Yingkun Hou

Papers

Total Citations

H-Index

About

Yingkun Hou is a researcher focused on advancing safety and security in human-robot interaction, with a particular emphasis on anomaly detection. Their most-cited work, "Asymmetric Anomaly Detection for Human-Robot Interaction" (2021), addresses the critical challenge of rapidly identifying abnormal events that could pose dangers during collaborative human-robot tasks. Hou’s research explores the use of 2D and 3D convolutional autoencoders to enhance detection speed and accuracy, aiming to reduce the probability of hazardous incidents in real-time interactions. Although early in their career, with this paper garnering 3 citations, Hou’s contributions are foundational to developing more reliable and responsive robotic systems. Their work sits at the intersection of computer vision, machine learning, and robotics, offering practical solutions for safer human-robot collaboration in industrial, service, and domestic settings. As the field of human-robot interaction grows, Hou’s focus on asymmetric detection methods—where the robot and human have different roles in monitoring—provides a novel approach to preventing accidents. This research is particularly valuable for students and engineers designing autonomous systems that must operate safely alongside people.