英伟达 Cosmos 3：全球首个开源具身智能世界模型深度剖析

# 传统方法的局限性示例
class TraditionalAI:
    """传统AI缺乏物理世界理解能力"""
    
    def __init__(self):
        self.capabilities = {
            "vision": "只能识别图像内容",
            "reasoning": "缺乏物理直觉",
            "prediction": "无法预测物体运动",
            "action": "无法生成协调动作"
        }
    
    def analyze_scene(self, image):
        """传统视觉分析 - 只能识别静态内容"""
        objects = self.detect_objects(image)
        return {
            "what": objects,  # "有一个杯子"
            "where": "桌上",   # 模糊的位置描述
            # 缺少：杯子会倒下吗？水会洒出来吗？
        }
    
    def plan_action(self, goal):
        """传统规划 - 无法考虑物理约束"""
        # 只能执行预定义的规则
        # 无法处理未知的物理交互
        pass

class CosmosWorldModel:
    """Cosmos 3 - 物理AI世界模型"""
    
    def __init__(self):
        self.capabilities = {
            "vision": "深度理解场景几何关系",
            "reasoning": "物理直觉推理",
            "prediction": "预测未来状态序列",
            "action": "生成协调动作序列"
        }
    
    def analyze_scene(self, image):
        """Cosmos场景分析 - 理解物理动态"""
        physics = self.understand_physics(image)
        return {
            "what": "杯子 + 水 + 倾斜桌面",
            "physics": {
                "gravity": "向下9.8m/s²",
                "杯中水量": "约200ml",
                "倾角": "30度",
                "预测": "2.3秒后水将洒出"
            },
            "action_plan": ["扶正杯子", "擦干桌面"]
        }

# Cosmos 3 训练数据配置
TRAINING_CONFIG = {
    "multimodal_data": {
        "text_corpus": "数十亿条文本-图像对",
        "video_clips": "数十亿分钟视频",
        "audio_samples": "数十亿条音效片段", 
        "action_trajectories": "数百万条机器人动作轨迹",
        "sensor_readings": "传感器数据点"
    },
    "data_sources": [
        " robotics_demos",      # 机器人演示视频
        " autonomous_driving",  # 自动驾驶数据
        " manipulation_tasks",  # 物体操控任务
        " human_activities",    # 人类活动视频
        " physics_simulations"  # 物理仿真数据
    ],
    "tokenization": {
        "video_tokenizer": "Cosmos Tokenizer",
        "text_tokenizer": "T5/Gemma",
        "action_tokenizer": "离散动作表示"
    }
}

class CosmosDataPipeline:
    """数据处理管道"""
    
    def __init__(self, config):
        self.config = config
        self.tokenizers = self._init_tokenizers()
    
    def _init_tokenizers(self):
        """初始化多模态tokenizer"""
        return {
            "video": VideoTokenizer(
                architecture="Cosmos Tokenizer",
                compression_ratio=64,  # 64x压缩
                spatial_tokens=256,
                temporal_tokens=32
            ),
            "text": TextTokenizer(
                model="T5-large",
                vocab_size=32000
            ),
            "action": ActionTokenizer(
                discretization_bins=256,
                action_dim=7  # 位置+姿态+夹爪
            )
        }
    
    def process_batch(self, batch):
        """批量处理多模态数据"""
        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 双Transformer核心架构
    
    特点:
    1. Visual Encoder Transformer: 处理视觉输入，提取世界状态
    2. Action Decoder Transformer: 基于世界状态生成动作序列
    3. 共享潜空间: 两个Transformer通过共享表示进行通信
    """
    
    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: 视觉输入编码
        self.visual_encoder = VisualEncoderTransformer(
            input_dim=visual_dim,
            hidden_dim=hidden_dim,
            num_heads=num_heads,
            num_layers=num_layers,
            dropout=dropout
        )
        
        # Action Decoder: 动作序列生成
        self.action_decoder = ActionDecoderTransformer(
            output_dim=action_dim,
            hidden_dim=hidden_dim,
            num_heads=num_heads,
            num_layers=num_layers,
            dropout=dropout
        )
        
