Zhikai Yang

Shanghai Jiao Tong University, Yanshan University

Papers

Total Citations

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About

Zhikai Yang is a researcher at the intersection of medical robotics and artificial intelligence, with a focus on enhancing the precision and autonomy of robotic systems in clinical settings. His primary research areas include medical robot control, human-robot interaction, and motion compensation for image-guided interventions. Yang’s most cited work, a 2021 pilot study on target localization during respiratory motion, introduces a Long Short-Term Memory (LSTM) network to predict internal target displacement for robotic puncture systems—a critical contribution to improving biopsy accuracy. With 10 citations, this paper addresses the challenge of respiratory-induced soft tissue motion, offering a data-driven correlation model that enhances real-time localization. In another notable study (4 citations), Yang applies Deep Q-Networks (DQN) to extract executable action sequences from natural language instructions, enabling more intuitive human-robot collaboration for medical service robots. This work advances the design of stable, simple interaction systems that allow doctors to command robots verbally. Together, Yang’s contributions bridge deep learning and robotics, tackling real-world clinical challenges in precision and usability, and laying groundwork for safer, more responsive medical automation.