Yufan Zhou

University of Ottawa, Centre Inria de l'Université de Lille

Papers

Total Citations

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About

Yufan Zhou is a researcher at the intersection of computer vision and autonomous robotics, with key contributions in deep learning-based prediction and robot motion control. His most cited work, "Deep Learning in Next-Frame Prediction: A Benchmark Review" (2020, 74 citations), provides a comprehensive analysis of next-frame prediction as an unsupervised representation learning problem—a promising direction for enabling robots to anticipate future visual scenes from historical data. This work has significant implications for robot decision-making and autonomous navigation. More recently, Zhou has advanced the field of autonomous mobile robotics with "Inverse Kinematics on Guiding Vector Fields for Robot Path Following" (2025), where he innovatively extends classical inverse kinematics—traditionally applied to robotic arm end-effectors—to guiding vector fields for path-following control. This novel approach allows mobile robots to follow complex, implicitly defined paths with greater precision. Zhou’s research bridges the gap between high-level visual prediction and low-level motion control, making him a notable figure in the development of more intelligent and autonomous robotic systems.