        # 共享潜空间映射
        self.world_state_proj = nn.Linear(hidden_dim, hidden_dim)
        self.physical_constraints = PhysicalConstraintModule(hidden_dim)
        
        # 输出头
        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]:
        """
        前向传播
        
        Args:
            visual_input: 视频输入 [批次, 时序, 通道, 高, 宽]
            text_instruction: 文本指令 [批次, 序列长度]
            action_context: 历史动作上下文（可选）
        
        Returns:
            包含动作预测和状态预测的字典
        """
        # 1. 视觉编码
        visual_features = self.visual_encoder(visual_input)
        
        # 2. 世界状态理解
        world_state = self.world_state_proj(visual_features)
        physics_knowledge = self.physical_constraints(world_state)
        
        # 3. 动作解码
        action_output = self.action_decoder(
            encoded_state=world_state,
            text_instruction=text_instruction,
            action_context=action_context
        )
        
        # 4. 预测与生成
        predicted_actions = self.action_head(action_output)
        predicted_states = self.state_prediction_head(visual_features)
        
        return {
            "actions": predicted_actions,           # 动作序列
            "state_predictions": predicted_states,  # 未来状态预测
            "world_representation": world_state,    # 世界表示
            "physics_reasoning": physics_knowledge  # 物理推理结果
        }


class PhysicalConstraintModule(nn.Module):
    """
    物理约束模块
    
    学习物理世界的隐式规则：
    - 重力方向
    - 物体惯性
    - 碰撞检测
    - 摩擦力
    - 物体持久性
    """
    
    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种基础物理量
        )
        
        # 预定义的物理约束先验
        self.register_buffer(
            "gravity_vector", 
            torch.tensor([0, -9.81, 0])
        )
        
    def forward(self, world_state: torch.Tensor) -> torch.Tensor:
        """
        编码物理约束到状态表示中
        """
        physics_features = self.physics_encoder(world_state)
        
        # 物理特征含义：
        # [0]: 重力影响, [1]: 速度, [2-4]: 惯性张量
        # [5]: 摩擦系数, [6]: 弹性系数, [7]: 碰撞标志
        # ...
        
        return physics_features

class CosmosVisualReasoning:
    """Cosmos 3 视觉推理能力"""
    
    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:
        """
        深度场景理解
        
        不仅识别"有什么"，更理解"会发生什么"
        """
        # 几何关系理解
        geometry = self.scene_understanding.extract_3d_geometry(video_frames)
        
        # 物理关系分析
        physics_relations = self._analyze_physics(geometry)
        
        # 因果推理
        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]:
        """
        预测动作结果
        
        给定一个场景和动作，预测可能的结果
        """
        # 物理仿真预测
        future_states = self._simulate_physics(scene, action, steps=50)
        
        # 可能性评分
        likelihoods = self._score_outcomes(future_states)
        
        return sorted(
            zip(future_states, likelihoods),
            key=lambda x: x[1],
            reverse=True
        )[:5]  # 返回top 5预测


# 使用示例
reasoning = CosmosVisualReasoning("cosmos3-visual")
scene = reasoning.understand_scene(video_frames)

# 场景理解结果示例：
{
    "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 世界生成能力"""
    
    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]:
        """
        生成一致的世界状态序列
        
        核心特点：
        1. 物理一致性：物体运动符合物理定律
        2. 时间一致性：状态变化平滑连续
        3. 因果一致性：结果有合理的原因
        """
        # 初始状态token化
        initial_tokens = self._tokenize_condition(initial_condition)
        
        # 条件编码
        if conditioning:
            condition_tokens = self._encode_conditions(conditioning)
        else:
            condition_tokens = None
        
        # 生成视频帧
        generated_frames = self.video_diffusion.generate(
            initial_tokens=initial_tokens,
            condition_tokens=condition_tokens,
            num_steps=num_frames,
            guidance_scale=7.5
        )
        
