refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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+---
+id: wiki-2026-0508-embodied-ai
+title: Embodied AI
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [embodied AI, robot AI, VLA, vision-language-action, sim2real, robot foundation model]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.96
+verification_status: applied
+tags: [ai, robotics, embodied-ai, vla, foundation-model, sim2real, manipulation]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python
+  framework: ROS 2 / PyTorch / Isaac / MuJoCo
+---
+
+# Embodied AI
+
+## 매 한 줄
+> **"매 physical body 의 의 perceive + act + learn"**. 매 disembodied LLM 의 X — 매 manipulator + locomotion + navigation. 매 modern: 매 RT-2, OpenVLA, π0 — 매 VLM + action. 매 sim2real + diffusion policy.
+
+## 매 핵심
+
+### 매 task
+- **Navigation**: 매 ObjectNav, PointNav.
+- **Manipulation**: 매 pick-place, insertion.
+- **Locomotion**: 매 quadruped, humanoid.
+- **Mobile manipulation**: 매 fetch.
+- **Long-horizon**: 매 cook, clean.
+
+### 매 modern method
+- **Diffusion Policy** (Chi 2023): 매 visual → action 의 diffusion.
+- **VLA** (RT-2, OpenVLA): 매 VLM + action token.
+- **π0** (Physical Intelligence): 매 generalist robot foundation.
+- **ACT** (Aloha): 매 chunked transformer.
+
+### 매 sim2real
+- **Domain randomization**: 매 light, texture, dynamics.
+- **Real2sim2real**: 매 real data + sim refine.
+- **Co-training**: 매 sim + real mix.
+
+### 매 platform
+- **NVIDIA Isaac Sim / Lab**.
+- **MuJoCo / DeepMind Control**.
+- **PyBullet**.
+- **Habitat** (navigation).
+- **RoboCasa** (kitchen).
+
+### 매 응용
+1. **Industrial**: 매 assembly.
+2. **Logistics**: 매 pick-pack.
+3. **Service**: 매 cleaning.
+4. **Surgery**: 매 da Vinci.
+5. **Domestic**: 매 humanoid (1X, Figure, Optimus).
+
+## 💻 패턴
+
+### Diffusion Policy (Chi 2023)
+```python
+import torch
+from torch import nn
+
+class DiffusionPolicy(nn.Module):
+    def __init__(self, obs_dim, action_dim, horizon=8, n_steps=100):
+        super().__init__()
+        self.horizon = horizon
+        self.n_steps = n_steps
+        self.cond_encoder = nn.Linear(obs_dim, 256)
+        self.noise_pred = nn.Sequential(
+            nn.Linear(action_dim * horizon + 256 + 1, 512),
+            nn.ReLU(),
+            nn.Linear(512, action_dim * horizon),
+        )
+    
+    def predict(self, obs):
+        cond = self.cond_encoder(obs)
+        x = torch.randn(self.horizon * 2)
+        for t in reversed(range(self.n_steps)):
+            t_emb = torch.tensor([t / self.n_steps])
+            noise = self.noise_pred(torch.cat([x, cond, t_emb]))
+            x = x - 0.01 * noise
+        return x.reshape(self.horizon, -1)
+```
+
+### VLA (RT-2 / OpenVLA style)
+```python
+class VLA(nn.Module):
+    def __init__(self, vlm, action_dim=7, n_bins=256):
+        super().__init__()
+        self.vlm = vlm  # 매 PaLI-X / Llama-VL
+        self.action_proj = nn.Linear(vlm.hidden_dim, n_bins * action_dim)
+        self.n_bins = n_bins
+    
+    def forward(self, image, instruction):
+        feat = self.vlm(image, instruction).last_hidden_state[:, -1]
+        logits = self.action_proj(feat).reshape(-1, 7, self.n_bins)
+        action_bins = logits.argmax(-1)
+        return self.bin_to_action(action_bins)
+```
+
+### Behavior cloning (basic IL)
+```python
+def behavior_cloning(demos, model):
+    """매 (obs, action) 의 supervised learning."""
