Synthetic Data Generation for Perception Model Training in Isaac Sim

Welcome to Synthetic Data Generation for Perception Model Training in Isaac Sim. In this module, we’ll delve into the fundamentals of perception models, synthetic data generation, and how to apply these concepts to real-world robotic applications. Throughout this module, we’ll take a hands-on approach to learning, guiding you through the process of designing a simulated environment, generating synthetic data, training an AI perception model, and integrating it into a robotic workflow.

Note

This module offers a dynamic blend of learning styles—some lessons are lecture-focused, providing foundational knowledge, while others are hands-on, guiding you through practical activities. Together, they ensure you gain both the understanding and skills needed to train and deploy AI perception models effectively.

Learning Objectives

In this module we will:

• Analyze the role of perception models in dynamic robotic tasks and identify the importance of fine-tuned models in specific settings.

• Design a simulated scene suitable for synthetic data generation (SDG) by creating an OpenUSD scene with SimReady assets.

• Apply domain randomization techniques using Replicator in Isaac Sim to generate a synthetic dataset for training AI models.

• Evaluate the effectiveness of a trained AI perception model by conducting validation and testing.

• Use a comprehensive workflow for training a perception model using synthetic data generation, including data generation, model training, and model validation.

By the end of this module, you’ll have the skills and knowledge to develop and test robust perception models for your own robotic projects. In the first lesson, “Perception Models in Dynamic Robotic Tasks,” we’ll lay the foundation for the module by discussing the role of perception models in dynamic robotic tasks and how they enable robots to operate in changing environments.