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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Antigravity Agent
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---
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id: wiki-2026-0508-robotics
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title: Robotics
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Robotic Systems, Robot Engineering, Robotics Foundations]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.95
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verification_status: applied
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tags: [robotics, perception, planning, control, foundation-models, embodied-AI]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python/C++
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framework: ROS2/Isaac/MuJoCo/PyTorch
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---
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# Robotics
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## 매 한 줄
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> **"매 sense → think → act 의 closed loop 의 physical world 의 agent."**. Robotics 의 perception (vision/lidar/IMU), planning (motion + task), control (PID → MPC → RL) 의 stack 의, 매 2024-2026 inflection 의 vision-language-action (VLA) foundation models — Google RT-2, Physical Intelligence π0, Figure 02 — 의 generalist embodied AI 의 era 의 driving.
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## 매 핵심
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### 매 classical stack
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1. **Perception**: SLAM (ORB-SLAM3, Kimera), object detection, pose estimation.
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2. **State estimation**: Kalman filter, factor graphs (GTSAM).
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3. **Planning**:
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- Task: PDDL, behavior trees, LLM planners.
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- Motion: RRT*, trajectory optimization (TrajOpt, CHOMP).
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4. **Control**: PID, LQR, MPC, impedance control.
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5. **Hardware abstraction**: ROS2, drivers, real-time scheduling.
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### 매 modern (2026) shift
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- **VLA models**: π0, RT-2, OpenVLA — image+text→action tokens.
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- **Diffusion policies**: Chi et al. 2023 — multimodal action distributions.
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- **Sim2Real**: Isaac Lab, MuJoCo MJX — massive parallel simulation + DR.
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- **Humanoids**: Figure 02, Tesla Optimus, Apptronik Apollo, 1X Neo — commercial pilots.
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- **Whole-body MPC**: real-time on humanoid (Boston Dynamics Atlas).
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### 매 응용
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1. Industrial automation (pick-and-place, welding).
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2. Autonomous mobile robots (Amazon Proteus, warehouse AGVs).
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3. Surgical robotics (da Vinci, Intuitive Ion).
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4. Humanoid general purpose (Figure, Optimus, BMW pilot 2025).
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5. Self-driving (Waymo, Tesla FSD).
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## 💻 패턴
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### ROS2 Node Skeleton
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```python
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import rclpy
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from rclpy.node import Node
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from sensor_msgs.msg import Image
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from geometry_msgs.msg import Twist
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class Controller(Node):
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def __init__(self):
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super().__init__("controller")
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self.sub = self.create_subscription(Image, "/cam", self.cb_img, 10)
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self.pub = self.create_publisher(Twist, "/cmd_vel", 10)
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def cb_img(self, msg):
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cmd = Twist()
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cmd.linear.x = 0.5
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self.pub.publish(cmd)
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def main(): rclpy.init(); rclpy.spin(Controller()); rclpy.shutdown()
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```
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### Extended Kalman Filter (state estimation)
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```python
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import numpy as np
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class EKF:
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def __init__(self, x0, P0, Q, R):
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self.x, self.P, self.Q, self.R = x0, P0, Q, R
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def predict(self, f, F):
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self.x = f(self.x)
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self.P = F @ self.P @ F.T + self.Q
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def update(self, z, h, H):
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y = z - h(self.x)
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S = H @ self.P @ H.T + self.R
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K = self.P @ H.T @ np.linalg.inv(S)
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self.x = self.x + K @ y
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self.P = (np.eye(len(self.x)) - K @ H) @ self.P
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```
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### MPC (acados / cvxpy)
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```python
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import cvxpy as cp
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import numpy as np
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def mpc_step(x0, A, B, Q, R, N=10, u_max=1.0):
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nx, nu = A.shape[0], B.shape[1]
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x = cp.Variable((nx, N+1))
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u = cp.Variable((nu, N))
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cost = sum(cp.quad_form(x[:,k], Q) + cp.quad_form(u[:,k], R) for k in range(N))
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cons = [x[:,0] == x0]
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for k in range(N):
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cons += [x[:,k+1] == A @ x[:,k] + B @ u[:,k],
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cp.norm_inf(u[:,k]) <= u_max]
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cp.Problem(cp.Minimize(cost), cons).solve()
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return u.value[:,0]
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```
