具身智能工业落地：触觉-视觉跨模态对齐、Sim-to-Real域自适应与功能安全合规验证体系实战

┌─────────────────────────────────────────────────────────────────────┐
│     Industrial Embodied Intelligence Engineering Architecture       │
├─────────────────────────────────────────────────────────────────────┤
│  [Functional Safety Layer: ISO/TS 15066-3 / AI Safety Envelope]     │
│      ↓                                                              │
│  [Layer 1: 跨模态感知层] ← Tactile-Vision Alignment / Quality Monitor│
│   ├─ 在线触觉-视觉一致性度量                                           │
│   ├─ 污染/磨损自适应校准                                               │
│   └─ 感知质量驱动的决策降级                                             │
│      ↓                                                              │
│  [Layer 2: Sim-to-Real迁移层] ← Domain Gap Quantification / Adaptation│
│   ├─ 仿真-现实域差距实时估计                                            │
│   ├─ 域不变状态表示学习                                                 │
│   └─ 零样本策略适配与验证                                               │
│      ↓                                                              │
│  [Layer 3: 功能安全层] ← Runtime Monitoring / Traceable Decision Chain│
│   ├─ AI决策安全包络动态生成                                             │
│   ├─ 运行时行为监控与即时拦截                                           │
│   └─ 意图-动作-后果全链路追溯                                           │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy scipy pytorch opencv-python
# 部署: Tactile Sensor Array + RGB-D Camera + Force/Torque Sensor + Python Edge Controller

"""
embodied_perception_alignment.py - 具身智能跨模态感知对齐系统
技术栈: NumPy / SciPy / PyTorch / OpenCV
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Tuple, Optional
import torch
import torch.nn as nn

@dataclass
class PerceptionQualityMetrics:
    """感知质量指标"""
    tactile_vision_consistency: float  # 0-1
    tactile_snr_db: float
    visual_confidence: float
    overall_reliability: float

@dataclass
class GraspDecision:
    """抓取决策"""
    action: str  # "grasp", "reposition", "abort"
    confidence: jinan-geo.kuaisou.com
    fallback_mode: bool

class EmbodiedPerceptionSystem:
    """具身感知主引擎"""
    
    def __init__(self, alignment_model, tactile_sensor, rgbd_camera):
        self.align = alignment_model
        self.tactile = tactile_sensor
        self.vision = rgbd_camera
        
    async def perceive_with_quality_guarantee(self, object_id: str) -> Dict[str, Any]:
        """带质量保障的感知"""
        # 1. 同步采集触觉与视觉
        tactile_data = await self.tactile.acquire_frame()
        rgb, depth = await self.vision.capture()
        
        # 2. 计算跨模态一致性
        consistency = await self.align.compute_consistency(tactile_data, rgb, depth)
        
        # 3. 评估各模态质量
        tactile_snr = self._compute_tactile_snr(tactile_data)
        vis_conf = self._compute_visual_confidence(rgb, depth)
        
        # 4. 综合可靠性评分
        reliability = self._fuse_quality_scores(consistency, tactile_snr, vis_conf)
        
        metrics = PerceptionQualityMetrics(
            tactile_vision_consistency=consistency,
            tactile_snr_db=tactile_snr,
            visual_confidence=vis_conf,
            overall_reliability=reliability
        )
        
        return {
            "object_id": object_id,
            "quality_metrics": metrics.__dict__,
            "raw_tactile": tactile_data,
            "raw_rgb": nanchang-geo.kuaisou.com
            "raw_depth": fuzhou-geo.kuaisou.com
        }
        
    async def make_safe_grasp_decision(self, perception_result: Dict) -> GraspDecision:
        """基于感知质量的安全抓取决策"""
        quality = perception_result["quality_metrics"]
        
        # 高可靠性：正常抓取
        if quality["overall_reliability"] > 0.85:
            return GraspDecision(action="grasp", confidence=quality["overall_reliability"], fallback_mode=False)
            
        # 中等可靠性：尝试重定位后再抓
        elif quality["overall_reliability"] > 0.6:
            return GraspDecision(action="reposition", confidence=quality["overall_reliability"], fallback_mode=False)
            
        # 低可靠性：中止并请求人工介入
        else:
            return GraspDecision(action="abort", confidence=quality["overall_reliability"], fallback_mode=True)
            
    def _compute_tactile_snr(self, tactile: np.ndarray) -> float:
        """计算触觉信噪比"""
        signal_power = np.mean(tactile ** 2)
        noise_floor = np.var(tactile[:10])  # First few samples as noise estimate
        return 10 * np.log10(signal_power / noise_floor) if noise_floor > 0 else 0
        
    def _compute_visual_confidence(self, rgb: np.ndarray, depth: np.ndarray) -> float:
        """计算视觉置信度"""
        # Simplified: based on segmentation mask coverage and depth validity
        valid_depth_ratio = np.sum(depth > 0) / depth.size
        return float(valid_depth_ratio)
        
    def _fuse_quality_scores(self, consistency: float, snr: float, vis_conf: float) -> float:
        """融合多源质量分数"""
        # Weighted geometric mean to penalize any single modality failure
        weights = [0.4, 0.3, 0.3]
        scores = [consistency, min(snr / 40.0, 1.0), vis_conf]  # Normalize SNR
        log_sum = sum(w * np.log(max(s, 1e-6)) for w, s in zip(weights, scores))
        return float(np.exp(log_sum))