        # 一致性后处理
        validated_frames = self.world_consistency.validate(generated_frames)
        
        return validated_frames
    
    def generate_counterfactual(
        self,
        actual_scene: Dict,
        hypothetical_change: Dict
    ) -> np.ndarray:
        """
        生成反事实场景
        
        "如果当初做了不同的选择，现在会怎样？"
        """
        # 确定分歧点
        divergence_point = self._find_divergence(hypothetical_change)
        
        # 生成另一条时间线
        alternate_timeline = self._generate_timeline(
            scene=actual_scene,
            divergence=divergence_point,
            alternative_action=hypothetical_change["action"]
        )
        
        return alternate_timeline


# 物理AI世界模型推理管道完整示例
class PhysicalAIInferencePipeline:
    """
    物理AI世界模型完整推理管道
    
    端到端处理：视频输入 → 世界理解 → 动作预测 → 执行验证
    """
    
    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):
        """初始化tokenizer"""
        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]:
        """
        完整推理流程
        
        Args:
            rgb_frames: RGB视频帧 [T, 3, H, W]
            depth_frames: 深度图 [T, 1, H, W]
            instruction: 文本指令 "pick up the red cup"
            num_prediction_steps: 预测步数
        
        Returns:
            包含预测动作和未来状态的字典
        """
        # 数据预处理
        rgb_tokens = self.tokenizers["video"](rgb_frames)
        depth_tokens = self.tokenizers["video"](depth_frames)
        text_tokens = self.tokenizers["text"](instruction)
        
        # 融合多模态输入
        multimodal_input = self._fuse_inputs(rgb_tokens, depth_tokens, text_tokens)
        
        # 世界模型推理
        with torch.no_grad():
            outputs = self.cosmos(
                visual_input=multimodal_input,
                text_instruction=text_tokens
            )
        
        # 动作预测解码
        predicted_actions = self._decode_actions(outputs["actions"])
        
        # 未来状态预测
        future_states = self._predict_future_states(
            current_state=outputs["world_representation"],
            actions=predicted_actions,
            steps=num_prediction_steps
        )
        
        # 物理可行性验证
        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):
        """多模态特征融合"""
        # 早期融合：拼接RGB和深度
        visual = torch.cat([rgb, depth], dim=-1)
        
        # 交叉注意力融合文本
        fused = self.cross_attention_fusion(visual, text)
        
        return fused

class CosmosActionPrediction:
    """Cosmos 3 动作预测能力"""
    
    def __init__(self, model):
        self.model = model
        self.action_space = self._define_action_space()
    
    def _define_action_space(self):
        """
        定义机器人动作空间
        
        典型的7自由度机械臂动作表示：
        - 6个关节角度 (radians)
        - 1个夹爪命令 (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,  # 预测10步动作
            "temporal_horizon": 1.0  # 1秒时间跨度
        }
    
    def predict_action_sequence(
        self,
        observation: Dict,
        goal: str,
        policy_type: str = "diffusion"
    ) -> np.ndarray:
        """
        预测动作序列
        
        支持多种策略：
        - diffusion: 扩散模型策略
        - autoregressive: 自回归策略
        - hierarchical: 分层策略
        """
        # 编码当前观测
        obs_encoding = self._encode_observation(observation)
        
        # 编码目标
        goal_encoding = self._encode_goal(goal)
        
        # 策略生成
        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)
        
        # 物理约束后处理
        actions = self._apply_physics_constraints(actions)
        
        return actions
    
    def _diffusion_denoise(self, obs, goal, num_steps=50):
        """扩散模型动作去噪"""
        # 从噪声开始
        action_sequence = torch.randn(
            self.action_space["action_horizon"],
            7  # 7维动作空间
        ).to(obs.device)
        
        # 迭代去噪
        for t in reversed(range(num_steps)):
            # 添加条件
            conditioned = torch.cat([action_sequence, obs, goal], dim=-1)
            
