Anup Teejo Mathew
Khalifa University of Science and Technology, National University of Singapore
Papers
20
Total Citations
522
H-Index
8
About
Anup Teejo Mathew is an emerging robotics researcher whose work sits at the intersection of soft robotics modeling, control, and simulation. His primary contributions center on developing rigorous mathematical frameworks for understanding and controlling soft and hybrid rigid-soft robotic systems, with a particular emphasis on geometric and strain-based approaches. His most influential work, "Soft Robots Modeling: A Structured Overview" (2023), has amassed an impressive 292 citations, establishing him as a key synthesizer of the field's theoretical landscape. Mathew's development of SoRoSim, an accessible MATLAB toolbox for hybrid robot simulation, has meaningfully lowered the barrier for researchers entering soft robotics, earning 75 citations since 2022. He has made notable advances in inverse kinematics for soft manipulators, reduced-order modeling, and implicit strain parameterization — tools critical for making soft robot control computationally feasible. Beyond manipulation, his research extends to medical robotics, including concentric tube robots for minimally invasive surgery, underwater bio-inspired drones, and proprioceptive sensing integration. Collectively, his work reflects a commitment to bridging theoretical rigor with practical, deployable soft robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Soft Robots Modeling: A Structured Overview292 citations · 2023
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- 5Dynamics and Control of Soft Robots With Implicit Strain Parametrization17 citations · 2024
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- 7Advancing Soft Robot Proprioception Through 6D Strain Sensors Embedding10 citations · 2025
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- 10Multifunctional Underwater Soft Robots: A Simulation Essay5 citations · 2022