Papers
2
Total Citations
17
H-Index
2
About
Xinyu Cao is a rising researcher at the intersection of artificial intelligence, human–machine interaction, and 3D vision. Her work focuses on enabling intuitive, intelligent interfaces that bridge the physical and digital worlds. She has made significant contributions to self-powered sensing systems for human–machine interfaces (HMIs), exemplified by her highly cited 2024 review on piezoelectric and triboelectric sensors, which has already garnered 12 citations for its comprehensive synthesis of this rapidly advancing field. In parallel, Cao has pioneered novel approaches to 3D part mobility parsing, introducing P^3-Net, a method that learns explicit point correspondence from raw point cloud sequences to understand articulated objects. This work, published in 2022 with 5 citations, addresses a fundamental challenge in robotic perception and embodied AI. By combining expertise in sensor design and geometric deep learning, Cao is shaping the future of how machines perceive and interact with both humans and their environments. Her research holds promise for applications ranging from smart wearables to autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2