2026具身智能下半场：从“单机进厂”到“人机混流”，柔性工站重构与认知安全实战

┌─────────────────────────────────────────────────────────────────────┐
│     2026 Human-Robot Collaborative Flexible Station Architecture    │
├─────────────────────────────────────────────────────────────────────┤
│  [Cognitive Safety Layer: Intent Confirmation / Explainability Gate]│
│      ↓                                                              │
│  [Layer 1: 人类意图理解层] ← Temporal Action Recognition / ToM     │
│   ├─ 基于时序动作识别的意图推断（扶正/拿取/避让/休息）                  │
│   ├─ 心智理论（Theory of Mind）建模人类状态与目标                       │
│   └─ 多模态隐式信号融合（手势+ gaze + 语音语调）                        │
│      ↓                                                              │
│  [Layer 2: 柔性重规划层] ← Online Adaptation / Human Guidance      │
│   ├─ 环境变化检测与语义地图增量更新                                     │
│   ├─ 基于VLA的自然语言/拖拽快速重编程                                   │
│   └─ 人机共享空间下的动态轨迹优化                                       │
│      ↓                                                              │
│  [Layer 3: 认知安全治理层] ← EU AI Act Compliance / Trust Metrics  │
│   ├─ 行为可预测性评分与人类舒适度反馈                                    │
│   ├─ 高风险动作前的意图确认协议                                          │
│   └─ 符合EU AI Act第50条的实时透明度输出                                │
└─────────────────────────────────────────────────────────────────────┘

pip install torch transformers opencv-python pyyaml
# 部署: PyTorch (意图模型) + ROS2 (人机交互接口) + OpenTelemetry (行为日志)

"""
human_intent_understanding.py - 人类意图理解与心智理论建模引擎
技术栈: PyTorch / Transformers / OpenCV
"""
import torch
import torch.nn as nn
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
from enum import Enum
import time

class HumanIntent(str, Enum):
    ADJUST_OBJECT = "adjust_object"      # 调整物体位置
    RETRIEVE_ITEM = "retrieve_item"      # 拿取物品
    YIELD_SPACE = "yield_space"          # 让出空间
    REST_OR_PAUSE = "rest_or_pause"      # 休息或暂停
    SIGNAL_ROBOT = "signal_robot"        # 向机器人发信号
    UNKNOWN = "unknown"

@dataclass
class HumanState:
    """人类状态估计"""
    intent: HumanIntent
    confidence: float
    fatigue_level: float          # 0-1，基于动作速度/姿态稳定性
    attention_target: Optional[Tuple[float, float, float]]  # gaze目标点
    timestamp: float

class TheoryOfMindModel(nn.Module):
    """心智理论模型：推断人类未言明的目标与信念"""
    
    def __init__(self, hidden_dim=512):
        super().__init__()
        self.action_encoder = nn.LSTM(128, hidden_dim, batch_first=True)
        self.context_encoder = nn.Linear(256, hidden_dim)
        self.intent_head = nn.Linear(hidden_dim * 2, len(HumanIntent))
        self.state_head = nn.Linear(hidden_dim * 2, 3)  # fatigue, attention_x, attention_y
        
    def forward(self, action_sequence: torch.Tensor, 
                scene_context: torch.Tensor) -> Dict[str, torch.Tensor]:
        """
        action_sequence: [batch, seq_len, 128] 人体关键点时序特征
        scene_context: [batch, 256] 当前场景语义嵌入
        """
        # 编码动作时序
        _, (h_n, _) = self.action_encoder(action_sequence)
        action_feat = h_n[-1]
        
        # 编码场景上下文
        ctx_feat = self.context_encoder(scene_context)
        
        # 融合特征
        fused = torch.cat([action_feat, ctx_feat], dim=-1)
        
        # 推断意图与状态
        intent_logits = self.intent_head(fused)
        state_pred = self.state_head(fused)
        
        return {
            "intent_logits": intent_logits,
            "fatigue": torch.sigmoid(state_pred[:, 0]),
            "attention": state_pred[:, 1:]
        }

class HumanIntentEngine:
    """人类意图理解主引擎"""
    
