Yasaman Mirmohammad
Papers
1
Total Citations
3
H-Index
1
About
Yasaman Mirmohammad is a robotics researcher whose work centers on humanoid robot motion planning and predictive control. Her most cited paper, "Ball Path Prediction for Humanoid Robots: Combination of k-NN Regression and Autoregression Methods" (2022), introduces a hybrid machine learning approach that fuses k-nearest neighbor regression with autoregressive modeling to enable humanoid robots to anticipate the trajectory of moving objects—a critical capability for real-time interaction and dynamic task execution. This contribution addresses a fundamental challenge in robotics: equipping humanoid platforms with the predictive intelligence needed to operate in unstructured, human-centric environments. Though early in her career, Mirmohammad’s work has already garnered attention, with her flagship paper accumulating 3 citations, signaling growing interest in her methodology. Her research sits at the intersection of machine learning, control theory, and humanoid locomotion, offering a practical framework for improving robot responsiveness. As the field pushes toward more autonomous and socially integrated robots, Mirmohammad’s predictive modeling techniques provide a foundation for safer, more adaptive human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1