Wenwen Si

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About

Wenwen Si is a pioneering researcher in human-robot interaction and augmented reality, whose work bridges the gap between robotic intelligence and intuitive human teaching. Her key research areas include interactive robot learning, knowledge representation, and augmented reality interfaces for robotics. Si's most influential contribution is her groundbreaking 2018 paper, "Interactive Robot Knowledge Patching Using Augmented Reality," which has garnered 73 citations. In this work, she introduced a novel approach using Microsoft HoloLens to diagnose, teach, and patch interpretable robot knowledge—a significant advancement in making robotic systems more transparent and adaptable. By developing a Temporal And-Or graph (T-AOG) of bottle-opening tasks learned from human demonstration and programmed to the robot, Si demonstrated how complex manipulation skills could be transferred seamlessly through augmented reality. This work addresses the critical challenge of enabling non-expert users to interactively correct and update robot knowledge without requiring programming expertise. Si's research has profound implications for manufacturing, service robotics, and assistive technologies, where robots must learn and adapt to dynamic environments. Her innovative fusion of augmented reality with robot learning continues to inspire new approaches in interactive machine teaching and human-robot collaboration.