2nd/10_Wiki/Topics/AI_and_ML/Embodied Cognition.md

---
id: wiki-2026-0508-embodied-cognition
title: Embodied Cognition
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [embodied mind, 4E cognition, embodied embedded enacted extended, situated cognition]
duplicate_of: none
source_trust_level: A
confidence_score: 0.93
verification_status: applied
tags: [philosophy, cognitive-science, embodiment, 4e-cognition, phenomenology, ai]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Cognitive Science
  applicable_to: [AI, Robotics, HCI, Therapy]
---

# Embodied Cognition

## 매 한 줄
> **"매 mind 의 brain 의 X — 매 body + environment 의 distributed"**. 매 cognition = embodied + embedded + enacted + extended (4E). 매 Lakoff & Johnson, Varela, Clark. 매 modern AI: 매 LLM 의 disembodied 의 critique → 매 embodied AI / robotics emphasis.

## 매 핵심

### 매 4E
- **Embodied**: 매 body 의 structure 의 cognition 의 shape.
- **Embedded**: 매 environment 의 part.
- **Enacted**: 매 action 의 perception.
- **Extended**: 매 tool 의 mind 의 part (Clark).

### 매 historical
- **Merleau-Ponty**: 매 phenomenology of body.
- **Gibson**: 매 affordance.
- **Lakoff & Johnson**: 매 conceptual metaphor (UP=GOOD).
- **Varela / Maturana**: 매 enaction, autopoiesis.
- **Clark & Chalmers**: 매 extended mind (1998).

### 매 evidence
- **Body posture**: 매 confidence ↑.
- **Gesture**: 매 speech production.
- **Mirror neuron**: 매 action understanding.
- **Numerical line**: 매 left-right space.
- **Metaphor**: 매 anger = heat.

### 매 AI implication
- **Symbol grounding**: Harnad's problem.
- **GOFAI critique**: 매 disembodied symbol.
- **Embodied AI**: 매 robot, sim2real.
- **LLM critique**: 매 token 의 only — 매 ground X.
- **Modern**: 매 vision-language-action (RT-2, OpenVLA).

### 매 응용
1. **Robotics**: 매 body 의 use 의 simplify control.
2. **HCI**: 매 gesture / posture interface.
3. **Therapy**: 매 somatic experiencing.
4. **Education**: 매 enactment.
5. **AI**: 매 embodied agent.

## 💻 패턴

### Affordance detection (vision)
```python
import torch
from torchvision import models

class AffordanceNet(torch.nn.Module):
    """매 segment 의 graspable / sittable / pushable."""
    def __init__(self, n_affordances):
        super().__init__()
        self.backbone = models.resnet50(pretrained=True)
        self.head = torch.nn.Conv2d(2048, n_affordances, 1)
    
    def forward(self, x):
        feat = self.backbone(x)
        return self.head(feat).softmax(1)
```

### Embodied simulation (PyBullet)
```python
import pybullet as p
p.connect(p.GUI)
robot = p.loadURDF('humanoid.urdf')

def policy(obs):
    # 매 body 의 dynamics 의 use 의 task 의 simplify
    com = p.getBasePositionAndOrientation(robot)[0]
    return compute_balance_action(com, obs)
```

### Conceptual metaphor (NLP)
```python
METAPHORS = {
    'UP_IS_GOOD': ['rise', 'high spirits', 'up', 'top'],
    'DOWN_IS_BAD': ['fall', 'low', 'down', 'bottom'],
    'WARM_IS_AFFECTIONATE': ['warm welcome', 'cold response'],
    'JOURNEY_IS_LIFE': ['path', 'crossroads', 'road'],
}

def detect_metaphors(text):
    found = []
    for meta, markers in METAPHORS.items():
        if any(m in text.lower() for m in markers):
            found.append(meta)
    return found
```

