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-simulation-environments
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title: Simulation Environments
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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: [Sim Environments, Robotics Simulators, RL Environments]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [simulation, robotics, rl, mujoco, isaac-sim]
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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
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framework: mujoco/isaac-sim/carla
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---
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# Simulation Environments
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## 매 한 줄
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> **"매 simulation environment 의 RL / robotics / autonomous-driving 의 training-deployment loop 의 backbone"**. 매 2026 의 dominant stack 의 MuJoCo (Google DeepMind) + Isaac Sim 4.x (NVIDIA) + CARLA (driving) + Habitat 3.x (embodied) + Gymnasium / PettingZoo API. 매 sim2real 의 domain randomization, real2sim 의 NeRF / 3DGS 의 reconstruction.
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## 매 핵심
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### 매 Major simulators
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- **MuJoCo**: rigid-body contact-rich, fast, MIT-licensed (post-2022). 매 manipulation default.
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- **Isaac Sim 4.x (Omniverse)**: GPU-parallel, photoreal, USD-based, 매 robotics scale.
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- **CARLA**: driving / autonomous vehicle, sensor stack.
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- **Habitat 3.x**: embodied AI, indoor nav, social.
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- **Genesis** (2025+): 매 unified physics + photoreal, 매 emerging.
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- **Gazebo / Webots**: classic robotics, ROS 2 ecosystem.
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### 매 API standards
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- **Gymnasium**: 매 single-agent RL 의 standard (post-Gym).
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- **PettingZoo**: 매 multi-agent.
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- **dm_env**: DeepMind's API.
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- **Isaac Lab**: 매 GPU-vectorized environment 의 standard.
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### 매 Sim2real techniques
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- **Domain randomization**: physics / texture / light / camera 의 random.
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- **System ID**: 매 real measurement 의 sim parameter 의 fit.
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- **Real2sim**: NeRF / 3DGS 의 scene reconstruct → sim.
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- **Adaptive curricula**: easy → hard 의 progressive.
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### 매 응용
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1. RL policy training (manipulation, locomotion).
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2. Synthetic data generation (perception).
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3. Driving stack regression (CARLA scenarios).
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4. Embodied agent (VLM + action) training.
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## 💻 패턴
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### MuJoCo + Gymnasium
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```python
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import mujoco
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import gymnasium as gym
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import numpy as np
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class ReachEnv(gym.Env):
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def __init__(self):
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self.model = mujoco.MjModel.from_xml_path("reach.xml")
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self.data = mujoco.MjData(self.model)
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self.action_space = gym.spaces.Box(-1, 1, (self.model.nu,))
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obs_dim = self.model.nq + self.model.nv + 3
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self.observation_space = gym.spaces.Box(-np.inf, np.inf, (obs_dim,))
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def reset(self, seed=None):
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super().reset(seed=seed)
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mujoco.mj_resetData(self.model, self.data)
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self.target = self.np_random.uniform(-0.3, 0.3, 3)
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return self._obs(), {}
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def step(self, action):
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self.data.ctrl[:] = action
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mujoco.mj_step(self.model, self.data)
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ee = self.data.site("ee").xpos
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rew = -np.linalg.norm(ee - self.target)
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term = bool(rew > -0.02)
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return self._obs(), float(rew), term, False, {}
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def _obs(self):
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return np.concatenate([self.data.qpos, self.data.qvel, self.target])
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```
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### Isaac Lab (GPU-vectorized, 4096 envs)
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```python
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from isaaclab.envs import ManagerBasedRLEnv, ManagerBasedRLEnvCfg
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from isaaclab.scene import InteractiveSceneCfg
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from isaaclab.assets import ArticulationCfg
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from isaaclab_assets.robots.franka import FRANKA_PANDA_CFG
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class FrankaCfg(ManagerBasedRLEnvCfg):
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decimation = 2
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episode_length_s = 5.0
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scene = InteractiveSceneCfg(num_envs=4096, env_spacing=2.5)
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robot: ArticulationCfg = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
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env = ManagerBasedRLEnv(cfg=FrankaCfg()) # 매 4096 envs in parallel on single GPU
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```
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### CARLA driving scenario
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```python
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import carla
