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About

Yunan Wang is a robotics researcher whose work bridges additive manufacturing and intelligent manipulation, with a focus on precision, autonomy, and physical interaction. Wang’s key research areas include robotic additive manufacturing, minimally invasive 3D printing, and learning-based robot manipulation. In their most cited work, “Optimization-based non-equidistant toolpath planning for robotic additive manufacturing with non-underfill orientation” (2023, 17 citations), Wang introduced a novel approach to reduce material waste and improve structural integrity in 3D printing. Another notable contribution is “A closed-loop minimally invasive 3D printing strategy with robust trocar identification and adaptive alignment” (2023, 9 citations), which advances surgical applications of additive manufacturing. Wang has also made significant strides in robot manipulation through object-centric learning. Their paper “Dynamics Learning With Object-Centric Interaction Networks for Robot Manipulation” (2021, 9 citations) presents a predictive dynamics model that enables robots to understand and plan actions for multi-object tasks, while “Learning Latent Object-Centric Representations for Visual-Based Robot Manipulation” (2022, 4 citations) tackles the challenge of predicting future object states from raw images. With a growing citation record and work spanning both manufacturing and manipulation, Yunan Wang is shaping the future of autonomous, intelligent robotic systems.