Yuqiao Chen

The University of Texas at Dallas, Zhuhai Institute of Advanced Technology

Papers

Total Citations

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About

Yuqiao Chen is a robotics researcher whose work bridges computer vision and medical robotics, with a focus on enabling machines to perceive and interact with the physical world. His key research areas include unseen object instance segmentation for robotic manipulation and continuum robotic systems for minimally invasive surgery. Chen’s most notable contribution is the Mean Shift Mask Transformer, a novel approach that integrates mean shift clustering with transformer architectures to segment unseen objects from images—a critical skill for robots to grasp and manipulate unfamiliar items. This work has garnered significant attention, with the 2024 version accumulating 14 citations, demonstrating its impact on the field of robotic perception. In parallel, Chen contributed to the design and validation of DESectBot, a two-segment decoupled continuum robotic system for Endoscopic Submucosal Dissection (ESD), a minimally invasive procedure for removing GI tract lesions. This work, with 3 citations, showcases his versatility in addressing real-world medical challenges. Chen’s research is characterized by its practical orientation, aiming to equip robots with the perception and dexterity needed for complex tasks, from warehouse automation to surgical assistance.