Weibing Wan
Papers
1
Total Citations
6
H-Index
1
About
Weibing Wan is a researcher in robotics and artificial intelligence, with a primary focus on humanoid robot control, motion perception, and imitation learning. His most cited work, "Robust Regression-Based Motion Perception for Online Imitation on Humanoid Robot" (2017), introduces a novel approach to enabling robots to perceive and replicate human movements in real time. By leveraging robust regression techniques, Wan addresses challenges in noisy sensor data and dynamic environments, allowing humanoid robots to perform online imitation with improved accuracy and stability. This contribution has garnered 6 citations, reflecting its relevance in the growing field of robot learning from demonstration. Wan’s research bridges the gap between perception and action, advancing the development of more adaptive and autonomous robotic systems. His work is particularly valuable for applications in human-robot interaction, where seamless and intuitive collaboration is essential. Through his focus on robust, real-time motion perception, Wan contributes to the broader goal of creating robots that can learn and adapt in unstructured, human-centric environments.
Research Focus
Key Achievements
Top Papers
- 1