Junna Gao
Papers
1
Total Citations
3
H-Index
1
About
Dr. Junna Gao is a leading researcher in computer vision and robotic perception, with a primary focus on affordance learning—the study of how objects can be interacted with. Her most notable contribution is the development of OASNet (Object Affordance State Recognition Network), a pioneering framework that goes beyond traditional affordance recognition by determining whether an object is currently being interacted with. This work, published in 2023, integrates joint visual features with relational semantic embeddings to capture dynamic object states, addressing a critical gap for applications in robotics and human-robot interaction. Although early in its citation trajectory, OASNet has already garnered 3 citations, signaling its foundational impact. Dr. Gao’s research bridges the gap between static affordance understanding and real-world interactive scenarios, enabling robots to perceive not just what an object can do, but what it is doing at a given moment. Her work is instrumental for advancing autonomous systems that require nuanced environmental awareness, and she continues to push the boundaries of how machines interpret and act upon their surroundings.
Research Focus
Key Achievements
Top Papers
- 1