Papers
2
Total Citations
17
H-Index
2
About
Zeyuan Feng is a robotics researcher whose work bridges human-robot collaboration and legged locomotion. His key research areas include human-robot collaborative assembly, quadrupedal robot morphology, and trajectory optimization. Feng’s major contributions include developing an action fusion recognition model based on GAT-GRU binary classification networks, which enhances the safety and efficiency of human-robot collaborative assembly tasks. This work has garnered 9 citations, reflecting its relevance to the growing field of collaborative robotics. Additionally, Feng has explored the impact of torso morphology on quadrupedal locomotion, challenging conventional rigid torso designs by comparing them to more flexible, animal-like spines through trajectory optimization. This study, with 8 citations, provides insights into how actuated degrees of freedom can be distributed to improve robot agility and efficiency. His work is notable for questioning established design paradigms in legged robotics, drawing inspiration from biological systems to advance robot performance. Feng’s research is valuable for students and researchers interested in the intersection of human-robot interaction, biomechanics, and robot design optimization.
Research Focus
Key Achievements
Top Papers
- 1
- 2