Data Augmentation Pipeline for Zero-Shot Sim-to-Real Transfer in Vision-Based Robot Navigation
Description
We developed a data augmentation pipeline to enhance the training of vision-based navigation models for robotics, addressing the challenges of limited real-world data.
Detailed example
realistic, labeled images from synthetic data
AI / analytics pattern
Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
By combining foundation model-based segmentation with CycleGAN for sim-to-real style transfer, our approach generates realistic, labeled images from synthetic data, enabling models to better generalize to real-world environments. This innovative capability enhances vision-based navigation tasks, such as vehicle pose estimation and road segmentation, significantly closing the sim-to-real gap in robotic perception applications.
Controls / human review
ATO: Not reported; PIA: Not published