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About

Gaurav Bhardwaj is a robotics researcher whose work focuses on the dynamic locomotion and control of bipedal robots, with a particular emphasis on stair climbing and descending. His major contributions lie in developing novel trajectory planning and control strategies for toe-foot bipedal robots, addressing the complex challenge of stable and efficient stair navigation. Bhardwaj’s most cited paper, "Neural network temporal quantized lagrange dynamics with cycloidal trajectory for a toe-foot bipedal robot to climb stairs" (2022, 9 citations), introduces a sophisticated neural network-based approach to optimize gait. He further advanced this area with "Fast Terminal Discrete-time Sliding Mode Control with Fuzzy-based Impedance Modulation for Toe Foot Bipedal Robot Going Upstairs" (2023, 5 citations), combining sliding mode control with fuzzy logic for enhanced stability. His work on "Planning Adaptive Brachistochrone and Circular Arc Hip Trajectory for a Toe-Foot Bipedal Robot going Downstairs" (2021, 4 citations) proposes a novel downstairs trajectory that leverages the brachistochrone curve—the fastest path between two points—for hip motion, a key insight for maintaining center-of-mass stability. Bhardwaj’s research also extends to manipulator inverse kinematics using unsupervised neural networks and autonomous car robotics, demonstrating a broad expertise in intelligent robotic systems.