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About

Tao Xie is a robotics researcher whose work spans autonomous perception, robotic manipulation, and system dynamics — bridging foundational machine learning techniques with real-world robotic applications. His most recognized contribution, "Poly-MOT: A Polyhedral Framework For 3D Multi-Object Tracking" (2023), has garnered 52 citations and represents a significant advance in enabling mobile robots to perceive and track surrounding objects with greater precision by moving beyond single-metric data association approaches — a critical capability for intelligent navigation and motion planning. His earlier work demonstrates a sustained interest in robotic manipulators, including a wavelet network-based solution for inverse kinematics problems (2006) that applied neural architectures to multi-input, multi-output robotic systems. Xie has also contributed to hardware innovation, developing a linear driving actuator designed for robots operating in both terrestrial and deep-sea environments, reflecting his interest in ruggedized, real-world deployments. His most recent work on the NL-WCS algorithm advances data-driven dynamic modeling of serial robots by addressing non-linear friction — a persistent challenge in precision robotics. Across his career, Xie has consistently pursued the intersection of intelligent perception, mechanical design, and robot learning.