Kejian Shi
Papers
3
Total Citations
50
H-Index
2
About
Kejian Shi is at the forefront of robotic manipulation and surgical automation, pioneering methods that bridge computer vision and embodied intelligence. His major contributions center on two transformative challenges: enabling robots to grasp unfamiliar objects with minimal sensory data, and endowing surgical robots with generalized task autonomy. In his landmark work, *RGBGrasp* (2024, 24 citations), Shi introduced an innovative approach that captures multiple RGB views during a robot arm’s natural movement, leveraging Neural Radiance Fields to reconstruct 3D geometry without expensive point-cloud cameras. This breakthrough significantly reduces hardware requirements while improving grasp success on diverse shapes and materials. Equally impactful is his research on surgical embodied intelligence (2025, 24 citations), where he developed frameworks for laparoscopic robots to autonomously perform a wide range of surgical tasks across varying clinical environments—moving beyond rigid, context-specific automation toward true generalizability. By tackling the core limitations of data dependency and task specificity, Shi’s work has already garnered attention for its practical implications in manufacturing and healthcare. His achievements mark him as a rising leader in creating more adaptable, intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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