Nicholas Paperno
University of Central Florida, United States Patent and Trademark Office
Papers
4
Total Citations
69
H-Index
4
About
Nicholas Paperno’s research lies at the intersection of assistive robotics, human-robot interaction, and adaptive control, with a focus on making robotic manipulators more accessible and intuitive for diverse users. His most impactful work, “System Design and Implementation of UCF-MANUS—An Intelligent Assistive Robotic Manipulator” (30 citations), details the integration of sensory, computational, and multimodal interfaces to create a smart assistive system for individuals with disabilities. This foundational contribution established a framework for end-to-end robotic assistance. Paperno further advanced the field with “An Adaptive Control-Based Approach for 1-Click Gripping of Novel Objects” (19 citations), where he developed an intelligent algorithm enabling robots to grasp unfamiliar objects without prior geometric models—a significant leap toward practical, user-friendly manipulation. His human factors research, including “A Predictive Model for Use of an Assistive Robotic Manipulator” (13 citations), systematically identifies individual differences—such as dexterity—that predict user performance, while “Age and Gender Differences in Performance for Operating a Robotic Manipulator” (7 citations) explores how these factors affect interaction across populations. Collectively, Paperno’s work bridges engineering and human-centered design, demonstrating how adaptive control and user modeling can democratize access to robotic assistance.
Research Focus
Key Achievements
Top Papers
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