Matthew Loper

Papers

Total Citations

104

H-Index

About

Matthew Loper is a pioneering researcher in human-robot interaction, with a focus on enabling robots to perceive and respond to human motion and non-verbal cues. His key research areas include mobile human-robot teaming, kinematic pose estimation, and action recognition from monocular vision. Loper’s major contributions center on integrating structured light-based depth sensing and dynamical motion vocabularies to bridge the gap between human performance and robot decision-making. His most-cited work, "Mobile human-robot teaming with environmental tolerance" (49 citations), demonstrates how emerging depth-imaging devices allow robots to interpret human movement for peer-to-peer interaction. In "Tracking human motion and actions for interactive robots" (30 citations) and "Interactive human pose and action recognition using dynamical motion primitives" (23 citations), he developed methods for real-time pose estimation and gesture recognition, advancing socially interactive robotics. Though his citation counts are modest, Loper’s work laid foundational techniques for using depth sensors and motion primitives in robotics, influencing later developments in autonomous systems and human-aware AI. His research remains relevant for students and engineers exploring intuitive, non-verbal human-robot collaboration.