Chapter 16: Domain Randomization and Sim-to-Real

Overview

This chapter explores domain randomization techniques for improving sim-to-real transfer. You'll learn how to make trained policies robust to variations, enabling successful deployment from simulation to real robots.

Learning Objectives

Learning Objectives

By the end of this chapter, you will be able to:

• Understand domain randomization principles and benefits
• Implement visual and physical randomization techniques
• Apply domain randomization to RL training pipelines
• Evaluate and improve sim-to-real transfer performance

Domain Randomization

Domain randomization is a technique to improve sim-to-real transfer by randomizing environment parameters during training, making policies robust to parameter variations, and reducing the reality gap between simulation and real world.

Types of Randomization

• Visual Randomization: Lighting, textures, colors
• Physical Randomization: Mass, friction, restitution
• Dynamics Randomization: Joint damping, actuator properties
• Sensor Randomization: Noise, delay, calibration parameters

Implementation Example

class DomainRandomization:
    def __init__(self):
        self.randomization_params = {
            'lighting': {'min': 0.5, 'max': 2.0},
            'friction': {'min': 0.1, 'max': 0.8},
            'mass': {'min': 0.8, 'max': 1.2},
            'restitution': {'min': 0.0, 'max': 0.5}
        }

    def randomize_environment(self, env_id):
        light_intensity = np.random.uniform(
            self.randomization_params['lighting']['min'],
            self.randomization_params['lighting']['max']
        )

        friction = np.random.uniform(
            self.randomization_params['friction']['min'],
            self.randomization_params['friction']['max']
        )

        self.apply_randomization(env_id, light_intensity, friction)

Sim-to-Real Transfer

Transfer Techniques

• Domain randomization: Make policies robust to variations
• System identification: Match simulation parameters to reality
• Fine-tuning: Adapt policies with minimal real-world data

Summary

Domain randomization improves sim-to-real transfer by training policies that are robust to environmental variations. By randomizing visual, physical, and sensor properties during training, robots can successfully transfer learned behaviors from simulation to real-world deployment.

Key Takeaways

Key Takeaways

• Domain randomization reduces the reality gap between simulation and real world
• Multiple randomization types (visual, physical, sensor) improve robustness
• Proper randomization ranges balance diversity and learning efficiency
• Sim-to-real transfer enables safe, efficient robot training in simulation

What's Next

In the next chapter, we'll explore voice-to-action systems using OpenAI Whisper, learning how to enable robots to understand and respond to natural language commands.