Sahar Leisiazar

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About

Sahar Leisiazar is a robotics researcher specializing in autonomous navigation and human-robot interaction, with a focus on developing intelligent systems for dynamic, real-world environments. Her work centers on three key areas: obstacle and occlusion avoidance in robotic follow-ahead applications, multi-floor mapping using SLAM algorithms, and adaptive human-following strategies. Leisiazar’s most impactful contribution is her 2023 paper on an MCTS-DRL based methodology for robotic follow-ahead, which has garnered 9 citations and addresses the critical challenge of navigating robots safely around obstacles and occlusions in cluttered settings. She also pioneered real-time mapping of multi-floor buildings using elevators (2022, 5 citations), overcoming limitations in traditional SLAM algorithms for elevation detection and reflective environments. Her 2025 work on adapting to frequent human direction changes (4 citations) introduces a novel approach that considers multiple potential human trajectories rather than relying on a single prediction, enhancing robot responsiveness in highly variable human behavior scenarios. Leisiazar’s research bridges reinforcement learning, path planning, and perception, offering practical solutions for service robots in crowded, multi-level spaces. Her work is particularly notable for its focus on real-time adaptability, making her a rising figure in autonomous navigation and human-robot collaboration.