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About

Shenli Yuan is a robotics and human-computer interaction researcher whose work spans dexterous robotic manipulation, tactile sensing, and interactive shape displays. Yuan is perhaps best known for the shapeShift project, a compact, high-resolution tabletop shape display enabling novel 2D spatial and self-actuated interactions, which has garnered over 130 citations and demonstrated significant influence in the HCI community. A central thread of Yuan's research is the development of the Roller Grasper family — a series of non-anthropomorphic robot hands employing actively driven rolling fingertips to achieve sophisticated in-hand object manipulation. This iterative body of work, spanning Roller Grasper V2 through V3 and culminating in a tactile-reactive variant, collectively represents a meaningful advance in robotic dexterity, accumulating over 130 citations across its versions. Yuan has also contributed to compliant robotic hands with active surfaces (BACH) and stretchable tactile sensing skins for navigation in cluttered environments. More recently, Yuan has explored cross-embodiment imitation learning through the LEGATO framework. With a research portfolio exceeding 330 total citations, Yuan's work consistently bridges mechanical design, sensing, and control to push the boundaries of robotic and interactive systems.