Apoorva Parashar
Maharshi Dayanand University, Indian Institute of Management Rohtak
Papers
2
Total Citations
17
H-Index
2
About
Apoorva Parashar’s research lies at the intersection of humanoid robotics, dynamic balance control, and machine learning. Her most cited work focuses on a critical challenge in bipedal locomotion: push recovery in dynamic environments. Parashar developed a control system that enables humanoid robots to maintain balance after external disturbances—a fundamental ability for real-world deployment. By integrating push recovery strategies with K-Means clustering, she pioneered a method to classify and adapt to varying perturbation patterns, enhancing robot robustness. Her contributions, reflected in papers accumulating over 17 citations, address the core problem of bipedal stability, bridging control theory and unsupervised learning. This work is notable for its practical approach to making humanoid robots safer and more reliable in unpredictable settings, such as disaster response or human interaction. Parashar’s research has been recognized for its potential to advance autonomous locomotion, and she continues to explore how data-driven techniques can refine robot reflexes. For students and researchers, her work offers a clear example of how classical control and modern AI can converge to solve real-world robotics challenges.
Research Focus
Key Achievements
Top Papers
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- 2