Gbenga Abiodun Odesanmi

Papers

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About

Gbenga Abiodun Odesanmi is a robotics researcher whose work centers on advancing human–robot interaction and dexterous manipulation. His primary contributions lie in developing control frameworks that make robots more capable of learning and executing complex tasks alongside humans. His most cited work, the 2022 paper "Skill learning framework for human–robot interaction and manipulation tasks," has garnered 36 citations, reflecting its significance in the field. In this research, Odesanmi addresses the challenge of enabling robots to acquire new manipulation skills through interaction, bridging the gap between theoretical control and practical application. He has also tackled computational efficiency in robotic control, as seen in his 2019 paper on "Task Space Robotic Manipulation Based on Revised Virtual Decomposition Plus PD Control," which proposes a subsystem dynamics approach to reduce the heavy computational load of traditional Lagrange-based methods. By addressing the complexity and unknowns of robotic joint parameters, Odesanmi’s work offers more practical, real-time solutions for robot control. His research is particularly relevant for students and engineers interested in the intersection of control theory, machine learning, and human-centered robotics, where his frameworks promise safer and more intuitive human–robot collaboration.