            # 预测噪声
            noise_pred = self.model.denoise(conditioned, t)
            
            # 去噪步骤
            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:
        """
        应用物理约束后处理
        
        确保动作：
        1. 不超过关节限位
        2. 满足速度/加速度约束
        3. 避免奇异姿态
        """
        constrained = actions.copy()
        
        # 关节限位约束
        for i, (min_j, max_j) in enumerate(self.action_space["joint_limits"]):
            constrained[:, i] = np.clip(constrained[:, i], min_j, max_j)
        
        # 速度约束（有限差分）
        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]
        
        # 平滑处理
        from scipy.ndimage import gaussian_filter1d
        constrained[:, :6] = gaussian_filter1d(constrained[:, :6], sigma=1, axis=0)
        
        return constrained

# cosmos3_deployment_config.yaml
# Cosmos 3 多版本部署配置文件

cosmos3_super:
  # 云端训练版本
  model:
    name: "Cosmos-3-Super-70B"
    architecture: "Dual-Transformer"
    parameters: 70000000000  # 70B参数
    
  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:
  # 云端推理版本
  model:
    name: "Cosmos-3-Nano-14B"
    parameters: 14000000000  # 14B参数
    
  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:
  # 边缘部署版本
  model:
    name: "Cosmos-3-Edge-3B"
    parameters: 3000000000  # 3B参数
    
  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 多版本部署代码

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

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


class Cosmos3ModelLoader:
    """Cosmos 3 模型加载器"""
    
    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:
        """
        加载指定版本的Cosmos 3模型
        
        自动处理：
        - 硬件检查
        - 模型分片
        - 精度转换
        - 量化（如适用）
        """
        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]
        
        # 获取模型注册信息
        registry = self.MODEL_REGISTRY[version]
        
        # 检查硬件兼容性
        self._check_hardware(version)
        
        # 加载模型
        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):
        """硬件兼容性检查"""
        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):
        """实际加载模型"""
        from transformers import AutoModelForCausalLM
        
        # 加载基础模型
        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            torch_dtype=self._get_dtype(precision),
            device_map="auto" if version != "super" else None
        )
        
        # 精度转换
        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):
        """获取PyTorch数据类型"""
        dtype_map = {
            "fp32": torch.float32,
            "fp16": torch.float16,
            "bf16": torch.bfloat16,
            "int8": torch.float16,  # int8在推理时转换
        }
        return dtype_map.get(precision, torch.float16)


# 边缘设备专用推理引擎
class Cosmos3EdgeEngine:
    """
    Cosmos 3 Edge 推理引擎
    
    专为Jetson Orin等边缘设备优化
    """
    
    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优化
        self._setup_tensorrt()
        
        # CUDA图优化
        self._enable_cuda_graphs()
    
    def _load_optimized_model(self, model_path: str):
        """加载优化后的模型"""
        # 使用TensorRT加载
        from tensorrt import runtime
        
        # 检查是否有优化的TensorRT引擎
        trt_engine_path = f"{model_path}/model.engine"
        
        if os.path.exists(trt_engine_path):
            # 加载已有的TensorRT引擎
            return self._load_trt_engine(trt_engine_path)
        else:
            # 从PyTorch模型转换
            return self._convert_to_tensorrt(model_path)
    
    def _setup_tensorrt(self):
        """配置TensorRT优化"""
        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):
        """启用CUDA Graphs减少CPU开销"""
        # CUDA Graphs可以显著减少小批量推理的CPU开销
        self.use_cuda_graphs = True
    
    @torch.no_grad()
    def infer(
        self, 
        video_frames: torch.Tensor,
        instruction: str
    ) -> Dict[str, torch.Tensor]:
        """
        边缘推理（优化版）
        
        目标：50ms延迟内完成推理
        """
        # 预处理
        input_ids, attention_mask = self.preprocess(instruction)
        
        # 使用CUDA Graph执行（如启用）
        if self.use_cuda_graphs:
            # 预热
            if not hasattr(self, '_cuda_graph_warmed'):
                self._cuda_graph_warmed = True
            
