Haoyang Mao
Papers
1
Total Citations
15
H-Index
1
About
Haoyang Mao is a researcher at the forefront of human-robot collaboration, with a primary focus on assembly action recognition and intelligent manufacturing. His most influential work, "A skeleton-based assembly action recognition method with feature fusion for human-robot collaborative assembly" (2024), has already garnered 15 citations, signaling its growing impact in the field. Mao’s key contribution lies in developing a novel skeleton-based approach that integrates multi-modal feature fusion—combining spatial, temporal, and joint-level data—to accurately recognize and predict human assembly actions in real time. This innovation directly addresses critical challenges in collaborative robotics, such as safe and efficient human-robot interaction, by enabling machines to understand and anticipate worker motions during complex assembly tasks. His method not only improves recognition accuracy but also reduces computational overhead, making it practical for industrial deployment. Mao’s work is particularly notable for bridging the gap between computer vision and manufacturing automation, offering a scalable solution for smart factories. As a rising scholar, his research promises to reshape how humans and robots work side by side, enhancing both productivity and workplace safety in the era of Industry 4.0.
Research Focus
Key Achievements
Top Papers
- 1