Xun Shen

The Graduate University for Advanced Studies, SOKENDAI, Jiangnan University

Papers

Total Citations

H-Index

About

Xun Shen is a researcher at the forefront of human-robot interaction (HRI) and artificial intelligence, with a primary focus on developing emotionally intelligent robotic systems. His key research areas include multimodal emotion recognition, attention-based deep learning architectures, and reinforcement learning for autonomous agents. Shen’s most significant contribution is his pioneering work on attention-based multimodal fusion for estimating human emotion in real-world HRI, a 2020 paper that has garnered 12 citations and addresses the critical challenge of enabling robots to perceive and respond to human affective states. This approach bridges the gap between traditional early fusion methods and more sophisticated attention mechanisms, advancing the goal of empathetic, harmonious human-robot collaboration. Earlier in his career, Shen explored reinforcement learning in competitive environments, as demonstrated by his 2011 study on RoboCup vanguard goal-scoring ability using Q-learning. While this work has received 2 citations, it showcases his foundational interest in autonomous decision-making and adaptive behavior. Shen’s research is particularly notable for its real-world applicability, aiming to make HRI more natural and intuitive. His work continues to inspire students and researchers seeking to integrate emotional intelligence into robotic systems, with potential applications in healthcare, education, and service robotics.