            # 执行捕获的图
            return self._run_cuda_graph(input_ids, attention_mask)
        
        # 普通推理
        outputs = self.model(
            input_ids=input_ids.cuda(),
            attention_mask=attention_mask.cuda()
        )
        
        return self.postprocess(outputs)

# Cosmos 3 评测结果
EVALUATION_RESULTS = {
    "Artificial Analysis": {
        "description": "综合能力评测（推理、代码、问答）",
        "score": 92.4,
        "rank": "#1 (开源模型)",
        "metrics": {
            "reasoning": 91.2,
            "coding": 89.7,
            "qa": 94.1,
            "math": 90.3
        }
    },
    "Physics-IQ": {
        "description": "物理智能专项评测",
        "score": 88.6,
        "rank": "#1 (开源模型)",
        "metrics": {
            "object_interaction": 91.2,
            "trajectory_prediction": 87.4,
            "physical_reasoning": 86.9,
            "collision_understanding": 89.1
        }
    },
    "RoboLab": {
        "description": "机器人任务评测",
        "score": 85.3,
        "rank": "#1 (开源模型)",
        "subtasks": {
            "pick_and_place": 92.1,
            "tool_use": 83.4,
            "navigation": 87.6,
            "manipulation": 84.2
        }
    }
}

class EvaluationBenchmark:
    """评测基准"""
    
    def __init__(self, model, benchmark_name: str):
        self.model = model
        self.benchmark_name = benchmark_name
        self.results = {}
    
    def run_full_benchmark(self) -> Dict:
        """运行完整评测"""
        self.results = {
            "overall_score": 0,
            "sub_scores": {},
            "metadata": {
                "model": self.model.name,
                "timestamp": datetime.now().isoformat(),
                "hardware": self._get_hardware_info()
            }
        }
        
        # 按评测类型执行
        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):
        """运行物理智能评测"""
        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):
        """运行机器人任务评测"""
        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 机器人控制框架

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

class JointGroup(Enum):
    """关节组定义"""
    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:
    """机器人配置"""
    # 关节数量配置
    joint_counts: Dict[JointGroup, int] = None
    
    # 总自由度
    total_dof: int = 75
    
    # 身体参数
    height_m: float = 1.8
    weight_kg: float = 68.0
    
    # 关节限位 (rad)
    joint_limits: Dict[str, Tuple[float, float]] = None
    
    # 关节速度限制 (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]]:
        """默认关节限位"""
        return {
            # H2 Plus标准关节限位
            "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),
            # 手臂关节 (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),
            # 腿部关节 (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),  # 膝关节通常是单侧限位
            "ankle_pitch": (-0.52, 0.52),
            "ankle_roll": (-0.52, 0.52),
            # 灵巧手 (22 DOF × 2)
            "finger_*": (-1.57, 1.57),  # 通用手指关节
        }
    
    def _default_velocity_limits(self) -> Dict[str, float]:
        """默认速度限制"""
        return {
            "head": 2.0,      # rad/s
            "torso": 1.5,
            "arm": 3.0,
            "leg": 2.5,
            "hand": 5.0,
            "default": 2.0
        }


class IsaacGR00TController:
    """
    Isaac GR00T 控制器
    
    基于Cosmos 3世界模型的机器人控制框架
    """
    
    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()
        
        # 状态机
        self.current_state = "idle"
        self.target_pose = None
        
    def _init_cosmos_model(self):
        """初始化Cosmos 3世界模型"""
        from cosmos3_edge import Cosmos3EdgeEngine
        
        model = Cosmos3EdgeEngine(
            model_path="nvidia/cosmos-3-edge-3b",
            device="cuda"
        )
        return model
    
    def _init_state_estimator(self):
        """初始化状态估计器"""
        return StateEstimator(
            sensors=["camera", "imu", "ft_sensor"],
            update_rate_hz=100
        )
    
    def _init_motion_planner(self):
        """初始化运动规划器"""
        return MotionPlanner(
            algorithm="mpc",
            horizon_steps=20,
            dt=0.01  # 10ms控制周期
        )
    
    def _init_low_level_controller(self):
        """初始化低层控制器"""
        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]:
        """
        感知与规划
        
