Synthetic Data Generation

In the field of robotics, synthetic data can be used to train AI models that are deployed for robot perception, grasping, or on robots used for visual inspection.

Detecting defects in manufactured parts is extremely difficult because ‌often the anomalies are subtle. Synthetic data based on actual defects such as scratches, chips, or dents, can be created to train AI models to catch defects early in the manufacturing process.

Deploying an autonomous vehicle so that it can safely navigate its surroundings requires massive amounts of training data, which is extremely expensive and dangerous to acquire in real life. 3D synthetic data can be used to develop and test autonomous vehicle solutions in a simulation environment, reducing testing and training times, and lowering costs.

To generate synthetic data, you must first create a digital twin of the environment that you’ll be training your AI model on. 

If training an AI model for a warehouse robot, you will need to create a virtual scene with objects such as pallet jacks and storage racks. If training an AI model for visual inspection on an assembly line, you will need to create a virtual scene with objects such as a conveyor belt and the product being produced.

One of the key challenges that developers face in developing synthetic data pipelines is closing the sim-to-real gap. To create synthetic data that reflects real-world scenarios, you will need to randomize your scene to reflect the plethora of scenarios that an AI model might encounter. This means modifying aspects of the scene such as the position of objects, texture, and lighting. You may also want to modify the camera position and add environmental distractors that may affect the model's performance.

With NVIDIA Omniverse™ Replicator SDK, developers can build custom pipelines that enable technical artists to create and randomize synthetic data for various AI training use cases. Omniverse Replicator powers NVIDIA Isaac Sim™, enabling you to generate synthetic data for robotics applications, and autonomous vehicle simulation, which enables you to generate synthetic data for accelerated development.