Gaurav Kumar Yadav
Papers
2
Total Citations
13
H-Index
2
About
Gaurav Kumar Yadav is a robotics researcher whose work focuses on bipedal locomotion and human motion prediction, aiming to enhance the stability and naturalness of walking robots. His most cited paper, "Generic Walking Trajectory Generation of Biped using Sinusoidal Function and Cubic Spline" (2020, 10 citations), addresses the fundamental challenge of maintaining balance in biped robots, which are inherently unstable due to their inverted pendulum dynamics. By combining sinusoidal functions with cubic spline interpolation, Yadav proposed a trajectory generation method that improves stability without relying solely on complex numerical solvers, offering a more robust approach to gait planning. More recently, in "Development of human motion prediction strategy using inception residual block" (2023, 3 citations), he has advanced into deep learning, employing inception residual blocks to forecast human movements—a critical capability for human-robot interaction and assistive robotics. Though his citation counts are modest, Yadav’s work bridges classical control theory with modern AI techniques, contributing to the practical realization of stable, adaptive bipedal systems. His research holds promise for applications in prosthetics, exoskeletons, and autonomous humanoid robots.
Research Focus
Key Achievements
Top Papers
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