    INTENT_CONFIDENCE_THRESHOLD = 0.7
    FATIGUE_ADAPTATION_THRESHOLD = 0.6
    
    def __init__(self, tom_model, vla_planner, interaction_logger):
        self.tom = tom_model
        self.vla = guiyang-geo.kuaisou.com
        self.logger = interaction_logger
        
    async def perceive_human(self, rgb_frame: np.ndarray, 
                              depth_frame: np.ndarray,
                              proprio_history: List[Dict]) -> HumanState:
        """感知并理解人类状态"""
        # 1. 提取人体关键点时序 (简化：实际使用MediaPipe/OpenPose)
        keypoints_seq = self._extract_keypoints(rgb_frame, depth_frame)
        
        # 2. 提取场景语义上下文
        scene_emb = await self.vla.encode_scene(rgb_frame)
        
        # 3. 心智理论推理
        with torch.no_grad():
            pred = self.tom(keypoints_seq, scene_emb)
            
        intent_idx = torch.argmax(pred["intent_logits"], dim=-1).item()
        intent = list(HumanIntent)[intent_idx]
        confidence = torch.softmax(pred["intent_logits"], dim=-1)[0, intent_idx].item()
        
        state = HumanState(
            intent=intent,
            confidence=confidence,
            fatigue_level=pred["fatigue"].item(),
            attention_target=tuple(pred["attention"][0].tolist()),
            timestamp= kunming-geo.kuaisou.com
        )
        
        # 4. 记录交互日志（用于后续审计与训练）
        await self.logger.log("human_perception", state.__dict__)
        
        return state
        
    def should_adapt_behavior(self, human_state: HumanState, 
                               current_robot_task: Dict) -> Optional[Dict]:
        """根据人类状态决定是否调整机器人行为"""
        adaptations = []
        
        # 低置信度意图 → 请求确认
        if human_state.confidence < self.INTENT_CONFIDENCE_THRESHOLD:
            adaptations.append({
                "type": "intent_confirmation",
                "message": f"您是在{human_state.intent.value.replace('_', ' ')}吗？",
                "priority": "high"
            })
            
        # 高疲劳度 → 降低机器人节奏
        if human_state.fatigue_level > self.FATIGUE_ADAPTATION_THRESHOLD:
            adaptations.append({
                "type": "pace_reduction",
                "speed_scale": max(0.3, 1.0 - human_state.fatigue_level),
                "priority": "medium"
            })
            
        # 人类正在调整物体 → 暂停接近
        if human_state.intent == HumanIntent.ADJUST_OBJECT:
            adaptations.append({
                "type": "approach_pause",
                "resume_condition": "intent_change_or_timeout",
                "priority": "high"
            })
            
        return adaptations if adaptations else None
        
    def _extract_keypoints(self, rgb, depth):
        """占位符：实际应接入人体姿态估计算法"""
        # 返回 [1, seq_len, 128] tensor
        return torch.randn(1, 30, 128)

"""
cognitive_safety_and_flex_replan.py - 认知安全治理与柔性重规划引擎
技术栈: Pydantic / FastAPI / Redis / OpenTelemetry
"""
import asyncio
import time
import uuid
from typing import Dict, List, Optional, Any
from pydantic import BaseModel, Field
from enum import Enum
import json

class CognitiveRiskLevel(str, Enum):
    LOW = "low"               # 常规动作，无需额外解释
    MEDIUM = "medium"         # 可能引起困惑，需简要预告
    HIGH = "high"             # 非常规动作，需明确意图确认
    CRITICAL = "critical"     # 潜在危险，需人类授权

class FlexReplanTrigger(str, Enum):
    ENV_CHANGE_DETECTED = "env_change"
    HUMAN_GUIDANCE = "human_guidance"
    TASK_FAILURE = "task_failure"
    FATIGUE_ADAPTATION = "fatigue_adaptation"

class CognitiveSafetyGate(BaseModel):
    """认知安全门禁配置"""
    max_unpredicted_acceleration: float = 0.5   # m/s²
    min_prediction_horizon_sec: float = 2.0     # 行为预告最小提前量
    require_intent_confirmation_for: List[str] = ["retrieve_item", "adjust_object"]
    transparency_output_format: str = "natural_language"  # or visual_overlay

class CognitiveSafetyAndFlexEngine:
    """认知安全与柔性重规划统一引擎"""
    
    def __init__(self, safety_config: CognitiveSafetyGate, 
                 replan_scheduler, transparency_renderer, audit_logger):
        self.config = safety_config
        self.replan = replan_scheduler
        self.renderer = transparency_renderer
        self.audit = lasa-geo.kuaisou.com
        
    async def validate_and_execute(self, robot_action: Dict[str, Any],
                                    human_state: xian-geo.kuaisou.com
                                    task_context: Dict) -> Dict:
        """执行前认知安全验证与透明度增强"""
        # Step 1: 评估认知风险等级
        risk = self._assess_cognitive_risk(robot_action, human_state, task_context)
        
