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

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:

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

Use Cases

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