        核心流程：
        1. 感知当前环境状态
        2. 理解任务指令
        3. 使用Cosmos 3预测未来状态
        4. 生成运动轨迹
        """
        # 1. 状态估计
        current_state = self.state_estimator.estimate(
            rgb=rgb_images,
            depth=depth_images,
            imu=self.read_imu(),
            ft=self.read_force_torque()
        )
        
        # 2. 世界模型推理
        world_prediction = self.cosmos_model.infer(
            video_frames=torch.from_numpy(np.stack(rgb_images)),
            instruction=instruction
        )
        
        # 3. 动作序列生成
        action_sequence = self._generate_action_sequence(
            current_state=current_state,
            world_prediction=world_prediction,
            goal=self._parse_goal(instruction)
        )
        
        # 4. 运动规划
        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
    ):
        """
        执行运动轨迹
        
        Args:
            trajectory: 目标关节角度序列 [N, 75]
            duration_s: 执行时间
        """
        # 安全检查
        self._safety_check(trajectory)
        
        # 插值轨迹
        t = np.linspace(0, duration_s, len(trajectory))
        target_positions = trajectory
        
        # 执行控制循环
        for i, t_i in enumerate(t):
            # 获取当前状态
            current_joints = self.read_joint_positions()
            
            # 计算控制命令
            target = target_positions[i]
            torque_cmd = self._compute_torque(current_joints, target)
            
            # 发送低层控制
            self.low_level_controller.send_torque(torque_cmd)
            
            # 等待下一周期
            self._wait_for_next_cycle()
    
    def _safety_check(self, trajectory: np.ndarray):
        """安全检查"""
        # 检查关节限位
        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")
        
        # 检查速度突变
        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")
        
        # 检查力矩限制
        # （实际实现需要考虑动力学模型）


class LowLevelController:
    """
    低层控制器
    
    运行在1kHz，负责：
    - 关节力矩控制
    - 电流控制
    - 紧急停止
    """
    
    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参数（力矩控制模式）
        self.kp = np.diag([50] * 75)  # 比例增益
        self.ki = np.diag([0.1] * 75)  # 积分增益
        self.kd = np.diag([10] * 75)   # 微分增益
        
        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:
        """
        计算关节力矩命令
        
        使用PID控制律：
        τ = Kp * e + Ki * ∫e dt + Kd * ė
        """
        error = target_position - current_position
        
        # 积分项
        self.integral_error += error * self.dt
        
        # 微分项
        if current_velocity is not None:
            derivative = current_velocity
        else:
            derivative = (error - self.prev_error) / self.dt
        self.prev_error = error
        
        # PID控制
        torque = (
            self.kp @ error +
            self.ki @ self.integral_error +
            self.kd @ derivative
        )
        
        return torque
    
    def send_torque(self, torque: np.ndarray):
        """发送力矩命令到执行器"""
        # 实际实现需要通过CAN总线发送
        # 这里用占位符表示
        pass


# 完整控制循环示例
def robot_control_loop():
    """
    机器人控制主循环
    
    典型运行频率：100Hz
    """
    # 初始化
    config = RobotConfig()
    controller = IsaacGR00TController(config)
    
    # 等待系统就绪
    controller.wait_for_ready()
    
    # 主循环
    while True:
        # 1. 读取传感器数据
        rgb = controller.read_camera_rgb()
        depth = controller.read_camera_depth()
        imu = controller.read_imu()
        
        # 2. 感知与规划 (在单独线程中运行)
        plan = controller.perceive_and_plan(
            rgb_images=rgb,
            depth_images=depth,
            instruction="Pick up the red cup on the table"
        )
        
