Song Ci

Zhejiang University, Beijing Institute of Technology

Papers

Total Citations

H-Index

About

Song Ci is a rising researcher in the fields of human-robot collaboration, deep learning, and robotic manufacturing. His work focuses on enabling safer and more efficient interactions between humans and industrial robots, particularly in complex, real-world environments. A key contribution is his development of a deep learning-enabled visual-inertial fusion method for human pose estimation, which addresses the critical challenge of occlusion in human-robot collaborative assembly scenarios—a problem that has long hindered seamless cooperation on factory floors. This work has already garnered 15 citations, reflecting its immediate relevance. Ci has also made significant strides in optimizing robotic performance for precision tasks, as evidenced by his comprehensive review on serial robots for milling operations (14 citations), which synthesizes best practices to enhance accuracy and efficiency in automated manufacturing. His research sits at the intersection of computer vision, sensor fusion, and industrial robotics, offering practical solutions that push the boundaries of what collaborative systems can achieve. With a growing citation record and a focus on solving real-world bottlenecks, Song Ci is establishing himself as a promising voice in next-generation robotics and automation.