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About

Yanzi Miao is a robotics researcher whose work spans soft robotics, visual servoing, autonomous navigation, and 3D perception — areas that collectively push the boundaries of intelligent robotic systems in complex, real-world environments. Among Miao's most influential contributions is pioneering research in cable-driven soft robot manipulators, including shape-feature-based visual servoing (69 citations) and underwater dynamic modeling (58 citations), establishing a foundational framework for controlling highly compliant robotic structures that interact safely with humans and unstructured surroundings. Miao has also made significant strides in flexible-link manipulator control, developing vibration suppression techniques through visual feedback without requiring direct deformation measurements (39 citations). Beyond manipulation, Miao's research extends to mobile robot autonomy, with notable work in graph relational reinforcement learning for crowd navigation (36 citations) and 3D scene flow estimation using generative adversarial networks. Earlier contributions to multi-robot odor source localization using swarm optimization algorithms reflect a longstanding interest in collective robotic intelligence. More recently, Miao has explored sim-to-real transfer via 3D Gaussian Splatting, signaling engagement with cutting-edge representation learning for robotic manipulation. Together, these contributions demonstrate a career dedicated to bridging theoretical modeling with practical, deployable robotic intelligence.