Building Synthetic Training Data Pipelines for Robotics

Building Synthetic Training Data Pipelines for Robotics

Real-world robot data is expensive to collect and label. Synthetic data pipelines — generating training data directly from simulation, complete with automatic ground-truth labels — let you produce far larger and more diverse datasets than manual collection ever could, for a fraction of the cost. This tutorial covers designing and building a synthetic data pipeline for robotics, from scene generation through to a usable, labeled dataset.

Where synthetic data fits in a robot learning pipeline

Synthetic data pipelines are most valuable for:

Perception training — object detection, segmentation, pose estimation, and grasp point prediction all benefit enormously from large volumes of perfectly labeled data, which is exactly what simulation provides for free.