Majid Roshanfar
Papers
12
Total Citations
132
H-Index
8
About
Majid Roshanfar is an emerging researcher at the intersection of soft robotics, surgical robotics, and biomechanical sensing, with a focused body of work advancing the capabilities of compliant robotic systems for minimally invasive medical interventions. His research addresses some of the most pressing challenges in the field — including force sensing, shape estimation, stiffness adaptation, and dynamic modeling of soft robots — with particular emphasis on cardiac and intraluminal surgical applications. Among his most impactful contributions are novel polymer-based embedded sensors capable of six-degree-of-freedom force-torque measurement, validated using learning-based calibration techniques, and a gelatin-based shape sensor tailored for low-stiffness soft robotic platforms. His work on hyperelastic and Cosserat rod-based modeling of hybrid-actuated soft robots has provided rigorous theoretical frameworks that bridge continuum mechanics with real-world surgical constraints. Roshanfar has also pioneered semi-autonomous stiffness adaptation strategies, enabling soft surgical robots to dynamically modulate rigidity during interventions — a critical advancement for safe tissue interaction. With over 125 cumulative citations across publications since 2021, and contributions spanning deep learning-based force estimation to next-generation cardiac intervention robotics, Roshanfar's work is rapidly shaping the future of intelligent, compliant surgical systems.
Research Focus
Key Achievements
Top Papers
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- 7Advanced Robotics for the Next-Generation of Cardiac Interventions10 citations · 2025
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