Bixiao Wu
Papers
2
Total Citations
67
H-Index
2
About
Bixiao Wu is a researcher at the forefront of human-robot interaction, specializing in vision-based gesture recognition and prediction. Their work focuses on enabling more intuitive communication between humans and social robots by interpreting hand movements in real time. Wu’s most influential contribution is a visual-based gesture prediction framework (2021, 42 citations), which anticipates hand gestures before they are fully executed, allowing robots to respond proactively in daily-life settings. This system leverages hand position, direction, and joint data, bridging the gap between raw visual input and robotic action. In a second highly cited study (2021, 25 citations), Wu tackled a critical challenge in the field: transfer learning for gesture recognition. By enabling models to apply knowledge from existing domains to new, unseen gestures, this work reduces the need for costly data collection and retraining, making gesture interfaces more scalable and practical. Together, these contributions advance the goal of seamless, adaptive human-robot collaboration, with Wu’s research cited over 67 times in the growing literature on social robotics and interactive AI.
Research Focus
Key Achievements
Top Papers
- 1A Visual-Based Gesture Prediction Framework Applied in Social Robots42 citations · 2021
- 2Research on Transfer Learning of Vision-based Gesture Recognition25 citations · 2021