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About

Jessica Leu is a robotics researcher whose work sits at the intersection of motion planning, optimization, and human-robot collaboration. Her primary contributions lie in developing hybrid algorithms that fuse sampling-based planning with trajectory optimization to solve complex, long-horizon navigation problems in cluttered environments. Her most influential work, “Efficient Robot Motion Planning via Sampling and Optimization” (2021, 14 citations), established a foundational framework for combining the exploratory strengths of RRT* with the fine-tuning power of optimization, enabling robots to find collision-free, dynamically feasible paths more reliably. She extended this approach in her 2022 paper on RRT*-sOpt (6 citations), which specifically addresses long-horizon planning by segmenting trajectories for improved scalability. Beyond motion planning, Leu has advanced safe control for mobile manipulators through her hierarchical receding horizon control algorithm (HRHC, 2020, 3 citations) and tackled the growing challenge of human-robot collaboration with robust task planning for assembly lines (2022, 2 citations). Her work is characterized by a practical, systems-level approach that bridges theoretical planning algorithms with real-world deployment constraints, making her a rising voice in modern robotics.