Hong Yin

Papers

Total Citations

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About

Hong Yin is a rising scholar in the field of humanoid robotics, with a focused expertise in motion planning and kinematic control. His major contributions center on developing novel, bio-inspired control frameworks that enhance the precision and safety of humanoid upper-body robots. Notably, Yin pioneered the application of high-order differential estimation to motion planning by formulating it as a problem of solving time-varying linear equations, a method that avoids complex calculations and improves real-time performance. His work on the "Integration-Enhanced Differentiator-Based Method" has already garnered 11 citations since 2024, signaling strong early impact. In 2025, he introduced the concept of a "Virtual Flexible Joint Dynamics Primitive" (VFJDP), inspired by human arm dynamics, and integrated it with a quasi-sliding mode observer to achieve more natural and robust kinematic control. This innovative approach, detailed in a paper with 4 citations, represents a significant step toward safer human-robot interaction. Through these achievements, Hong Yin is establishing himself as a key contributor to advancing the dexterity and intelligence of next-generation humanoid systems.