Mistral Robostral Navigate 8B：法国AI公司跨界机器人赛道，8B参数+单目摄像头实现工业级自主导航的轻量化范式

"""
Mistral Robostral Navigate 8B: Monocular Visual Navigation Architecture
"""

import numpy as np
from typing import Tuple, List


class VisualEncoder:
    """视觉编码器：将单目摄像头图像压缩为特征向量"""
    
    def __init__(self, input_size: Tuple[int, int] = (224, 224)):
        self.input_size = input_size
        self.feature_dim = 1024
    
    def encode(self, image: np.ndarray) -> np.ndarray:
        """
        将单目图像编码为特征向量。
        
        架构设计：
        - 使用轻量级视觉Transformer（ViT-Tiny）
        - 参数量：~20M
        - 输出：1024维特征向量
        """
        # 模拟图像编码过程
        features = np.random.randn(self.feature_dim)
        return features / np.linalg.norm(features)


class NavigationTransformer:
    """导航Transformer：8B参数的核心模型"""
    
    def __init__(self):
        self.hidden_size = 4096
        self.num_layers = 32
        self.num_heads = 32
        
        # 视觉-语言-动作联合嵌入
        self.visual_proj = np.random.randn(1024, self.hidden_size)
        self.text_proj = np.random.randn(4096, self.hidden_size)
        self.action_head = np.random.randn(self.hidden_size, 6)  # 6-DoF动作
    
    def forward(self, 
                visual_features: np.ndarray,
                instruction_embedding: np.ndarray,
                past_trajectory: List[np.ndarray]) -> np.ndarray:
        """
        前向传播：从视觉+语言输入预测动作
        
        Args:
            visual_features: 当前视觉特征 (1024,)
            instruction_embedding: 导航指令嵌入 (4096,)
            past_trajectory: 历史轨迹点列表
        
        Returns:
            6-DoF动作向量 (vx, vy, vz, roll, pitch, yaw)
        """
        # 融合视觉和语言特征
        vis = self.visual_proj.T @ visual_features
        txt = self.text_proj.T @ instruction_embedding
        
        # 加入历史轨迹信息
        if past_trajectory:
            traj_feat = np.mean(past_trajectory[-10:], axis=0)
            combined = vis + txt + traj_feat
        else:
            combined = vis + txt
        
        # 预测动作
        action = self.action_head.T @ combined
        return action


class RobostralNavigate8B:
    """
    Robostral Navigate 8B完整系统
    
    架构：Visual Encoder (20M) + Navigation Transformer (8B)
    输入：单目摄像头RGB图像 + 自然语言指令
    输出：6-DoF动作向量
    """
    
    def __init__(self):
        self.encoder = VisualEncoder()
        self.transformer = NavigationTransformer()
        self.trajectory_buffer = []
        self.max_buffer_size = 100
    
    def navigate(self, 
                 image: np.ndarray, 
                 instruction: str) -> np.ndarray:
        """
        单步导航推理
        
        Args:
            image: 当前帧RGB图像
            instruction: 导航指令，如"go to the loading dock"
        
        Returns:
            6-DoF动作指令
        """
        # 编码视觉特征
        visual_features = self.encoder.encode(image)
        
        # 编码指令（简化）
        instruction_embedding = self._encode_instruction(instruction)
        
        # Transformer推理
        action = self.transformer.forward(
            visual_features, 
            instruction_embedding,
            self.trajectory_buffer
        )
        
        # 更新轨迹缓冲区
        self.trajectory_buffer.append(action)
        if len(self.trajectory_buffer) > self.max_buffer_size:
            self.trajectory_buffer.pop(0)
        
        return action
    
    def _encode_instruction(self, instruction: str) -> np.ndarray:
        """编码自然语言指令"""
        # 使用Mistral的文本编码器
        return np.random.randn(4096)  # 简化实现


class NavigationEvaluator:
    """导航性能评估器"""
    
    @staticmethod
    def evaluate_success_rate(model: RobostralNavigate8B,
                               scenarios: List[dict]) -> dict:
        """评估导航成功率"""
        successes = 0
        total = len(scenarios)
        collision_free = 0
        path_lengths = []
        
        for scenario in scenarios:
            # 模拟导航
            result = NavigationEvaluator._simulate_navigation(
                model, scenario
            )
            
            if result["success"]:
                successes += 1
            if result["collision_free"]:
                collision_free += 1
            path_lengths.append(result["path_length"])
        
        return {
            "success_rate": successes / total,
            "collision_free_rate": collision_free / total,
            "avg_path_length": np.mean(path_lengths),
            "path_efficiency": np.mean([
                scenario["optimal_length"] / length
                for scenario, length in zip(scenarios, path_lengths)
            ]),
        }
    
    @staticmethod
    def _simulate_navigation(model, scenario: dict) -> dict:
        """模拟单次导航过程"""
        import random
        return {
            "success": random.random() > 0.15,
            "collision_free": random.random() > 0.08,
            "path_length": scenario["optimal_length"] * random.uniform(1.0, 1.3),
        }


# 对比分析
class NavigationComparison:
    """不同导航方案对比"""
    
    @staticmethod
    def compare():
        configs = [
            {
                "name": "Robostral Navigate 8B",
                "sensors": "单目摄像头",
                "params": "8B",
                "sensor_cost": "$50",
                "inference_speed": "30 FPS",
                "power": "15W",
                "deployment": "边缘端",
            },
            {
                "name": "传统LiDAR+SLAM",
                "sensors": "LiDAR+IMU+摄像头",
                "params": "N/A",
                "sensor_cost": "$5000",
                "inference_speed": "10-20 FPS",
                "power": "100W",
                "deployment": "工控机",
            },
            {
                "name": "端到端VLA（大模型）",
                "sensors": "多摄像头+LiDAR",
                "params": "70B+",
                "sensor_cost": "$3000",
                "inference_speed": "5-10 FPS",
                "power": "300W+",
                "deployment": "云端推理",
            },
        ]
        
        print("=" * 80)
        print("Robot Navigation Solution Comparison")
        print("=" * 80)
        print(f"{'方案':<28} {'传感器':<16} {'参数':<10} {'成本':<12} {'速度':<12} {'功耗':<10}")
        print("-" * 80)
        for cfg in configs:
            print(f"{cfg['name']:<28} {cfg['sensors']:<16} {cfg['params']:<10} "
                  f"{cfg['sensor_cost']:<12} {cfg['inference_speed']:<12} {cfg['power']:<10}")
        print("=" * 80)


if __name__ == "__main__":
    NavigationComparison.compare()
    
    # 评估导航性能
    evaluator = NavigationEvaluator()
    model = RobostralNavigate8B()
    
    scenarios = [
        {"optimal_length": 50, "obstacles": 5},
        {"optimal_length": 100, "obstacles": 12},
        {"optimal_length": 200, "obstacles": 20},
    ]
    
    results = evaluator.evaluate_success_rate(model, scenarios)
    print(f"\nNavigation Performance:")
    print(f"  Success Rate:      {results['success_rate']*100:.1f}%")
    print(f"  Collision-Free:    {results['collision_free_rate']*100:.1f}%")
    print(f"  Path Efficiency:   {results['path_efficiency']*100:.1f}%")