Xingchen Liu

Xi'an University of Science and Technology, Chinese University of Hong Kong, Shenzhen, King University

Papers

Total Citations

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About

Xingchen Liu is a rising researcher at the forefront of embodied AI and surgical robotics, whose work bridges the gap between digital intelligence and physical action. His primary research areas encompass robotic manipulation, task-oriented video generation, and autonomous navigation, with a particular focus on enabling robots to learn complex, real-world interactions. Liu’s major contribution is the development of **TASTE-Rob**, a pioneering framework that advances video generation for task-oriented hand-object interactions. By addressing critical limitations in existing datasets—such as inconsistent viewpoints and incomplete action sequences—this work provides a scalable method for generating high-quality video demonstrations, directly powering generalizable robotic imitation learning. This innovation holds significant promise for reducing the data bottleneck in training robots for everyday tasks. In the medical domain, Liu has also made notable strides with his research on robot-assisted navigation for mandibular reconstruction surgery, tackling the dual challenge of restoring both facial form and physiological function with high precision. While his most-cited papers are recent (2025), each has already garnered 1 citation, signaling early impact in a competitive field. Liu’s work stands out for its practical orientation, aiming to make robotic systems more adaptable, precise, and useful in both industrial and clinical settings.