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About

Hann Woei Ho is a robotics and autonomous systems researcher whose work bridges computer vision, control theory, and machine learning to advance the capabilities of autonomous aerial and ground vehicles. His research spans optical flow-based navigation, nonlinear control for micro air vehicles (MAVs), and deep reinforcement learning for mobile robot navigation. Ho's early contributions focused on vision-based autonomy for MAVs, most notably developing an optic-flow algorithm for slope estimation to enable robust autonomous landing — work that has accumulated 39 citations and remains a foundational reference in the field. His 2021 paper on extended incremental nonlinear dynamic inversion extended these ideas into a sophisticated control framework, earning 24 citations. He also pioneered a self-supervised learning approach using optical flow for obstacle detection, reducing reliance on labeled data in robotic perception. More recently, Ho has turned his expertise toward deep reinforcement learning, producing influential work on hierarchical reinforcement learning for non-stationary environments and advanced deep Q-network architectures for real-time mobile robot navigation. Across these contributions, his research consistently addresses practical challenges — uncertainty, dynamic environments, and sensor noise — making it highly relevant to both academic and applied robotics communities.