University of Waterloo

FLAIROP (Federated Learning for Robot Picking)

Yuhao Chen

May 20, 2022, 11:30am EC4-2101A

Artificial Intelligence (AI) has proven to enable large economic efficiency gains. In the field of industrial robotics, AI has the potential to automate tedious, heavy, and complex tasks, but needs large amounts of data. As many industrial manufacturing companies are small or medium sized. They often use a small amount of data for a specific task. Sharing data across factories and companies is a promising but challenging approach, as companies do not like to share their critical production data. The resulting small datasets lead to less accurate AI models for robotic applications and the full potential of AI systems in industrial environments is not exploited. Federated learning is an emerging approach toward distributed, privacy-preserving machine learning. In this project, we aim to develop a federated learning system in the domain of robotic picking and placing of unknown objects. The goal is to boost current AI solutions with more data while preserving privacy regulations.