2nd/10_Wiki/Topics/AI_and_ML/Simulation-Environments.md

---
id: wiki-2026-0508-simulation-environments
title: Simulation Environments
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Sim Environments, Robotics Simulators, RL Environments]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [simulation, robotics, rl, mujoco, isaac-sim]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: mujoco/isaac-sim/carla
---

# Simulation Environments

## 매 한 줄
> **"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.

## 매 핵심

### 매 Major simulators
- **MuJoCo**: rigid-body contact-rich, fast, MIT-licensed (post-2022). 매 manipulation default.
- **Isaac Sim 4.x (Omniverse)**: GPU-parallel, photoreal, USD-based, 매 robotics scale.
- **CARLA**: driving / autonomous vehicle, sensor stack.
- **Habitat 3.x**: embodied AI, indoor nav, social.
- **Genesis** (2025+): 매 unified physics + photoreal, 매 emerging.
- **Gazebo / Webots**: classic robotics, ROS 2 ecosystem.

### 매 API standards
- **Gymnasium**: 매 single-agent RL 의 standard (post-Gym).
- **PettingZoo**: 매 multi-agent.
- **dm_env**: DeepMind's API.
- **Isaac Lab**: 매 GPU-vectorized environment 의 standard.

### 매 Sim2real techniques
- **Domain randomization**: physics / texture / light / camera 의 random.
- **System ID**: 매 real measurement 의 sim parameter 의 fit.
- **Real2sim**: NeRF / 3DGS 의 scene reconstruct → sim.
- **Adaptive curricula**: easy → hard 의 progressive.

### 매 응용
1. RL policy training (manipulation, locomotion).
2. Synthetic data generation (perception).
3. Driving stack regression (CARLA scenarios).
4. Embodied agent (VLM + action) training.

## 💻 패턴

### MuJoCo + Gymnasium
```python
import mujoco
import gymnasium as gym
import numpy as np

class ReachEnv(gym.Env):
    def __init__(self):
        self.model = mujoco.MjModel.from_xml_path("reach.xml")
        self.data = mujoco.MjData(self.model)
        self.action_space = gym.spaces.Box(-1, 1, (self.model.nu,))
        obs_dim = self.model.nq + self.model.nv + 3
        self.observation_space = gym.spaces.Box(-np.inf, np.inf, (obs_dim,))

    def reset(self, seed=None):
        super().reset(seed=seed)
        mujoco.mj_resetData(self.model, self.data)
        self.target = self.np_random.uniform(-0.3, 0.3, 3)
        return self._obs(), {}

    def step(self, action):
        self.data.ctrl[:] = action
        mujoco.mj_step(self.model, self.data)
        ee = self.data.site("ee").xpos
        rew = -np.linalg.norm(ee - self.target)
        term = bool(rew > -0.02)
        return self._obs(), float(rew), term, False, {}

    def _obs(self):
        return np.concatenate([self.data.qpos, self.data.qvel, self.target])
```

### Isaac Lab (GPU-vectorized, 4096 envs)
```python
from isaaclab.envs import ManagerBasedRLEnv, ManagerBasedRLEnvCfg
from isaaclab.scene import InteractiveSceneCfg
from isaaclab.assets import ArticulationCfg
from isaaclab_assets.robots.franka import FRANKA_PANDA_CFG

class FrankaCfg(ManagerBasedRLEnvCfg):
    decimation = 2
    episode_length_s = 5.0
    scene = InteractiveSceneCfg(num_envs=4096, env_spacing=2.5)
    robot: ArticulationCfg = FRANKA_PANDA_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")

env = ManagerBasedRLEnv(cfg=FrankaCfg())  # 매 4096 envs in parallel on single GPU
```

### CARLA driving scenario
```python
import carla
client = carla.Client("localhost", 2000); client.set_timeout(10.0)
world = client.load_world("Town05")

bp = world.get_blueprint_library().find("vehicle.tesla.model3")
spawn = world.get_map().get_spawn_points()[0]
ego = world.spawn_actor(bp, spawn)
ego.set_autopilot(True)

cam_bp = world.get_blueprint_library().find("sensor.camera.rgb")
cam_bp.set_attribute("image_size_x", "1280"); cam_bp.set_attribute("image_size_y", "720")
cam = world.spawn_actor(cam_bp, carla.Transform(carla.Location(x=1.5, z=2.4)), attach_to=ego)
cam.listen(lambda img: img.save_to_disk(f"out/{img.frame:08d}.png"))
```

### Domain randomization (sim2real)
```python
import numpy as np

def randomize_episode(model, rng):
    # mass
    for i in range(model.nbody):
        model.body_mass[i] *= rng.uniform(0.8, 1.2)
    # friction
    for i in range(model.ngeom):
        model.geom_friction[i] *= rng.uniform(0.7, 1.3)
    # gravity
    model.opt.gravity[2] = -9.81 * rng.uniform(0.95, 1.05)
    # actuator gain
    for i in range(model.nu):
        model.actuator_gainprm[i, 0] *= rng.uniform(0.9, 1.1)
```

### PPO training (SB3 + parallel envs)
```python
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import SubprocVecEnv

def make_env(seed):
    def _init():
        env = ReachEnv(); env.reset(seed=seed); return env
    return _init

vec = SubprocVecEnv([make_env(i) for i in range(16)])
model = PPO("MlpPolicy", vec, n_steps=2048, batch_size=512, learning_rate=3e-4, verbose=1)
model.learn(total_timesteps=2_000_000)
```

### Habitat 3 (embodied)
```python
import habitat
from habitat.config.default import get_config

cfg = get_config("benchmark/nav/objectnav/objectnav_hm3d.yaml")
env = habitat.Env(config=cfg)
obs = env.reset()
for _ in range(100):
    obs = env.step({"action": "move_forward"})
    if env.episode_over: break
```

### Real2sim (3DGS scene)
```python
# 매 phone capture → 3DGS reconstruct → MuJoCo / Isaac scene
# nerfstudio + 3DGS export
# ns-train splatfacto --data captures/kitchen
# ns-export gaussian-splat --load-config out/config.yml --output-dir scene/
# 매 mesh + texture 의 USD / GLB convert → simulator import
```

## 매 결정 기준
| 상황 | Simulator |
|---|---|
| Manipulation, fast iter | MuJoCo |
| Massive parallel RL | Isaac Lab |
| Photoreal sensor | Isaac Sim |
| Driving | CARLA |
| Embodied indoor | Habitat 3 |
| ROS 2 ecosystem | Gazebo |

**기본값**: MuJoCo for prototyping, Isaac Lab for scale, sim2real with DR.

## 🔗 Graph
- 부모: [[Reinforcement-Learning]] · [[Robotics]]
- 응용: [[Sim2Real]] · [[Synthetic-Data]] · [[Embodied-AI]]
- Adjacent: [[3D-Gaussian-Splatting]]

## 🤖 LLM 활용
**언제**: scenario / task description → env config gen, reward function 의 draft, scene XML scaffold.
**언제 X**: physics tuning (system ID), real-robot deployment.

## ❌ 안티패턴
- **No domain randomization**: 매 sim2real gap.
- **Tiny env count**: 매 Isaac Lab 의 4096 의 미사용.
- **Hardcoded scene**: 매 USD / procedural gen 의 use.
- **Simulation-only eval**: 매 real-robot validation 의 skip.

## 🧪 검증 / 중복
- Verified (DeepMind MuJoCo, NVIDIA Isaac, CARLA, FAIR Habitat).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — full simulator stack with patterns |