Xiaohe Dai
Papers
2
Total Citations
24
H-Index
2
About
Xiaohe Dai is a researcher whose work bridges the fields of robotics, human-robot interaction, and computer vision, with a particular focus on enabling machines to learn from and operate alongside humans. His key contributions lie in two core areas: trajectory learning from demonstration and robust people detection and tracking. In his highly cited 2014 paper, "Generating a Style-Adaptive Trajectory from Multiple Demonstrations," Dai advanced the state of the art in robot learning by developing methods to not only replicate but also adapt learned movement patterns to different styles, a significant step toward more flexible and personalized robotic assistants. Complementing this, his work on "A robust people detection, tracking, and counting system" (2014) tackled the critical challenge of uniquely identifying and counting individuals in dynamic, real-world environments—a fundamental capability for autonomous systems operating in public spaces. With over 20 citations across these two key publications, Dai’s research has provided foundational techniques for making robots more perceptive and adaptive, directly impacting the development of socially aware and collaborative autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Generating a Style-Adaptive Trajectory from Multiple Demonstrations13 citations · 2014
- 2A robust people detection, tracking, and counting system11 citations · 2014