Nazerke Sandibay
Papers
1
Total Citations
9
H-Index
1
About
Nazerke Sandibay is a robotics researcher whose work bridges deep learning, optimal control, and nonlinear dynamics. Her most-cited paper, "Deep Learning-Based Approximate Optimal Control of a Reaction-Wheel-Actuated Spherical Inverted Pendulum" (2020, 9 citations), addresses a core challenge in variable impedance actuation: achieving safe, efficient physical interaction while overcoming the low motion bandwidth inherent in such systems. By integrating deep neural networks with approximate optimal control, she developed a framework that enables a reaction-wheel-actuated spherical inverted pendulum to stabilize and maneuver with enhanced dynamic adaptation—a critical step toward more responsive and safer robotic systems. This contribution has implications for human-robot collaboration, where safety and agility must coexist. Sandibay’s work demonstrates a talent for applying advanced computational methods to real-world control problems, earning recognition in the robotics community for its practical impact. Her research continues to explore how learning-based approaches can unlock the full potential of variable impedance actuators, making robots not only safer but also more capable in unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1