Papers
1
Total Citations
2
H-Index
1
About
Qihang Tang is a rising researcher at the forefront of human-robot interaction (HRI), with a specialized focus on leveraging augmented reality (AR) to enhance collaborative experiences. Their key research areas include AR-mediated HRI, dynamic interaction design, and human-centered robotics. Tang’s most notable contribution is the development of a “Dynamic Dual-Layer Interaction Adjustment” framework, which addresses a critical gap in AR-HRI: the discomfort users often feel when authoring tasks. By dynamically adjusting interaction layers based on user intent and task complexity, this work significantly improves psychological comfort and operational efficiency. Although early in their career, Tang’s 2024 paper has already garnered 2 citations, signaling growing interest in their innovative approach. This work builds on prior studies demonstrating AR’s potential to improve shared robot intent and visual feedback, but Tang’s contribution uniquely tackles the challenge of interaction selection—a key bottleneck in real-world AR-HRI applications. Their research promises to make human-robot collaboration more intuitive, comfortable, and creative, positioning Tang as a promising voice in next-generation HRI design.
Research Focus
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Top Papers
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