Amir Hooshair
Papers
2
Total Citations
9
H-Index
2
About
Amir Hooshair is a researcher at the forefront of soft robotics and medical instrumentation, specializing in the miniaturization of optical tactile sensors and the integration of intelligent sensing in flexible robotic systems. His work addresses critical challenges in robotic minimally invasive surgery, particularly the lack of tactile feedback in confined anatomical spaces. Hooshair’s 2018 study on a bending-based formulation for light intensity modulation introduced a novel, miniaturized tactile sensor design, validated for enhanced sensitivity and compactness—a foundational contribution with 7 citations that continues to influence optical sensing strategies. More recently, his 2023 paper on WaveLeNet presents a transfer neural calibration framework for embedded sensing in soft robots, targeting intraluminal procedures like bronchoscopy and cardiovascular intervention. This work, already garnering 2 citations, demonstrates how machine learning can overcome calibration hurdles in highly compliant, sensorized robots. By bridging optical physics and deep learning, Hooshair is advancing the practical deployment of soft, sensor-rich robots in delicate surgical environments, making him a notable emerging voice in the intersection of tactile sensing, miniaturization, and intelligent control.
Research Focus
Key Achievements
Top Papers
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- 2