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About

Shiqiu Gong’s research lies at the intersection of human-robot interaction, motion planning, and human motor control, with a specific focus on enabling anthropomorphic robot arms to move in more natural, human-like ways. Gong’s major contributions center on the development of the Human Arm Movement Primitive (HAMP) framework, which captures the intrinsic mechanisms of human arm motion to guide robot planning. In their most-cited work, “Task motion planning for anthropomorphic arms based on human arm movement primitives” (13 citations), Gong established a task planning method that minimizes task cost while producing human-like trajectories. Building on this, Gong introduced the HAMP chain concept for generating motion patterns that are human-like not only in configuration but also in timing and coordination. To enhance safety in shared environments, Gong developed a modified minimum jerk model for predicting human arm motion, addressing three fundamental challenges in real-time human-robot interaction. Their high-precision MMJM model further refined predictions of reaching motions. With a total of 26 citations across these four papers, Gong’s work is steadily gaining recognition for its practical impact on making collaborative robots safer and more intuitive partners in industrial and assistive settings.