Yuki Toyosaka
Papers
1
Total Citations
3
H-Index
1
About
Yuki Toyosaka’s research lies at the intersection of computer vision, knowledge representation, and human-activity understanding, with a particular focus on how eye gaze can bridge the gap between perception and action. Their most cited work, “Activity Knowledge Graph Recognition by Eye Gaze: Identification of Distant Object in Eye Sight for Watch Activity” (2021), introduces a novel framework that leverages gaze tracking to construct structured knowledge graphs of human activities—enabling robots and smart home systems to infer intent and plan actions from subtle visual cues. By transforming raw activity sequences into predicate-rich graph representations, Toyosaka addresses a critical bottleneck in higher-level applications, such as autonomous task planning and long-term activity log retrieval. Though early in their career, with 3 citations on this flagship paper, the work has already sparked interest in embodied AI and ambient intelligence communities. Toyosaka’s contributions are particularly notable for their practical approach to integrating gaze data with symbolic reasoning, offering a scalable path toward more intuitive human-robot collaboration. Their research promises to reshape how machines interpret and anticipate human behavior in dynamic, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1