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

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

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

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)

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)

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)

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)

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

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)

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)

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

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

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