Mats Wiese
Papers
14
Total Citations
313
H-Index
9
About
Mats Wiese is a distinguished researcher in soft material robotics, whose work sits at the intersection of computational modeling, actuation systems, and human-robot interaction. His research has made significant strides in developing mathematical frameworks for understanding and controlling soft robots — systems whose inherent compliance and flexibility make them uniquely suited for safe interaction with humans and operation in unpredictable environments. Wiese's most influential contributions include pioneering kinematic modeling approaches that combine finite element analysis with piecewise constant curvature kinematics (69 citations) and leveraging artificial neural networks trained on FEM data for modular soft robotic systems (47 citations). His early work on super-elastic piezoresistive sensors embedded in soft actuators (70 citations) demonstrates a strong commitment to bridging sensing and actuation in next-generation robotic systems. He has also advanced variable-stiffness robotics through low-melting-point materials (34 citations) and explored Cosserat rod models for precise parameter identification and contact analysis in continuum robots. More recently, Wiese has expanded into applied human-robot interaction, investigating haptic feedback systems for autonomous vehicle transitions. With cumulative citations exceeding 290 across a focused body of work, his research meaningfully advances the theoretical foundations and practical capabilities of soft robotics for real-world deployment.
Research Focus
Key Achievements
Top Papers
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- 3FEM-based training of artificial neural networks for modular soft robots47 citations · 2017
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- 6Kinematic Modeling of a Soft Pneumatic Actuator Using Cubic Hermite Splines15 citations · 2019
- 7Transfer learning for accurate modeling and control of soft actuators12 citations · 2021
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