Yaqing Song
Papers
4
Total Citations
120
H-Index
4
About
Yaqing Song’s research lies at the intersection of reconfigurable mechanisms, parallel kinematic machines, and bio-inspired robotics, with a growing focus on learning from demonstration for collaborative robots. Her most impactful work, “Reconfigurability of the origami-inspired integrated 8R kinematotropic metamorphic mechanism and its evolved 6R and 4R mechanisms” (2021, 90 citations), introduces a novel class of metamorphic mechanisms that can change mobility and topology—a key advance for adaptive robotic systems. She has also contributed to precision engineering through interval kinetostatic modeling for Exechon-like parallel kinematic machines, addressing parameter uncertainties to improve reliability. In bipedal locomotion, Song proposed a full-range walking energy efficiency concept, optimizing mechanism parameters for human-like, energy-saving gait. Most recently, she has explored robust learning from demonstration using GANs and affine transformations (2024), aiming to simplify programming for collaborative robots. Her work demonstrates a consistent thread: enabling robots to move, reconfigure, and learn more like living systems. With over 120 total citations and publications spanning mechanism design, dynamics, and machine learning, Song is establishing herself as a versatile researcher in modern robotics and mechanical design.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4