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About

Minhong Wan is a robotics researcher whose work bridges human-robot interaction, motion planning, and computer vision. Her research focuses on enabling robots to understand and replicate human movements with high fidelity, particularly in complex, real-world scenarios. A key contribution is her work on frame-by-frame motion retargeting with self-collision avoidance, which addresses the challenging nonlinear problem of transferring diverse human demonstrations to robots with different kinematic configurations. She has also advanced multi-person tracking for service robots, notably developing the TGRMPT system and a large-scale dataset specifically designed for tour-guide robots, ensuring safe and polite navigation in crowded spaces. In the domain of dexterous manipulation, Wan has pioneered hierarchical optimal motion planning for piano-playing robots, tackling efficient trajectory generation for multi-degree-of-freedom fingers. Her work extends to 3D human pose estimation through the MSMB-GCN framework, which uses multi-scale graph convolutional networks to enhance robot perception in dynamic environments. With publications spanning 2022 to 2024, Wan’s research is laying foundational groundwork for more capable, socially-aware robots that can operate alongside humans in entertainment, service, and collaborative settings.