+    optim = torch.optim.AdamW(model.parameters(), lr=1e-4)
+    for epoch in range(100):
+        for obs, action in demos:
+            pred = model(obs)
+            loss = F.mse_loss(pred, action)
+            optim.zero_grad()
+            loss.backward()
+            optim.step()
+    return model
+```
+
+### Sim2Real (domain randomization)
+```python
+def randomize_env(env):
+    env.gravity = np.random.uniform(9.5, 10.1)
+    env.friction = np.random.uniform(0.5, 1.5)
+    env.light_intensity = np.random.uniform(0.5, 1.5)
+    env.texture = random.choice(textures)
+    env.payload_mass = np.random.uniform(0, 0.5)
+    return env
+```
+
+### Habitat navigation
+```python
+import habitat
+config = habitat.get_config('benchmark/nav/objectnav_hm3d_v1.yaml')
+env = habitat.Env(config)
+obs = env.reset()
+while not env.episode_over:
+    action = policy(obs)
+    obs = env.step(action)
+```
+
+### MuJoCo manipulation
+```python
+import mujoco
+model = mujoco.MjModel.from_xml_path('panda.xml')
+data = mujoco.MjData(model)
+mujoco.mj_step(model, data)
+ee_pos = data.site('end_effector').xpos
+```
+
+### Reward shaping (manipulation)
+```python
+def grasp_reward(state):
+    distance = np.linalg.norm(state.gripper_pos - state.object_pos)
+    in_grasp = state.gripper_holding_object
+    lifted = state.object_pos[2] - state.object_init_z > 0.1
+    
+    return -distance + (5 if in_grasp else 0) + (10 if lifted else 0)
+```
+
+### Curriculum learning
+```python
+def curriculum(success_rate, level):
+    if success_rate > 0.8: return level + 1
+    if success_rate < 0.3: return max(0, level - 1)
+    return level
+
+# 매 level 0: easy (objects close, no obstacles)
+# 매 level 1: clutter
+# 매 level 2: distractors + dynamic
+```
+
+### Real2Sim2Real (RoboCasa-style)
+```python
+def real2sim(real_traj):
+    # 매 real state 의 sim recreate
+    sim_init = match_initial_state(real_traj[0])
+    sim_traj = simulate(sim_init, real_traj.actions)
+    return sim_traj
+
+def sim_train_real_eval(sim_data, real_data):
+    model = train_on(sim_data + real_data)
+    return evaluate_real(model, real_data.eval)
+```
+
+### Action chunking (ACT)
+```python
+class ACT(nn.Module):
+    """매 Aloha bimanual."""
+    def __init__(self, chunk=100):
+        super().__init__()
+        self.chunk = chunk
+        self.encoder = TransformerEncoder()
+        self.decoder = TransformerDecoder()
+    
+    def forward(self, obs):
+        feat = self.encoder(obs)
+        actions = self.decoder(feat)  # 매 [chunk, action_dim]
+        return actions
+    
+    def execute(self, obs):
+        chunk = self.forward(obs)
+        # 매 temporal ensembling
+        return chunk[0]
+```
+
+### Safety filter
+```python
+def safe_action(proposed, state):
+    if proposed.force > MAX_FORCE: proposed.force = MAX_FORCE
+    if collision_imminent(proposed, state): return STOP_ACTION
+    if outside_workspace(proposed, state): return CLAMP_TO_WORKSPACE(proposed)
+    return proposed
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Visual policy | Diffusion Policy |
+| Language-conditioned | VLA (OpenVLA / π0) |
+| Multi-task | Foundation model |
+| Long-horizon | Hierarchical + chunking |
+| Sim-only | Domain randomization |
+| Few demos | BC + augmentation |
+| Generalist | π0 / RT-X |
+
+**기본값**: 매 modern = 매 VLA finetune (OpenVLA) + 매 diffusion policy + 매 sim2real domain randomization + 매 safety filter.
+
+## 🔗 Graph
+- 부모: [[AI]] · [[Robotics]] · [[Embodied Cognition]]
+- 변형: [[VLA]]
+- Adjacent: [[Foundation-Model]] · [[CLIP]] · [[π0]]
+
+## 🤖 LLM 활용
+**언제**: 매 robot. 매 manipulation. 매 navigation. 매 multimodal physical.
+**언제 X**: 매 pure simulation game. 매 disembodied chat.
+
+## ❌ 안티패턴
+- **No safety filter**: 매 hardware 의 damage.
+- **Sim-only no DR**: 매 sim2real gap.
+- **BC overfit demos**: 매 OOD fail.
+- **Tiny VLM 의 generalist 의 expect**: 매 capacity 의 부족.
+- **No chunking**: 매 jitter / instability.
+
+## 🧪 검증 / 중복
+- Verified (RT-2, OpenVLA, Diffusion Policy 2023, π0 2024).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-04-26 | EMBODIED-AI auto |
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — diffusion / VLA / BC / sim2real / curriculum / ACT code |