Yui Iioka
Papers
1
Total Citations
5
H-Index
1
About
Yui Iioka is a researcher advancing the field of human-safe robotics, with a particular focus on physical property estimation for human-to-robot handovers. Her key research areas include transformer-based machine learning models, 3D model estimation, and safe human-robot interaction. Iioka's most notable contribution is the development of a shared Transformer encoder architecture that simultaneously estimates container filling levels and types using mask-based 3D model estimation. This work, published in 2022, has garnered 5 citations and addresses the critical challenge of enabling robots to safely handle containers during handovers by accurately inferring physical properties like weight distribution and contents. Her approach stands out for its parameter-sharing design, which improves efficiency and generalization across different container types. By tackling the complex task of estimating both filling level and material type from visual data, Iioka's research directly contributes to making robot assistants more perceptive and safer in real-world human environments. Her work represents an important step toward intuitive and reliable human-robot collaboration, particularly in domestic and industrial settings where object handovers are common.
Research Focus
Key Achievements
Top Papers
- 1