Yining Zhou

Papers

Total Citations

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About

Yining Zhou is a robotics researcher whose work centers on the control and stability of semi-passive and passive walking robots. Her major contributions lie at the intersection of machine learning and bipedal locomotion, where she has pioneered the use of adaptive algorithms to achieve human-like, energy-efficient gait. In her most cited work, "Control of stability in semi-passive robot based on RBF neural network" (2023, 11 citations), she demonstrated how radial basis function networks can dynamically stabilize walking without heavy computational overhead. Building on this, she introduced a modified Q-learning algorithm for walking control (2024, 5 citations), enabling robots to learn and adapt their gait in real time. Her stability analysis for passive robots walking on inclined surfaces (2024, 3 citations) addresses a fundamental challenge: achieving consecutive, human-like gait on varying terrain without the stiff-joint control that typically incurs high computational costs. Zhou’s research is notable for its practical focus on reducing energy consumption and control complexity, making passive-dynamic principles more viable for real-world applications. Her work is increasingly cited in the fields of bio-inspired robotics and reinforcement learning for locomotion, marking her as an emerging voice in efficient robotic design.