        # 3. 执行轨迹
        controller.execute(
            trajectory=plan["trajectory"],
            duration_s=5.0
        )
        
        # 4. 监控状态
        if controller.check_fault():
            controller.emergency_stop()
            break
        
        # 5. 等待下一个控制周期
        controller.sleep_until_next_cycle()

# 具身智能训练加速pipeline

class EmbodiedAITrainingPipeline:
    """
    基于Cosmos 3的具身智能训练加速pipeline
    
    核心优化：
    1. 预训练的世界模型知识迁移
    2. 合成数据生成
    3. 分布式训练
    4. 混合精度训练
    """
    
    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:
        """
        训练具身智能模型
        
        相比从零训练，加速比可达 10-50x
        """
        print(f"Training embodied model for {robot_type}")
        
        # 1. 迁移学习初始化
        self._init_from_cosmos()
        
        # 2. 生成合成演示数据
        synthetic_demos = self._generate_synthetic_data(task_spec)
        
        # 3. 真实机器人数据微调
        real_demos = self._load_real_robot_data(robot_type)
        
        # 4. 混合训练
        combined_demos = self._mix_data(synthetic_demos, real_demos)
        
        # 5. 分布式训练
        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):
        """
        从Cosmos 3迁移学习
        
        冻结视觉编码器和世界模型层
        只微调动作头
        """
        # 加载Cosmos 3预训练权重
        cosmos_weights = self._load_cosmos_weights()
        
        # 迁移视觉编码器
        self.model.visual_encoder.load_state_dict(
            cosmos_weights["visual_encoder"],
            strict=False
        )
        
        # 迁移世界模型层
        self.model.world_model.load_state_dict(
            cosmos_weights["world_model"],
            strict=False
        )
        
        # 冻结已迁移层
        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]:
        """
        生成合成演示数据
        
        使用Cosmos 3世界模型生成多样化训练数据
        """
        print("Generating synthetic demonstrations...")
        
        synthetic_data = []
        
        for _ in range(self.config.get("num_synthetic", 10000)):
            # 随机初始化场景
            initial_scene = self._randomize_scene(task_spec)
            
            # 使用世界模型生成成功轨迹
            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:
        """
        分布式训练
        
        使用FSDP (Fully Sharded Data Parallel) 优化
        """
        from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
        from torch.distributed.fsdp import ShardingStrategy
        
        # 初始化分布式环境
        self.distributed_trainer.init_distributed()
        
        # 创建数据加载器
        sampler = DistributedSampler(data)
        dataloader = DataLoader(
            data,
            batch_size=self.config["batch_size"],
            sampler=sampler,
            num_workers=4,
            pin_memory=True
        )
        
        # 包装模型
        model = FSDP(
            self.model,
            sharding_strategy=ShardingStrategy.FULL_SHARD,
            device_id=torch.cuda.current_device()
        )
        
        # 优化器
        optimizer = torch.optim.AdamW(
            filter(lambda p: p.requires_grad, model.parameters()),
            lr=self.config["learning_rate"]
        )
        
        # 训练循环
        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:
        """计算训练损失"""
        # 动作预测损失
        pred_actions = model(batch["observations"])
        action_loss = nn.functional.mse_loss(
            pred_actions, batch["actions"]
        )
        
        # 世界模型一致性损失
        world_loss = self._world_model_loss(model, batch)
        
        # 总损失
        total_loss = action_loss + 0.1 * world_loss
        
        return total_loss


# CUDA加速示例
class CUDAAcceleratedOperations:
    """CUDA加速操作示例"""
    
    @staticmethod
    @torch.cuda.amp.autocast()  # 混合精度
    def batch_kinematics(
        joint_angles: torch.Tensor,  # [B, N_DOF]
        robot_model: dict
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        批量正运动学计算
        
        在GPU上执行，支持混合精度
        """
        B, N = joint_angles.shape
        
        # 前向运动学
        transforms = []
        for joint_idx in range(N):
            # 获取DH参数
            dh = robot_model["dh_params"][joint_idx]
            
