Bingyi Su
Papers
7
Total Citations
78
H-Index
5
About
Bingyi Su is a leading researcher at the forefront of human-robot collaboration (HRC), with a focused mission to make industrial robots not only more efficient but also safer and less stressful for human workers. Her work bridges the critical gap between automation and human well-being, addressing two core challenges: mental stress and physical musculoskeletal health. In her most cited work (33 citations), Su explores how robot interaction impacts workers' mental stress during collaborative assembly, while a series of high-impact studies (totaling over 75 citations) systematically investigate factors like robot approach direction, speed, and trajectory during handover tasks. She has pioneered the use of advanced computational methods to improve worker safety, developing novel model-free reinforcement learning and conditional variational auto-encoder models that dynamically optimize worker postures to reduce musculoskeletal disorder (MSD) risks. Her 2023 study on reinforcement learning for posture improvement has been particularly influential. Most recently, Su has ventured into the psychological dimensions of HRC, examining how anthropomorphic features—such as a robot’s face or voice—affect user perception and emotional response. Her work is essential reading for anyone designing collaborative robots that are truly human-centered.
Research Focus
Key Achievements
Top Papers
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