### Gesture recognition (MediaPipe)
```python
import mediapipe as mp
hands = mp.solutions.hands.Hands()

def classify_gesture(landmarks):
    # 매 thumb up: thumb tip y < other tips
    thumb_tip = landmarks[4]
    other_tips = [landmarks[i] for i in [8, 12, 16, 20]]
    if all(thumb_tip.y < t.y for t in other_tips):
        return 'thumbs_up'
    return 'unknown'
```

### Mirror system (action observation)
```python
class MirrorSystem:
    """매 observe 의 reproduce."""
    def __init__(self, action_repertoire):
        self.repertoire = action_repertoire  # 매 my own actions
    
    def imitate(self, observed_trajectory):
        # 매 best-match 의 own action
        best = min(self.repertoire, key=lambda a: dtw_distance(a, observed_trajectory))
        return self.execute(best)
```

### VLA (Vision-Language-Action) like OpenVLA
```python
def vla_policy(image, instruction):
    """매 robot 의 embodied LLM."""
    img_emb = vision_encoder(image)
    text_emb = text_encoder(instruction)
    fused = transformer(img_emb, text_emb)
    action = action_decoder(fused)  # 매 7-dim end-effector
    return action
```

### Body schema (proprioception)
```python
class BodySchema:
    def __init__(self, joint_limits):
        self.joints = joint_limits
        self.tool_attached = None
    
    def attach_tool(self, tool):
        """매 extended embodiment."""
        self.tool_attached = tool
        # 매 reachable space 의 tool length 의 extend
    
    def reachable(self, target):
        max_reach = self.compute_max_reach()
        if self.tool_attached:
            max_reach += self.tool_attached.length
        return np.linalg.norm(target) < max_reach
```

### Posture-mood feedback (HCI)
```python
def posture_suggestion(pose_landmarks):
    """매 power pose 의 confidence ↑ 의 evidence."""
    shoulder_angle = compute_shoulder_openness(pose_landmarks)
    if shoulder_angle < 30:
        return "Try opening your shoulders — research suggests it improves confidence."
    return None
```

### Symbol grounding via interaction
```python
def ground_word(word, sensorimotor_history):
    """매 word 의 sensory-motor 의 contingencies 의 link."""
    contexts = [h for h in sensorimotor_history if word in h.transcript]
    visual_features = average_visual([c.frame for c in contexts])
    motor_features = average_motor([c.action for c in contexts])
    return {'word': word, 'visual': visual_features, 'motor': motor_features}
```

### Enactive perception
```python
def enactive_perception(world, action_capabilities):
    """매 affordance 의 own capability 의 dependent."""
    perceived_affordances = {}
    for obj in world.objects:
        relevant_actions = [a for a in action_capabilities if a.applicable_to(obj)]
        perceived_affordances[obj] = relevant_actions
    return perceived_affordances
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Robot policy | Embodied (use body dynamics) |
| LLM critique | Add multimodal + grounding |
| HCI | Gesture + posture |
| Therapy | Somatic + body schema |
| AI agent | VLA / VLM-action |
| Linguistics | Conceptual metaphor |

**기본값**: 매 multimodal + 매 grounding (vision + motor) + 매 affordance + 매 sim2real for robot.

## 🔗 Graph
- 변형: [[Embodied-AI]]
- 응용: [[Robotics]] · [[VLA]]
- Adjacent: [[Affordance]]

## 🤖 LLM 활용
**언제**: 매 robotics. 매 multimodal AI. 매 grounded language.
**언제 X**: 매 pure symbolic reasoning.

## ❌ 안티패턴
- **Pure GOFAI**: 매 ground X.
- **Disembodied LLM 의 only**: 매 physical sense X.
- **Body 의 ignore**: 매 sim2real gap.
- **Metaphor 의 literal**: 매 abstraction lose.

## 🧪 검증 / 중복
- Verified (Lakoff & Johnson, Varela, Clark, Harnad).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-04-20 | Auto-reinforced |
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — 4E + 매 affordance / VLA / mirror / body schema code |