Gundula Runge-Borchert
Papers
2
Total Citations
19
H-Index
2
About
Gundula Runge-Borchert is a leading researcher in soft robotics, specializing in the modeling and control of soft pneumatic actuators. Her work bridges the gap between the inherent flexibility of soft material systems and the precision required for real-world applications, particularly in safe human-robot interaction. She is best known for pioneering the use of transfer learning to overcome the complex, nonlinear dynamics of soft actuators, a contribution detailed in her highly cited 2021 paper (12 citations). This approach enables accurate modeling and control without extensive retraining, significantly advancing the field's practical viability. Her earlier 2018 study on optimizing neural network hyperparameters for soft actuators (7 citations) laid essential groundwork, demonstrating her sustained impact. Runge-Borchert's research directly addresses the core challenge of making soft robots—which offer unparalleled safety and adaptability—reliable enough for deployment in healthcare, manufacturing, and assistive technologies. Her work is a critical step toward realizing the full potential of soft robotics in human-centric environments.
Research Focus
Key Achievements
Top Papers
- 1Transfer learning for accurate modeling and control of soft actuators12 citations · 2021
- 2