Yanhao Yang

Oregon State University, Carnegie Mellon University

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Total Citations

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About

Yanhao Yang is a leading researcher in legged robotics and optimal control, whose work bridges the gap between theoretical guarantees and real-world autonomy. His primary research areas include proprioceptive locomotion, adaptive model predictive control (MPC), and extreme-terrain traversal for quadruped robots. Yang’s major contributions are twofold: first, he demonstrated that integrating tail dynamics with proprioceptive feedback enables quadruped robots to robustly navigate unpredictable environments like rocky hills and curbs, achieving 11 citations for his 2023 work on this topic. Second, he developed Adaptive Complexity MPC, a formulation that dynamically adjusts model complexity while preserving feasibility and stability—a breakthrough that earned 9 citations in 2024. This approach overcomes the traditional trade-off between computational efficiency and control fidelity. Yang’s impact is evident in his citation counts, which, though early in his career, reflect growing recognition from the robotics and controls communities. Notably, his work on tail-assisted locomotion was featured in high-impact venues, and his MPC framework has been adopted for real-time applications in agile robotics. For students and researchers, Yang’s research exemplifies how principled control theory can unlock practical, robust performance in challenging physical domains.