Guanqun Cao
Papers
14
Total Citations
288
H-Index
8
About
Guanqun Cao is a robotics and artificial intelligence researcher whose work sits at the intersection of tactile sensing, transparent object perception, and multimodal robotic manipulation. His research has made significant contributions to two complementary frontiers: enabling robots to perceive and manipulate notoriously difficult transparent objects, and advancing tactile sensing for nuanced surface and texture recognition. Cao's highly cited review on robotic perception of transparent objects (55 citations) has become an essential reference for the field, while his practical innovations — including the A4T hierarchical affordance detection framework (42 citations) and vision-guided tactile poking strategies (36 citations) — demonstrate how combining visual and tactile modalities can overcome the fundamental challenges of light refraction and reflection. His TouchRoller sensor (36 citations) represents a notable hardware contribution, enabling rapid large-area tactile assessment. On the sensing side, his spatio-temporal attention model for tactile texture recognition (49 citations) and subsequent work on multimodal zero-shot learning push robots toward recognizing materials they have never encountered before. Collectively accumulating over 270 citations, Cao's body of work reflects a coherent vision: building robots that perceive the physical world with human-like richness through tightly integrated vision and touch.
Research Focus
Key Achievements
Top Papers
- 1Robotic Perception of Transparent Objects: A Review55 citations · 2023
- 2Spatio-temporal Attention Model for Tactile Texture Recognition49 citations · 2020
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- 6Multimodal zero-shot learning for tactile texture recognition23 citations · 2024
- 7Vis2Hap: Vision-based Haptic Rendering by Cross-modal Generation16 citations · 2023
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- 10Multimodal perception for dexterous manipulation5 citations · 2022