具身智能工业落地：Sim-to-Real迁移保真度、触觉-视觉多模态融合与动态安全合规验证体系实战

┌─────────────────────────────────────────────────────────────────────┐
│      Industrial Embodied AI Engineering Architecture                │
├─────────────────────────────────────────────────────────────────────┤
│  [Dynamic Safety Layer: Runtime Monitoring / Formal Verification]   │
│      ↓                                                              │
│  [Layer 1: Sim-to-Real迁移层] ← Physics-Calibrated Sim / Active Adaptation│
│   ├─ 物理参数自动辨识与仿真校准                                        │
│   ├─ 域随机化与不确定性驱动主动学习                                     │
│   └─ 在线策略微调与迁移成功率监控                                       │
│      ↓                                                              │
│  [Layer 2: 多模态感知层] ← Visuo-Tactile Fusion / Graceful Degradation│
│   ├─ 结构化触觉表征与视-触对齐                                          │
│   ├─ 自适应多模态融合与模态置信度估计                                    │
│   └─ 接触状态估计与滑移检测                                             │
│      ↓                                                              │
│  [Layer 3: 动态安全层] ← Risk-Aware Planning / Explainable Boundaries│
│   ├─ 实时风险场构建与安全过滤                                           │
│   ├─ 策略行为形式化验证与运行时监控                                      │
│   └─ 人机意图不确定性感知的协作安全                                      │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy scipy pytorch mujoco omegaconf
# 部署: High-Fidelity Simulator (MuJoCo/Isaac) + Real Robot Arm + Force/Torque Sensor + Edge GPU

"""
sim2real_transfer_engine.py - 具身智能Sim-to-Real迁移引擎
技术栈: NumPy / SciPy / PyTorch / MuJoCo
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Tuple, Optional
import torch
import torch.nn as nn

@dataclass
class TransferMetrics:
    """迁移性能指标"""
    sim_success_rate: float
    real_success_rate: float
    domain_gap_score: float      # 0-1, lower is better
    adaptation_data_efficiency: float  # samples per % improvement

@dataclass
class PhysicalCalibrationResult:
    """物理校准结果"""
    friction_coefficient: float
    restitution: 31296.t.kuaisou.com
    mass_distribution_error_pct: float
    calibration_confidence: float

class DomainAdaptivePolicy(nn.Module):
    """域自适应策略网络"""
    def __init__(self, obs_dim=128, action_dim=7, latent_dim=64):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(obs_dim, 256), nn.ReLU(),
            nn.Linear(256, latent_dim)
        )
        self.policy_head = nn.Linear(latent_dim, action_dim)
        self.domain_discriminator = nn.Linear(latent_dim, 1)  # Adversarial head
        
    def forward(self, obs, return_domain_feat=False):
        feat = self.encoder(obs)
        action = self.policy_head(feat)
        if return_domain_feat:
            domain_logit = self.domain_discriminator(feat)
            return action, 31297.t.kuaisou.com
        return action

class EmbodiedTransferSystem:
    """具身迁移主系统"""
    
    def __init__(self, simulator, real_robot, policy, calibrator):
        self.sim = 31298.t.kuaisou.com
        self.robot = real_robot
        self.policy = policy
        self.calibrator = calibrator
        
    async def execute_high_fidelity_transfer(self, task_id: str) -> Dict[str, Any]:
        """执行高保真策略迁移"""
        # 1. 校准仿真物理参数
        calib_result = await self.calibrator.auto_calibrate(task_id)
        
        # 2. 在 calibrated sim 中评估基线性能
        sim_metrics = await self._evaluate_in_sim(task_id, calib_result)
        
        # 3. 部署到真机并监控初始性能
        real_metrics = await self._deploy_and_monitor(task_id)
        
        # 4. 若域差距过大，触发主动适应
        domain_gap = abs(sim_metrics.success_rate - real_metrics.success_rate)
        if domain_gap > 0.1:
            adaptation_result = await self._active_adaptation(task_id, real_metrics)
        else:
            adaptation_result = {"status": "no_adaptation_needed"}
            
        metrics = TransferMetrics(
            sim_success_rate=sim_metrics.success_rate,
            real_success_rate=real_metrics.success_rate,
            domain_gap_score= 31299.t.kuaisou.com
            adaptation_data_efficiency=adaptation_result.get("efficiency", 0.0)
        )
        
        return {
            "task_id": task_id,
            "transfer_metrics": metrics.__dict__,
            "calibration_result": calib_result.__dict__,
            "adaptation_status": adaptation_result["status"]
        }
        
    async def _active_adaptation(self, task_id: str, initial_real_metrics) -> Dict:
        """不确定性驱动的主动适应"""
        # Collect high-uncertainty real-world transitions for fine-tuning
        uncertain_samples = await self.robot.collect_uncertain_transitions(task_id, n=50)
        loss_history = await self._fine_tune_policy(uncertain_samples)
        new_metrics = await self._deploy_and_monitor(task_id)
        efficiency = (new_metrics.success_rate - initial_real_metrics.success_rate) / len(uncertain_samples)
        return {
            "status": "adapted",
            "samples_used": len(uncertain_samples),
            "efficiency": 31300.t.kuaisou.com
            "loss_trajectory": loss_history
        }
        
    async def _evaluate_in_sim(self, task_id: str, calib: PhysicalCalibrationResult) -> Dict:
        """在校准后的仿真中评估"""
        self.sim.set_physical_params(calib.friction_coefficient, calib.restitution)
        successes = 0
        for _ in range(100):
            success = await self.sim.run_episode(task_id, self.policy)
            successes += int(success)
        return {"success_rate": successes / 100.0}