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client = carla.Client("localhost", 2000); client.set_timeout(10.0)
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world = client.load_world("Town05")
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bp = world.get_blueprint_library().find("vehicle.tesla.model3")
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spawn = world.get_map().get_spawn_points()[0]
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ego = world.spawn_actor(bp, spawn)
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ego.set_autopilot(True)
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cam_bp = world.get_blueprint_library().find("sensor.camera.rgb")
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cam_bp.set_attribute("image_size_x", "1280"); cam_bp.set_attribute("image_size_y", "720")
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cam = world.spawn_actor(cam_bp, carla.Transform(carla.Location(x=1.5, z=2.4)), attach_to=ego)
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cam.listen(lambda img: img.save_to_disk(f"out/{img.frame:08d}.png"))
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```
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### Domain randomization (sim2real)
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```python
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import numpy as np
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def randomize_episode(model, rng):
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# mass
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for i in range(model.nbody):
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model.body_mass[i] *= rng.uniform(0.8, 1.2)
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# friction
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for i in range(model.ngeom):
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model.geom_friction[i] *= rng.uniform(0.7, 1.3)
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# gravity
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model.opt.gravity[2] = -9.81 * rng.uniform(0.95, 1.05)
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# actuator gain
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for i in range(model.nu):
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model.actuator_gainprm[i, 0] *= rng.uniform(0.9, 1.1)
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```
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### PPO training (SB3 + parallel envs)
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```python
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from stable_baselines3 import PPO
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from stable_baselines3.common.vec_env import SubprocVecEnv
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def make_env(seed):
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def _init():
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env = ReachEnv(); env.reset(seed=seed); return env
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return _init
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vec = SubprocVecEnv([make_env(i) for i in range(16)])
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model = PPO("MlpPolicy", vec, n_steps=2048, batch_size=512, learning_rate=3e-4, verbose=1)
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model.learn(total_timesteps=2_000_000)
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```
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### Habitat 3 (embodied)
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```python
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import habitat
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from habitat.config.default import get_config
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cfg = get_config("benchmark/nav/objectnav/objectnav_hm3d.yaml")
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env = habitat.Env(config=cfg)
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obs = env.reset()
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for _ in range(100):
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obs = env.step({"action": "move_forward"})
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if env.episode_over: break
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```
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### Real2sim (3DGS scene)
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```python
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# 매 phone capture → 3DGS reconstruct → MuJoCo / Isaac scene
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# nerfstudio + 3DGS export
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# ns-train splatfacto --data captures/kitchen
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# ns-export gaussian-splat --load-config out/config.yml --output-dir scene/
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# 매 mesh + texture 의 USD / GLB convert → simulator import
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```
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## 매 결정 기준
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| 상황 | Simulator |
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|---|---|
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| Manipulation, fast iter | MuJoCo |
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| Massive parallel RL | Isaac Lab |
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| Photoreal sensor | Isaac Sim |
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| Driving | CARLA |
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| Embodied indoor | Habitat 3 |
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| ROS 2 ecosystem | Gazebo |
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**기본값**: MuJoCo for prototyping, Isaac Lab for scale, sim2real with DR.
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## 🔗 Graph
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- 부모: [[Reinforcement-Learning]] · [[Robotics]]
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- 응용: [[Sim2Real]] · [[Synthetic-Data]] · [[Embodied-AI]]
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- Adjacent: [[3D_Gaussian_Splatting]]
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## 🤖 LLM 활용
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**언제**: scenario / task description → env config gen, reward function 의 draft, scene XML scaffold.
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**언제 X**: physics tuning (system ID), real-robot deployment.
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## ❌ 안티패턴
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- **No domain randomization**: 매 sim2real gap.
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- **Tiny env count**: 매 Isaac Lab 의 4096 의 미사용.
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- **Hardcoded scene**: 매 USD / procedural gen 의 use.
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- **Simulation-only eval**: 매 real-robot validation 의 skip.
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## 🧪 검증 / 중복
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- Verified (DeepMind MuJoCo, NVIDIA Isaac, CARLA, FAIR Habitat).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — full simulator stack with patterns |
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