Simulation

Simulation and Synthetic Data Generation

Why Synthetic Data?
Real data is expensive, limited, and may have privacy concerns. Synthetic data enables training at scale.

Simulation Environments
| Domain | Tools |
|--------|-------|
| Robotics | Isaac Sim, MuJoCo, PyBullet |
| Autonomous driving | CARLA, AirSim |
| Games/3D | Unity, Unreal Engine |
| Physics | PyBullet, Drake |

Synthetic Data Generation

3D Scene Generation

# Procedural scene generation
import blenderproc as bproc

# Random room layout
room = bproc.create_room()
objects = bproc.loader.load_objects("assets/")

# Random placement
for obj in objects:
    obj.set_location(random_position())
    obj.set_rotation(random_rotation())

# Render with random lighting
bproc.camera.add_camera_poses()
data = bproc.renderer.render()

Domain Randomization
Vary parameters to improve generalization:
| Parameter | Variations |
|-----------|------------|
| Lighting | Intensity, color, position |
| Textures | Color, patterns, materials |
| Camera | Position, angle, lens |
| Objects | Scale, position, orientation |
| Backgrounds | Variety of environments |

LLM-Generated Synthetic Data

Conversation Generation

def generate_synthetic_conversation(topic: str, style: str) -> list:
    return llm.generate(f"""
Generate a realistic conversation about {topic}.
Style: {style}
Format as JSON list of {{role, content}}.
    """)

Instruction Data

def generate_instruction_pairs(domain: str, n: int) -> list:
    return llm.generate(f"""
Generate {n} instruction-response pairs for {domain}.
Format: [{{instruction: ..., response: ...}}]
    """)

Sim-to-Real Transfer
| Technique | Description |
|-----------|-------------|
| Domain randomization | Train on varied simulated data |
| Adversarial adaptation | Learn domain-invariant features |
| Progressive transfer | Gradually increase realism |
| Real data fine-tuning | Small real dataset for final tuning |

Use Cases
| Use Case | Synthetic Data Approach |
|----------|------------------------|
| Object detection | Rendered 3D scenes |
| Autonomous driving | CARLA simulations |
| NLP training | LLM-generated text |
| Anomaly detection | Synthetic anomalies |
| Robot training | Physics simulation |

Best Practices
- Validate synthetic data quality with real data benchmarks
- Use domain randomization for generalization
- Mix synthetic with real data when possible
- Monitor for distribution shift
- Continuously improve realism