            # GPU上的矩阵运算
            angle = joint_angles[:, joint_idx]
            
            # 计算变换矩阵
            T = CUDAMatrixOps.homogeneous_transform(
                angle, dh["d"], dh["a"], dh["alpha"]
            )
            transforms.append(T)
        
        # 链式相乘得到末端执行器位姿
        end_effector_pose = transforms[0]
        for T in transforms[1:]:
            end_effector_pose = torch.matmul(end_effector_pose, T)
        
        # 雅可比矩阵
        jacobian = CUDAMatrixOps.compute_jacobian(
            joint_angles, robot_model
        )
        
        return end_effector_pose, jacobian

# 环境安装
pip install cosmos3-sdk torch>=2.0.0 transformers>=4.30.0
pip install nvidia-cosmos3  # 官方SDK

# 验证安装
python -c "import cosmos3; print(cosmos3.__version__)"

# cosmos3_quickstart.py
# Cosmos 3 快速上手示例

from cosmos3 import CosmosPipeline, CosmosConfig
import torch

# 初始化管道
config = CosmosConfig(
    version="nano",      # 可选: super, nano, edge
    precision="fp16",    # 可选: fp32, fp16, int8
    device="cuda"
)

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

# 示例1: 世界状态理解
def understand_world_state():
    """理解当前世界状态"""
    
    # 加载视频输入
    video = pipeline.read_video("robot_workspace.mp4")
    
    # 世界状态理解
    world_state = pipeline.understand(
        video_frames=video,
        query="描述这个场景中物体的空间关系和物理属性"
    )
    
    print(world_state)
    # {
    #     "objects": ["红色杯子", "木桌", "蓝色盒子"],
    #     "spatial_relations": ["杯子在桌子上", "盒子在杯子旁边"],
    #     "physical_properties": {
    #         "杯子": "可移动, 质量0.2kg",
    #         "桌子": "固定, 表面光滑"
    #     }
    # }


# 示例2: 动作预测
def predict_actions():
    """基于当前状态预测动作"""
    
    video = pipeline.read_video("scene.mp4")
    
    # 动作预测
    actions = pipeline.predict_action(
        video_frames=video,
        instruction="将红色杯子放到蓝色盒子上",
        num_candidates=5
    )
    
    for i, action in enumerate(actions):
        print(f"方案 {i+1}: {action['description']}")
        print(f"  置信度: {action['confidence']:.2%}")
        print(f"  预测轨迹: {action['trajectory'][:3]}...")


# 示例3: 场景生成
def generate_world():
    """生成一致的虚拟场景"""
    
    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}
    }
    
    # 生成视频序列
    video = pipeline.generate_world_states(
        initial_condition=initial_condition,
        num_frames=120,  # 4秒 @ 30fps
        physics_consistent=True
    )
    
    # 保存生成的视频
    pipeline.save_video(video, "generated_scene.mp4")


# 示例4: 机器人控制
def robot_control():
    """使用Cosmos 3控制机器人"""
    
    # 初始化机器人控制器
    controller = pipeline.init_robot_controller(
        robot_type="unitree_h2",
        hardware="jetson_agx_orin"
    )
    
    # 感知当前环境
    perception = controller.perceive()
    
    # 理解任务
    task = "Pick up the red object and place it in the bin"
    
    # 生成动作计划
    plan = pipeline.plan_action(
        perception=perception,
        task=task
    )
    
    # 执行计划
    controller.execute(plan)

# Cosmos 4 预期特性 (基于 roadmap)
FUTURE_ROADMAP = {
    "cosmos_4": {
        "expected_release": "2027",
        "planned_features": [
            "多智能体协作",
            "实时物理仿真集成",
            "触觉感知支持",
            "更强的因果推理能力",
            "更长的时序上下文"
        ]
    },
    "ecosystem": {
        "gr00t_nano": "面向中小型机器人的紧凑设计",
        "simulation_platform": "Omniverse深度集成",
        "developer_tools": "更完善的SDK和调试工具"
    }
}