Jingkun Wang

Texas A&M University, Shandong University

Papers

Total Citations

156

H-Index

About

Dr. Jingkun Wang is a leading voice in human-robot interaction, with a primary focus on the critical human factors that determine the success of collaborative robotics. Her landmark 2022 systematic review, garnering 152 citations, established a foundational framework for understanding how operator state and perception mediate the effectiveness of robot behaviors and autonomy levels. This work has become essential reading for researchers designing safe, intuitive shared spaces where humans and robots work side-by-side. Demonstrating a deep technical range, Dr. Wang’s earlier research explored sensor fusion for autonomous navigation, applying neural networks to integrate GPS and dead reckoning data for cost-effective positioning solutions. This dual expertise—bridging the technical mechanics of robotics with the nuanced psychology of human-robot teams—positions her as a pivotal figure in the field. Her contributions are not merely academic; they provide actionable metrics and design principles that directly inform the development of more responsive, trustworthy, and efficient collaborative systems for manufacturing, healthcare, and service robotics.