Yuki Suga
Papers
19
Total Citations
324
H-Index
7
About
Yuki Suga is a leading researcher at the intersection of robotics, deep learning, and multimodal perception, whose work is fundamentally advancing how robots perceive, learn from, and physically interact with their environment. His most impactful contribution is in **multimodal integration learning**, where he pioneered the use of deep neural networks to fuse sensory data—such as vision, touch, and proprioception—enabling robots to achieve greater perceptual precision and robust behavior. This foundational work, particularly his 2014 paper on the topic, has garnered **196 citations** and established a core framework for the field. Suga has also achieved remarkable feats in **dexterous manipulation**, most notably demonstrating the first successful in-air knotting of a rope using a dual-arm robot, a complex task requiring real-time adaptation to a flexible, dynamic object. His research extends to **human-robot collaboration**, including the development of intuitive interfaces for assistive robotics (e.g., a mouth-operated wheelchair arm) and the creation of compliant, inflatable robot arms for safe physical interaction. Furthermore, Suga is dedicated to education, having developed a basic educational kit to systematically integrate deep neural networks into robotics curricula, helping to train the next generation of roboticists.
Research Focus
Key Achievements
Top Papers
- 1Multimodal integration learning of robot behavior using deep neural networks196 citations · 2014
- 2In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning26 citations · 2021
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- 8Intersensory Causality Modeling Using Deep Neural Networks7 citations · 2013
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