize robot controller
    controller = pipeline.init_robot_controller(
        robot_type="unitree_h2",
        hardware="jetson_agx_orin"
    )
    
    # Perceive current environment
    perception = controller.perceive()
    
    # Understand task
    task = "Pick up the red object and place it in the bin"
    
    # Generate action plan
    plan = pipeline.plan_action(
        perception=perception,
        task=task
    )
    
    # Execute plan
    controller.execute(plan)

# Cosmos 4 Expected Features (based on roadmap)
FUTURE_ROADMAP = {
    "cosmos_4": {
        "expected_release": "2027",
        "planned_features": [
            "Multi-agent collaboration",
            "Real-time physics simulation integration",
            "Haptic sensing support",
            "Enhanced causal reasoning",
            "Longer temporal context"
        ]
    },
    "ecosystem": {
        "gr00t_nano": "Compact design for medium-sized robots",
        "simulation_platform": "Deep Omniverse integration",
        "developer_tools": "More complete SDK and debugging tools"
    }
}