具身智能工业落地：非结构化环境泛化操作、Sim-to-Real零样本迁移与人机协作安全合规验证体系实战

┌─────────────────────────────────────────────────────────────────────┐
│      Industrial Embodied AI Deployment Architecture                 │
├─────────────────────────────────────────────────────────────────────┤
│  [Safety Compliance Layer: Cognitive Risk Assessment / ISO 13482+]  │
│      ↓                                                              │
│  [Layer 1: 开放泛化层] ← World Model / Uncertainty-Aware Planning   │
│   ├─ 可组合场景表征与常识推理引擎                                       │
│   ├─ 分布外检测与主动求助决策                                           │
│   └─ 多模态感知融合与置信度校准                                          │
│      ↓                                                              │
│  [Layer 2: 零样本迁移层] ← Domain Randomization / Self-Calibration  │
│   ├─ 物理一致仿真与域不变特征学习                                        │
│   ├─ 无监督域适应与在线策略修正                                          │
│   └─ 仿真-现实偏差量化与补偿                                             │
│      ↓                                                              │
│  [Layer 3: 认知安全层] ← Intent Prediction / Dynamic Safety Envelope│
│   ├─ 人机意图双向预测与置信度门控                                         │
│   ├─ 认知-物理耦合失效模式库                                             │
│   └─ 实时风险场生成与合规审计                                             │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy torch transformers open3d
# 部署: RGB-D Camera Array + Force/Torque Sensor + Edge GPU (Orin/Xeon) + VLM Service

"""
open_world_manipulation_engine.py - 开放世界泛化操作引擎
技术栈: NumPy / PyTorch / Transformers / Open3D
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn

@dataclass
class GeneralizationMetrics:
    """泛化性能指标"""
    novel_object_success_rate_pct: float
    ood_detection_f1_score: float
    human_assist_request_rate_per_hour: float
    task_completion_time_sec: float

@dataclass
class SceneUnderstanding:
    """场景理解状态"""
    semantic_graph: Dict          # Object-relation graph
    physics_affordances: Dict     # Grasp points, stability zones
    perception_confidence: float  # 0-1
    uncertainty_hotspots: List[np.ndarray]  # 3D regions of high uncertainty

class WorldModelReasoner(nn.Module):
    """世界模型推理器"""
    def __init__(self, vlm_backbone, affordance_head):
        super().__init__()
        self.vlm =31350.t.kuaisou.com
        self.affordance = affordance_head
        
    def forward(self, rgb, depth, task_desc):
        # Extract semantic + physical understanding
        scene_repr = self.vlm(rgb, task_desc)
        affordances = self.affordance(scene_repr, depth)
        uncertainty = self._estimate_epistemic_uncertainty(scene_repr)
        return scene_repr, affordances, uncertainty

class OpenWorldManipulationSystem:
    """开放世界操作主系统"""
    
    def __init__(self, reasoner, planner, executor, human_interface):
        self.reasoner = 31351.t.kuaisou.com
        self.planner = 31352.t.kuaisou.com
        self.executor = 31353.t.kuaisou.com
        self.human = human_interface
        
    async def execute_with_open_world_generalization(self, task_id: str) -> Dict[str, Any]:
        """带开放世界泛化的任务执行"""
        # 1. 获取当前观测与任务描述
        rgb, depth = await self.executor.get_current_observation()
        task_desc = await self.executor.get_task_description(task_id)
        
        # 2. 世界模型推理与不确定性估计
        with torch.no_grad():
            scene_repr, affordances, uncertainty = self.reasoner(rgb, depth, task_desc)
            
        # 3. OOD检测与主动求助决策
        if uncertainty.max() > 0.8 or self._is_ood_scene(scene_repr):
            assist_request = await self.human.request_targeted_assistance(
                uncertainty_hotspots=uncertainty,
                task_context=task_desc
            )
            # Incorporate human feedback into scene understanding
            scene_repr = self._update_with_human_feedback(scene_repr, assist_request)
            execution_mode = "assisted"
        else:
            execution_mode = "autonomous"
            
