Papers
3
Total Citations
96
H-Index
2
About
David Lieb is a researcher whose work lies at the intersection of computer vision, self-supervised learning, and autonomous mobile robotics. His most significant contributions center on optical flow techniques for robot navigation, particularly through his highly cited 2007 paper *"Reverse Optical Flow for Self-Supervised Adaptive Autonomous Robot Navigation,"* which has garnered 84 citations. This work pioneered methods enabling robots to learn from their own motion and visual feedback without requiring labeled training data—a foundational concept in self-supervised learning for robotics. Lieb’s research demonstrated how optical flow could be reversed and adapted to allow mobile robots to navigate unfamiliar environments autonomously, reducing reliance on pre-mapped or structured settings. His second most-cited work, *"Optical Flow Approaches for Self-supervised Learning in Autonomous Mobile Robot Navigation"* (11 citations), further explored these adaptive navigation strategies. More recently, Lieb has turned his attention to the integration of complex robot systems, co-authoring a 2025 paper on model-driven approaches for Robot Operating System (ROS)-based systems, addressing the critical challenge of tool obsolescence in a rapidly evolving field. His work continues to influence both autonomous navigation and the systematic development of next-generation robotic platforms.
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