Rinto Yagawa
Papers
1
Total Citations
2
H-Index
1
About
Rinto Yagawa is a robotics researcher whose work centers on the intersection of manipulation, tactile sensing, and autonomous decision-making for fragile and deformable objects. His most notable contribution, "Learning Food Picking without Food: Fracture Anticipation by Breaking Reusable Fragile Objects" (2023), introduces a novel approach to robotic food handling by training models to anticipate fracture points using tactile sensors—without relying on real food items. This breakthrough addresses the challenge of inter- and intra-category variability in fragile objects, enabling robots to adaptively grasp items like pastries or produce without pre-programmed physical properties. While his citation count is still growing (2 citations for this work), the research has been recognized for its practical potential in food automation and assistive robotics. Yagawa’s work is particularly impactful for its resourceful methodology: using reusable, breakable proxies to train robots safely and efficiently, reducing waste and cost. His contributions are paving the way for more dexterous, sensor-driven robotic systems capable of handling the delicate, unpredictable objects encountered in everyday environments.
Research Focus
Key Achievements
Top Papers
- 1