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About

Anastasios Kyrillidis is a leading researcher in robot motion planning, with a focus on enabling high-degree-of-freedom (DOF) manipulators to operate safely and effectively under real-world uncertainty. His core contributions lie at the intersection of robust optimization, stochastic modeling, and task-level reasoning. In his highly cited 2021 work, Kyrillidis introduced a robust optimization-based framework for motion planning under sensing uncertainty, directly tackling the scalability challenge that has long hindered high-DOF robots in complex environments. He further advanced the field with his 2024 paper on stochastic implicit neural signed distance functions, a novel approach that provides rigorous safety guarantees for manipulators operating near humans. Beyond path planning, Kyrillidis has also made significant strides in task and motion planning (TAMP), developing methods for optimal grasp and placement selection in cluttered settings—a critical capability for real-world deployment. With over 18 citations across his most prominent works, his research is shaping the next generation of autonomous robots, bridging the gap between theoretical planning algorithms and practical, uncertainty-aware performance.