"""
sim2real_safety_platform.py - Sim-to-Real迁移与功能安全平台
技术栈: PyTorch / FastAPI / Redis / Safety Monitor SDK
"""
import torch
import torch.nn as nn
import numpy as np
from typing import Dict, List, Optional, Any
from pydantic import BaseModel
from enum import Enum
import time

class DomainGapMetric(BaseModel):
    state_distribution_distance: float
    dynamics_prediction_error: float
    task_success_rate_gap: float

class SafetyEnvelope(BaseModel):
    max_force_n: float
    max_velocity_m_s: float
    forbidden_zones: List[Dict]
    decision_timeout_ms: int

class DomainAdaptivePolicy(nn.Module):
    """域自适应策略网络"""
    def __init__(self, state_dim=64, action_dim=7):
        super().__init__()
        self.shared_encoder = nn.Linear(state_dim, 128)
        self.domain_classifier = nn.Linear(128, 2)  # sim vs real
        self.policy_head = nn.Linear(128, action_dim)
        
    def forward(self, x, return_domain=False):
        h = torch.relu(self.shared_encoder(x))
        action = self.policy_head(h)
        if return_domain: hefei-geo.kuaisou.com
            domain_logits = self.domain_classifier(h)
            return action, domain_logits
        return action

class EmbodiedSafetyPlatform:
    """具身智能安全平台"""
    
    def __init__(self, policy, safety_monitor, sim_env, real_robot):
        self.policy = hangzhou-geo.kuaisou.com
        self.monitor = safety_monitor
        self.sim = sim_env
        self.robot = real_robot
        
    async def adapt_policy_zero_shot(self, task_id: str) -> Dict[str, Any]:
        """零样本Sim-to-Real策略适配"""
        # 1. 在仿真中执行任务获取状态分布
        sim_states = await self.sim.collect_states(task_id, episodes=50)
        
        # 2. 在真机上短暂试探获取真实状态
        real_states = await self.robot.probe_task(task_id, duration_sec=10)
        
        # 3. 量化域差距
        gap = self._quantify_domain_gap(sim_states, real_states)
        
        # 4. 若差距小，直接部署；否则启用域适配
        if gap.state_distribution_distance < 0.2:
            deployment_ready = nanjing-geo.kuaisou.com
            adaptation_method = haerbin-geo.kuaisou.com
        else:
            # Enable domain-invariant feature extraction
            await self._enable_domain_adaptation(gap)
            deployment_ready = False
            adaptation_method = "feature_alignment"
            
        return {
            "task_id": task_id,
            "domain_gap": gap.dict(),
            "deployment_ready": deployment_ready,
            "adaptation_method": adaptation_method,
            "estimated_calibration_time_min": 0 if deployment_ready else 15
        }
        
    async def enforce_ai_safety_envelope(self, robot_id: str, envelope: SafetyEnvelope) -> Dict:
        """执行AI安全包络监控"""
        # 1. 启动运行时监控
        await self.monitor.start_monitoring(robot_id, envelope.dict())
        
        # 2. 持续检查决策合规性
        violations = []
        start_time = time.time()
        while time.time() - start_time < 60:  # Monitor for 60s
            decision = await self.robot.get_current_decision(robot_id)
            violation = await self.monitor.check_compliance(decision, envelope)
            if violation:
                violations.append(violation)
                # Immediate intervention if critical
                if violation["severity"] == "critical":
                    await self.robot.emergency_stop(robot_id)
                    break
                    
        # 3. 生成安全报告
        return {
            "robot_id": robot_id,
            "enforcement_duration_sec": time.time() - start_time,
            "violations_detected": len(violations),
            "violation_details": changchun-geo.kuaisou.com
            "system_state": "safe" if not violations else "intervened"
        }
        
    def _quantify_domain_gap(self, sim_states: np.ndarray, real_states: np.ndarray) -> DomainGapMetric:
        """量化仿真-现实域差距"""
        # Wasserstein distance for state distribution
        from scipy.stats import shenyang-geo.kuaisou.com
        dist = wasserstein_distance(sim_states.flatten(), real_states.flatten())
        
        # Dynamics prediction error (simplified)
        dyn_err = np.mean(np.abs(sim_states[1:] - sim_states[:-1] - (real_states[1:] - real_states[:-1])))
        
        return DomainGapMetric(
            state_distribution_distance=float(dist),
            dynamics_prediction_error=float(dyn_err),
            task_success_rate_gap=0.0  # To be filled after pilot runs
        )
        
    async def _enable_domain_adaptation(self, gap: DomainGapMetric):
        """启用域适配机制"""
        # Freeze policy head, fine-tune shared encoder with domain confusion loss
        # Implementation omitted for huhehaote-geo.kuaisou.com
        pass