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About

Zhaoyuan Shi is a robotics researcher whose work centers on humanoid robot perception, manipulation, and multi-robot coordination. Shi’s primary contributions lie in integrating advanced deep learning with classical robotic control, particularly for the NAO platform. A standout achievement is the development of a monocular vision system that combines the YOLOv8 network with geometric ranging to dramatically improve target localization and grasping accuracy—a method that addresses the persistent challenge of error magnification at longer distances. This work, published in 2023, has already garnered 19 citations, signaling its impact on practical visual servoing. Shi has also advanced the frontier of collaborative robotics by designing a distributed model predictive controller for dual humanoid robots, enabling coordinated transport tasks through a leader-follower architecture. This 2024 paper, with 10 citations, showcases a novel approach to flexible, human-like teamwork in complex environments. Earlier work on NAO-based navigation path optimization further demonstrates a sustained focus on closing the loop between perception and action. Through these contributions, Shi is helping to build more capable, autonomous humanoid systems that can see, grasp, and work together—pushing the boundaries of what small-scale robots can achieve in real-world tasks.