Qiao Yue

Papers

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About

Qiao Yue is a robotics researcher focused on advancing locomotion control for legged robots, particularly quadrupeds and hexapods. Their key research areas include model predictive control (MPC), trajectory optimization, and gait generation for multi-legged systems. Yue’s major contribution lies in addressing the computational bottleneck of full-body MPC for legged robots, proposing a high-frequency bounding gait control method for parallel quadruped robots that enables real-time, stable locomotion despite limited onboard computing power. This work, published in 2023, has garnered 2 citations and represents a practical step toward efficient, dynamic robot movement. Additionally, Yue developed an optimization-based locomotion planning framework for hexapod robots, published in 2022 with 1 citation, which reduces the complexity and design time of generating efficient, accurate motions through intelligent trajectory optimization. These contributions demonstrate Yue’s commitment to making legged robot control more computationally feasible and autonomous, bridging the gap between theoretical control methods and real-world robotic applications. Their work is particularly relevant for researchers and students interested in robot locomotion, control systems, and optimization in robotics.