Matthew Clevenger

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About

Matthew Clevenger is a researcher whose work sits at the intersection of robotics, human-machine interaction, and assistive technology. His primary research areas include robotic manipulation, force learning from human demonstration, and virtual reality (VR) applications for rehabilitation. Clevenger’s most influential contribution is his 2012 paper, “Learning grasping force from demonstration,” which has garnered 58 citations. In this work, he introduced a novel force learning framework that enables robots to replicate human fingertip forces during grasping and manipulation tasks, using a force imaging approach that eliminates the need for sensors on fingertips or objects—a significant step toward more intuitive and safe robotic interaction. He also contributed to the development of “VR4VR,” a virtual reality system for vocational rehabilitation, designed to assess and train individuals with severe disabilities. This project, presented in 2015, demonstrates his commitment to translating cutting-edge technology into practical assistive tools. With a focus on bridging human skill transfer and robotic learning, Clevenger’s research has meaningful implications for both industrial automation and rehabilitation robotics.