Rate My Professor Fares Abu-Dakka

Abu-Dakka's research centers on advancing robotic manipulation through the integration of control theory, differential geometry, and machine learning to enhance performance, adaptability, and safety. His work develops algorithms enabling robots to learn skills via demonstration, reinforcement learning, and adaptive control, with emphasis on geometric control and physical human–robot interaction. He has published extensively in top robotics forums, including “Dynamic Movement Primitives in Robotics: A Tutorial Survey” (International Journal of Robotics Research, 2023), “Learning Stable Robotic Skills on Riemannian Manifolds” (Robotics and Autonomous Systems, 2023), “CITR: A Coordinate-Invariant Task Representation for Robotic Manipulation” (IEEE ICRA 2024), “Safe Execution of Learned Orientation Skills with Conic Control Barrier Functions” (IEEE ICRA 2024), “MeshDMP: Motion Planning on Discrete Manifolds using Dynamic Movement Primitives” (IEEE ICRA 2025), and “Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview” (Information Fusion, 2024). As an Associate Editor for IEEE Robotics and Automation Letters and IEEE Transactions on Robotics, he contributes to the field’s editorial standards. Abu-Dakka is an IEEE Senior Member since 2024 and a member of the European Laboratory for Learning and Intelligent Systems (ELLIS) Society since 2024.