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About

Lauren Lieu is a roboticist whose work bridges the gap between theoretical control and real-world deployment, particularly in challenging environments. Her primary research areas include nonlinear model predictive control (MPC), multi-robot systems, and autonomous underwater mapping. Her most significant contribution is the development of a robust-adaptive nonlinear MPC technique that leverages past experiences to achieve tractability on computationally constrained systems, a method detailed in her most-cited paper (14 citations). This work extends the Experience-driven Predictive Control (EPC) algorithm, enabling robots to handle time-varying state uncertainty with unprecedented efficiency. Lieu has also pioneered cooperative control across geographically distributed testbeds, demonstrating robot swarming over the Internet (6 citations), a feat that required solving communication latency and coordination challenges across different time zones. In underwater robotics, she developed a novel method for three-dimensional mapping of cisterns and wells without relying on odometry (4 citations), using dual scanning sonars and a compass for offline SLAM. Her work is notable for its practical focus on computationally constrained systems, making advanced algorithms accessible for real-world robots operating in remote or hazardous environments.