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### RRT* Motion Planning
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```python
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def rrt_star(start, goal, obstacles, max_iter=2000, step=0.5, radius=2.0):
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tree = {tuple(start): {"parent": None, "cost": 0.0}}
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for _ in range(max_iter):
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rnd = sample_free(obstacles)
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nearest = min(tree, key=lambda n: dist(n, rnd))
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new = steer(nearest, rnd, step)
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if collision_free(nearest, new, obstacles):
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neighbors = [n for n in tree if dist(n, new) < radius]
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best = min(neighbors, key=lambda n: tree[n]["cost"] + dist(n, new))
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tree[tuple(new)] = {"parent": best, "cost": tree[best]["cost"] + dist(best, new)}
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if dist(new, goal) < step: return reconstruct_path(tree, tuple(new))
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return None
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```
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### Diffusion Policy (modern)
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```python
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import torch
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import torch.nn as nn
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class DiffusionPolicy(nn.Module):
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def __init__(self, obs_dim, action_dim, T=100):
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super().__init__()
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self.net = nn.Sequential(nn.Linear(obs_dim+action_dim+1, 256),
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nn.SiLU(),
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nn.Linear(256, action_dim))
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self.T = T
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def forward(self, obs, a_t, t):
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return self.net(torch.cat([obs, a_t, t.float().unsqueeze(-1)/self.T], -1))
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@torch.no_grad()
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def sample(self, obs, scheduler):
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a = torch.randn(obs.shape[0], self.action_dim, device=obs.device)
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for t in reversed(range(self.T)):
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eps = self.forward(obs, a, torch.full((obs.shape[0],), t, device=obs.device))
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a = scheduler.step(eps, t, a)
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return a
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```
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### VLA Inference (π0-style)
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```python
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def vla_act(model, image, instruction: str):
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"""매 VLA: image + text → action chunk."""
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inputs = model.processor(image=image, text=instruction, return_tensors="pt")
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out = model.generate(**inputs, max_new_tokens=64)
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action_chunk = model.detokenize_actions(out) # e.g. (8, 7) — 8 timesteps × 7 DoF
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return action_chunk
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```
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### Sim2Real Domain Randomization
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```python
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def randomize_env(env):
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env.set_friction(np.random.uniform(0.5, 1.5))
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env.set_mass_scale(np.random.uniform(0.8, 1.2))
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env.set_lighting(np.random.uniform(0.3, 1.0))
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env.add_observation_noise(scale=np.random.uniform(0.0, 0.05))
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```
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## 매 결정 기준
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| 상황 | Approach |
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| Industrial repetitive | Classical: vision + IK + PID |
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| Mobile navigation | SLAM + planning (Nav2) + MPC |
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| Manipulation, varied tasks | Diffusion policy or VLA |
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| Generalist humanoid | VLA (π0/RT-2) + whole-body MPC |
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| Safety-critical (surgery, AV) | Verified controllers + redundant perception |
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**기본값**: Classical stack for known tasks; VLA + diffusion policy 의 manipulation/general; Sim2Real with massive DR for new skills.
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## 🔗 Graph
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- 부모: [[Embodied AI]] · [[Control Theory]] · [[Computer Vision]]
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- 변형: [[Surgical-Robotics]]
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- 응용: [[Self-Driving]]
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- Adjacent: [[Reinforcement Learning]] · [[SLAM]] · [[MPC]]
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## 🤖 LLM 활용
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**언제**: high-level task planning (LLM-as-planner), code generation for robot skills, error diagnosis, sim asset generation.
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**언제 X**: 매 closed-loop control 의 X — latency/safety 의 LLM 의 unsuitable; small specialized policy 의 fast inference 의 use.
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## ❌ 안티패턴
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- **Open-loop trust**: sensor noise 의 underestimate — 매 always close the loop.
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- **Sim-only training**: no DR / no real fine-tune → fails reality.
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- **VLA for everything**: 매 simple reach-and-place 의 IK 의 better — VLA 의 long-tail 의 reserve.
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- **No safety layer**: ML policy 의 unbounded — torque limits + collision shield 필요.
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- **Real-time on Python only**: critical loop 의 C++/Rust + ROS2 real-time scheduler.
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## 🧪 검증 / 중복
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- Verified (Siciliano "Robotics: Modelling, Planning and Control"; π0/RT-2 papers; Boston Dynamics Atlas reports 2024-25).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — robotics canonical: classical + VLA + diffusion policy + Sim2Real |
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