Xiaotong Hu
Papers
1
Total Citations
24
H-Index
1
About
Xiaotong Hu is a leading researcher in tactile sensing, soft robotics, and human-machine interaction, with a focus on capturing complex physical interactions through multimodal sensing and deep learning. Their most-cited work, "Capturing forceful interaction with deformable objects using a deep learning-powered stretchable tactile array" (2024), introduces a novel visual-tactile system that reconstructs full hand-object states—including occluded object deformations—during manipulation. This breakthrough addresses a critical challenge in replicating realistic interactions for virtual reality, telemedicine, and robotics. By integrating a stretchable tactile array with deep learning, Hu’s approach enables precise force and geometry capture, achieving 24 citations in a short time and demonstrating significant early impact. Their contributions advance the understanding of deformable object manipulation, bridging the gap between tactile sensing and real-world applications. Hu’s work is notable for its interdisciplinary approach, combining materials science, computer vision, and machine learning, and holds promise for enhancing robotic dexterity and immersive virtual environments. As an emerging scholar, Hu’s research is poised to influence future developments in soft robotics and human-centered technologies.
Research Focus
Key Achievements
Top Papers
- 1