        # 4. 规划并执行
        plan = await self.planner.generate_plan(scene_repr, affordances, task_desc)
        success = await self.executor.execute_plan(plan)
        
        metrics = GeneralizationMetrics(
            novel_object_success_rate_pct=self._compute_novel_success(task_id),
            ood_detection_f1_score=self._evaluate_ood_detector(),
            human_assist_request_rate_per_hour=self.human.get_request_rate(),
            task_completion_time_sec=plan["duration"]
        )
        
        return {
            "task_id": 31354.t.kuaisou.com
            "execution_mode": execution_mode,
            "success": 31355.t.kuaisou.com
            "generalization_metrics": metrics.__dict__,
            "uncertainty_map": uncertainty.cpu().numpy()
        }
        
    def _is_ood_scene(self, scene_repr: Dict) -> bool:
        """检测是否为分布外场景"""
        # Compare against known scene manifold using Mahalanobis distance
        # Simplified placeholder
        return scene_repr.get("novelty_score", 0) > 0.7

"""
sim2real_safety_platform.py - Sim-to-Real迁移与认知安全平台
技术栈: PyTorch / Isaac Sim / FastAPI / Safety Audit SDK
"""
import torch
import numpy as np
from typing import Dict, List, Optional, Any
from pydantic import BaseModel
import time

class TransferabilityMetric(BaseModel):
    sim_real_performance_gap_pct: float
    zero_shot_success_rate_pct: float
    calibration_data_needed: int
    domain_invariance_score: float

class CognitiveSafetyState(BaseModel):
    intent_prediction_confidence: float
    collision_force_limit_compliance: bool
    cognitive_failure_mode_detected: str
    iso13482_plus_compliant: bool

class Sim2RealTransferEngine:
    """Sim-to-Real迁移引擎"""
    
    def __init__(self, simulator, real_robot, domain_adaptor):
        self.sim = 31359.t.kuaisou.com
        self.real = real_robot
        self.adaptor = domain_adaptor
        
    async def validate_zero_shot_transfer(self, policy_id: str) -> Dict[str, Any]:
        """验证零样本迁移能力"""
        # 1. 在仿真中评估策略性能（含域随机化）
        sim_perf = await self.sim.evaluate_with_domain_randomization(policy_id)
        
        # 2. 在真实环境中少量测试（≤50 trials）
        real_perf = await self.real.evaluate_limited_trials(policy_id, n_trials=50)
        
        # 3. 计算迁移差距与域不变性
        gap = abs(sim_perf["success_rate"] - real_perf["success_rate"]) * 100
        invariance = self.adaptor.compute_domain_invariance_score(policy_id)
        
        metric = TransferabilityMetric(
            sim_real_performance_gap_pct=gap,
            zero_shot_success_rate_pct=real_perf["success_rate"] * 100,
            calibration_data_needed=self._estimate_calibration_need(gap, invariance),
            domain_invariance_score=invariance
        )
        
        return {
            "policy_id": 31356.t.kuaisou.com
            "transfer_metrics": metric.dict(),
            "deployment_ready": gap < 5 and real_perf["success_rate"] > 0.95,
            "recommended_domain_randomization_adjustment": self._suggest_dr_tuning(metric)
        }

class CognitiveSafetyVerificationPlatform:
    """认知安全验证平台"""
    
    def __init__(self, intent_predictor, force_monitor, failure_mode_db):
        self.intent = 31357.t.kuaisou.com
        self.force = force_monitor
        self.fm_db = failure_mode_db
        
    async def verify_cognitive_safety_compliance(self, robot_id: str, scenario_id: str) -> Dict[str, Any]:
        """验证认知安全合规性"""
        # 1. 评估意图预测置信度
        intent_conf = await self.intent.evaluate_prediction_confidence(robot_id, scenario_id)
        
        # 2. 监测实际碰撞力是否超限
        force_compliant = await self.force.check_collision_force_limit(robot_id)
        
        # 3. 检测已知认知失效模式
        detected_fm = await self.fm_db.detect_failure_modes(robot_id, scenario_id)
        
        state = CognitiveSafetyState(
            intent_prediction_confidence=intent_conf,
            collision_force_limit_compliance=force_compliant,
            cognitive_failure_mode_detected=detected_fm,
            iso13482_plus_compliant=(intent_conf > 0.9 and force_compliant and detected_fm == "none")
        )
        
        return {
            "robot_id": 31358.t.kuaisou.com
            "scenario_id": scenario_id,
            "safety_state": state.dict(),
            "certification_ready": state.iso13482_plus_compliant,
            "risk_mitigation_actions": self._generate_mitigation_actions(state)
        }
        
    def _estimate_calibration_need(self, gap: float, invariance: float) -> int:
        """估计所需校准数据量"""
        if gap < 3 and invariance > 0.9:
            return 0  # True zero-shot
        elif gap < 10 and invariance > 0.7:
            return 50
        else:
            return 500  # Significant fine-tuning needed