Amina Keldibek

Papers

Total Citations

H-Index

About

Amina Keldibek is a pioneering researcher at the intersection of robotics and computational neuroscience, whose work focuses on developing bio-inspired control systems for humanoid robots. Her primary research areas include neuromorphic control architectures, central pattern generation (CPG) circuits, and chaotic neural networks for robotic locomotion. Keldibek’s major contributions center on proposing Chaotic Neural Networks (CNN) as a novel alternative to traditional CPG models for walking robots. Her most cited work, “A neuromorphic control architecture for a biped robot” (2019, 10 citations), introduces a modular control system capable of learning and autonomously reproducing complex periodic trajectories. In her 2016 paper (3 citations), she developed a new Matlab implementation of CNN, demonstrating their computational and functional advantages for generating rhythmic movements in humanoid robots. Her 2017 study (2 citations) further advanced the field by applying neuromorphic control to a lightweight, 3D-printed biped robot. While her citation counts remain modest, Keldibek’s innovative integration of chaotic dynamics with robotic control systems represents a promising direction for more adaptive and energy-efficient humanoid locomotion. Her work is particularly notable for bridging theoretical neuroscience with practical robotic applications.