Mitra Ghergherehchi
Papers
4
Total Citations
12
H-Index
2
About
Mitra Ghergherehchi is a rising researcher at the forefront of autonomous robotics, specializing in deep reinforcement learning (DRL) and transformer-based architectures for robot navigation and control. Her work addresses critical challenges in partially observable and dynamic environments, where sensor limitations, occlusions, and unpredictable human behavior complicate safe, collision-free movement. Ghergherehchi’s most-cited paper, “Memory-driven deep-reinforcement learning for autonomous robot navigation in partially observable environments” (2025, 5 citations), introduces a novel spatial-memory DRL algorithm that enables service robots to navigate with robust safety in human-centric settings. She further extends her contributions to multi-arm systems with “Transformer-based path planning for single-arm and dual-arm robots” (3 citations) and tackles environmental disturbances in “Transformer-based aerial robot tracking system in environments with wind disturbances” (2 citations). Though early in her career, her work has already garnered attention for its practical impact on real-world robot autonomy. Ghergherehchi’s research bridges cutting-edge AI with tangible robotic applications, positioning her as a promising innovator in the field of intelligent, adaptive robotics.
Research Focus
Key Achievements
Top Papers
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