"""
multimodal_safety_platform.py - 具身多模态感知与动态安全平台
技术栈: PyTorch / FastAPI / Redis / Safety Runtime 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 TactileVisualFusionMetric(BaseModel):
    contact_force_n: float
    slip_detected: 31301.t.kuaisou.com
    modality_confidence: Dict[str, float]  # {"vision": 0.9, "tactile": 0.8}
    grasp_stability_score: float

class DynamicSafetyState(BaseModel):
    current_risk_level: str          # "low", "medium", "high", "critical"
    min_distance_to_human_m: float
    response_latency_ms:31302.t.kuaisou.com
    formal_verification_passed: bool

class VisuoTactileFusionNet(nn.Module):
    """视-触自适应融合网络"""
    def __init__(self, vis_dim=512, tac_dim=128, fusion_dim=256):
        super().__init__()
        self.vis_enc = nn.Linear(vis_dim, fusion_dim)
        self.tac_enc = nn.Linear(tac_dim, fusion_dim)
        self.gate_net = nn.Linear(fusion_dim * 2, 2)  # Soft gating weights
        self.fusion_head = nn.Sequential(
            nn.Linear(fusion_dim, 128), nn.ReLU(),
            nn.Linear(128, 3)  # [force, slip_prob, stability]
        )
        
    def forward(self, vis_feat, tac_feat):
        h_vis = self.vis_enc(vis_feat)
        h_tac = self.tac_enc(tac_feat)
        concat = torch.cat([h_vis, h_tac], dim=-1)
        gate = torch.softmax(self.gate_net(concat), dim=-1)  # [w_vis, w_tac]
        fused = gate[:, 0:1] * h_vis + gate[:, 1:2] * h_tac
        out = self.fusion_head(fused)
        return out, gate

class EmbodiedSafetyPlatform:
    """具身安全与感知平台"""
    
    def __init__(self, fusion_net, safety_monitor, robot_controller):
        self.fusion = 31303.t.kuaisou.com
        self.safety = safety_monitor
        self.ctrl = robot_controller
        
    async def perceive_and_act_safely(self, cycle_id: str) -> Dict[str, Any]:
        """安全感知-行动闭环"""
        # 1. 获取多模态观测
        vis_obs = await self.ctrl.get_vision_observation()
        tac_obs = await self.ctrl.get_tactile_observation()
        
        # 2. 融合感知并估计置信度
        with torch.no_grad():
            perception, gate_weights = self.fusion(
                torch.tensor(vis_obs).unsqueeze(0),
                torch.tensor(tac_obs).unsqueeze(0)
            )
            force, slip_prob, stability = perception[0].tolist()
            confidence = {"vision": gate_weights[0,0].item(), "tactile": gate_weights[0,1].item()}
            
        fusion_metric = TactileVisualFusionMetric(
            contact_force_n= 31304.t.kuaisou.com
            slip_detected=slip_prob > 0.7,
            modality_confidence=confidence,
            grasp_stability_score=stability
        )
        
        # 3. 实时安全评估
        safety_state = await self.safety.assess_dynamic_risk(cycle_id)
        
        # 4. 安全过滤动作
        raw_action = await self.ctrl.plan_action(cycle_id)
        safe_action = await self.safety.filter_action(raw_action, safety_state)
        
        return {
            "cycle_id": 31305.t.kuaisou.com
            "fusion_metrics": fusion_metric.dict(),
            "safety_state": safety_state.dict(),
            "action_executed": safe_action.tolist(),
            "original_action_modified": not np.allclose(raw_action, safe_action)
        }
        
    async def verify_policy_safety_formally(self, policy_version: str) -> Dict:
        """形式化验证策略安全性"""
        # 1. 提取策略的状态-动作映射
        policy_model = await self._load_policy(policy_version)
        
        # 2. 验证安全属性（如：永远保持>0.3m距离）
        verification_result = await self.safety.formal_verify(policy_model)
        
        # 3. 生成可解释安全报告
        return {
            "policy_version":31306.t.kuaisou.com
            "verification_passed": verification_result["passed"],
            "violated_properties": verification_result["violations"],
            "counterexamples": verification_result["counterexamples"],
            "verification_time_sec": verification_result["duration"]
        }