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About

Yaoyu Sui is a robotics researcher whose work is driving advances in autonomous navigation and trajectory tracking for mobile, aerial, and legged robots. Sui’s core contributions lie at the intersection of control theory, reinforcement learning, and real-time perception, tackling the fundamental challenge of enabling robots to move with precision and intelligence in complex, often unmapped environments. A key achievement is the development of a novel trajectory tracking method for fully electrically driven quadruped robots, which significantly improves the accuracy of trunk center-of-mass and foot-end trajectory control—a critical step for dynamic locomotion. Sui has also pioneered the RPEOD system, a real-time pose estimation and object detection framework for aerial robots, enabling robust target tracking using binocular fisheye cameras. Further expanding the frontier of autonomy, Sui introduced a self-adaptive double Q-backstepping approach that leverages reinforcement learning to overcome the limitations of traditional backstepping methods for indoor mobile robot inspection tasks. With over 50 citations across these works, Sui’s research is gaining traction for its practical impact. Notably, the work on learning autonomous navigation in mapless, unknown environments—relying only on low-precision sensors—addresses a critical gap in field robotics, promising more resilient and adaptable autonomous systems for real-world deployment.