Halil Cetin

Papers

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About

Halil Cetin’s research sits at the intersection of robotics, artificial intelligence, and human-robot collaboration, with a particular focus on enabling machines to learn from human demonstration. His most influential work centers on imitation learning and neural network-based control for robotic systems. In his highly cited 2014 paper, “Robot imitation of human arm via Artificial Neural Network” (12 citations), Cetin designed a system where potentiometers on a human arm captured joint movements, and an ANN classified these gestures to drive a robot arm—a foundational step in intuitive human-robot interaction. That same year, his work on “Path planning of mobile robots with Q-learning” (10 citations) demonstrated how reinforcement learning could enable a mobile robot to autonomously map its environment and navigate to a target via the shortest path, a key contribution to autonomous navigation. Building on these ideas, his 2016 study “Imitation and learning of human hand gesture tasks of the 3D printed robotic hand by using artificial neural networks” (8 citations) explored social learning and skill acquisition, showing how a 3D-printed robotic hand could be taught dexterous tasks through human demonstration. Cetin’s work is notable for its practical, low-cost approach to complex problems—using off-the-shelf sensors and 3D printing—making advanced robotics more accessible. His research has clear applications in assistive technology, prosthetics, and collaborative manufacturing, where robots must learn safely and intuitively from humans.