Kai Zeng
Papers
1
Total Citations
12
H-Index
1
About
Kai Zeng is a rising researcher in human–robot interaction (HRI), with a focus on developing fast, responsive, and multimodal gesture recognition systems. Their most cited work, “A Fast-Response Dynamic-Static Parallel Attention GCN Network for Body–Hand Gesture Recognition in HRI” (2023, 12 citations), addresses critical limitations in current interaction methods—namely, slow algorithmic response times and insufficient integration of body and hand gestures. Zeng’s major contribution lies in designing a parallel attention graph convolutional network (GCN) that dynamically and statically processes gestures, enabling real-time, natural HRI. This work is notable for its potential to advance robotics applications where speed and multimodal input are essential. Though early in their career, Zeng’s research is already shaping the future of intuitive human–robot collaboration, promising more seamless and efficient interfaces for both industrial and service robotics.
Research Focus
Key Achievements
Top Papers
- 1