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About

Bernard Chen is a roboticist whose research lies at the intersection of bio-inspired design, surgical robotics, and haptic perception. His most impactful work, a lobster-inspired bending module for compliant robotic applications (2020, 27 citations), demonstrates his core contribution: developing soft, adaptable actuators that can bridge the gap between delicate object handling and heavy lifting. This principle extends to his work on a lobster-inspired articulated shaft for minimally invasive surgery (MIS) (2020, 10 citations), where he applies biological compliance to create safer, more dexterous surgical tools. Chen has also addressed critical challenges in surgical robotics, notably proposing a skeletal prototype for a surgical arm based on a dual-triangular mechanism (2016, 17 citations) to reduce the bulky footprint of traditional remote center of motion (RCM) devices, thereby minimizing arm interference in tight operating spaces. Beyond hardware, Chen investigates the efficiency of haptic-based object identification (2021, 3 citations) and has developed a Beta Mixture Model to manage uncertainties in robotic tactile sensing (2022, 3 citations). His work on pneumatic bidirectional modules with variable stiffness and closed-loop control (2021, 2 citations) further showcases his drive to create versatile, precisely controlled soft robots. With a total of 62 citations across his most-cited works, Chen is establishing himself as a key innovator in creating more compliant, intelligent, and space-efficient robotic systems for both industrial and medical applications.