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About

Andreas Geier is a researcher at the intersection of robotics, biomechanics, and tactile sensing. His work spans two distinct but equally impactful domains: advancing robotic touch through soft sensor skins and deep learning, and improving orthopedic implant performance through hardware-in-the-loop simulation. In robotic manipulation, Geier pioneered the use of densely distributed triaxial force sensors—embedding uSkin tactile arrays into an Allegro Hand to achieve active object recognition with 240 simultaneous force vector measurements. His 2018 paper on this topic has garnered 45 citations, establishing a foundation for dexterous, touch-driven robotic control. More recently, he has developed end-to-end tactile feedback loops using deep GRU-autoencoders, enabling dynamic texture recognition in teleoperation and prosthetic applications. In parallel, Geier has made significant contributions to orthopedic biomechanics. His robot-assisted test methods, which combine six-axis robots with musculoskeletal models, have been used to evaluate how surgical parameters affect total knee and hip replacements. His 2019 study on bicondylar knee endoprostheses (27 citations) and his 2017 work on dislocation factors in hip replacements (15 citations) provide critical insights for improving implant longevity and patient outcomes. By bridging soft robotics and medical device testing, Geier demonstrates a rare ability to translate sensor technology from the lab bench to the operating room.