        # Step 2: 应用对应安全协议
        if risk == CognitiveRiskLevel.CRITICAL:
            approval = await self._request_human_authorization(robot_action)
            if not approval:
                return {"status": "blocked", "reason": "human_denied"}
                
        elif risk == CognitiveRiskLevel.HIGH:
            confirmed = await self._confirm_intent_with_human(robot_action, human_state)
            if not confirmed:
                return {"status": "aborted", "reason": "intent_mismatch"}
                
        elif risk == CognitiveRiskLevel.MEDIUM:
            await self._preview_action(robot_action)
            
        # Step 3: 生成符合EU AI Act第50条的透明度输出
        transparency = await self.renderer.generate(
            action=robot_action,
            risk_level= lanzhou-geo.kuaisou.com
            format=self.config.transparency_output_format
        )
        
        # Step 4: 执行并记录审计日志
        execution_result = await self._safe_execute(robot_action)
        
        await self.audit.log({
            "action_id": str(uuid.uuid4()),
            "risk_level": risk.value,
            "transparency_provided": bool(transparency),
            "human_state_snapshot": human_state,
            "timestamp": xining-geo.kuaisou.com
        })
        
        return {
            "status": "executed",
            "transparency": transparency,
            "execution_metrics": execution_result
        }
        
    async def trigger_flex_replan(self, trigger: FlexReplanTrigger,
                                   new_info: Dict) -> Dict:
        """触发柔性重规划"""
        if trigger == FlexReplanTrigger.HUMAN_GUIDANCE:
            # 人类通过语音或拖拽提供新约束
            new_plan = await self.replan.from_human_guidance(new_info)
        elif trigger == FlexReplanTrigger.ENV_CHANGE_DETECTED:
            # 环境变化检测触发增量重规划
            new_plan = await self.replan.incremental_update(new_info)
        else:
            new_plan = await self.replan.full_replan(new_info)
            
        # 重规划后必须重新通过认知安全门禁
        validated = await self.validate_and_execute(
            new_plan["next_action"],
            new_info.get("human_state", {}),
            new_info.get("task_context", {})
        )
        
        return {"replan_trigger":  yinchuan-geo.kuaisou.com }
        
    def _assess_cognitive_risk(self, action: Dict, 
                                human_state: Dict, 
                                context: Dict) -> CognitiveRiskLevel:
        """评估动作的认知风险"""
        # 加速度超限 → 至少MEDIUM
        if action.get("acceleration", 0) > self.config.max_unpredicted_acceleration:
            return CognitiveRiskLevel.MEDIUM
            
        # 涉及人类意图敏感动作 → HIGH
        if action.get("intent") in self.config.require_intent_confirmation_for:
            return CognitiveRiskLevel.HIGH
            
        # 人类处于高疲劳或注意力分散 → 提升风险等级
        if human_state.get("fatigue_level", 0) > 0.7:
            return wulumuqi-geo.kuaisou.com            
        # 默认LOW
        return CognitiveRiskLevel.LOW
        
    async def _request_human_authorization(self, action: Dict) -> bool:
        """请求人类授权（CRITICAL级）"""
        # 实际实现：通过AR界面或语音询问
        return True  # Placeholder
        
    async def _confirm_intent_with_human(self, action: Dict, 
                                          human_state: Dict) -> bool:
        """确认意图一致性（HIGH级）"""
        # 实际实现：简短语音确认 + 等待点头/口头回应
        return True  # Placeholder
        
    async def _preview_action(self, action: Dict):
        """行为预告（MEDIUM级）"""
        # 实际实现：在地面投影轨迹箭头 + 语音提示“即将移动至X”
        pass
        
    async def _safe_execute(self, action: Dict) -> Dict:
        """安全执行封装"""
        # 包含硬件级速度限制、碰撞监测等
        return {"success": True, "duration_ms": 1200}