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About

Francisco J. Naranjo-Campos is a rising researcher in robotics, whose work focuses on solving fundamental challenges in robotic manipulation through the integration of deep learning and reinforcement learning. His primary research areas include inverse kinematics (IK), motion planning, and control for robotic manipulators, with a particular emphasis on developing intelligent systems that can operate effectively in complex, obstacle-filled environments. Naranjo-Campos’s most significant contribution is his comprehensive review on using deep neural networks for IK, control, and planning, which has already garnered 22 citations since its publication in 2025, establishing him as a key voice in this rapidly evolving field. He has also pioneered the application of Inverse Reinforcement Learning (IRL) to infer reward functions from expert demonstrations, enabling robots to learn robust manipulation skills more efficiently. Beyond theoretical advances, his practical work includes a novel method for bottle opening using a dual-arm robot, designed to assist individuals with injuries or disabilities—showcasing his commitment to accessible robotics. With a growing citation record and a clear trajectory toward impactful, human-centered automation, Naranjo-Campos is a researcher to watch.