Jennifer Wakulicz

Papers

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About

Jennifer Wakulicz is a rising star in robotics, whose research lies at the intersection of topological methods and motion planning. Her work addresses fundamental challenges in autonomous navigation and manipulation by leveraging algebraic topology to simplify complex, high-dimensional spaces. In her most-cited paper, "Topological Trajectory Prediction with Homotopy Classes" (2023, 6 citations), she introduced a novel framework that partitions the infinite space of possible trajectories into distinct homotopy classes, enabling more efficient and robust path planning in cluttered environments. This approach has significant implications for real-world autonomous systems, from self-driving cars to warehouse robots. Her second key contribution, "Multi-query Robotic Manipulator Task Sequencing with Gromov-Hausdorff Approximations" (2022, 4 citations), tackles the robot task sequencing problem (RTSP) by using Gromov-Hausdorff distances to approximate the cost of transitions between tasks. This work dramatically reduces the computational burden of planning sequences for multi-query manipulation, offering a scalable solution for industrial automation. Though early in her career, Wakulicz’s integration of topological data analysis with robotics has already garnered attention, positioning her as a leading voice in the next generation of intelligent motion planning.