Atsumu Hiramoto
Papers
1
Total Citations
46
H-Index
1
About
Dr. Atsumu Hiramoto is a leading roboticist specializing in dexterous manipulation, tactile sensing, and machine learning for robotics. His research focuses on enabling multi-fingered robotic hands to achieve human-like in-hand manipulation, particularly for objects with diverse properties. Dr. Hiramoto’s major contribution lies in integrating graph convolutional networks (GCNs) with distributed tactile sensors to overcome the challenge of varying sensor geometries across different finger sizes and shapes. His most-cited work, "Multi-Fingered In-Hand Manipulation With Various Object Properties Using Graph Convolutional Networks and Distributed Tactile Sensors" (2022, 46 citations), demonstrates how GCNs can process irregular tactile data to enhance manipulation stability and adaptability. This approach marks a significant advance over traditional convolutional neural networks, which struggle with non-uniform sensor layouts. Dr. Hiramoto’s work has been recognized for bridging the gap between tactile feedback and robust control, with potential applications in prosthetics, industrial automation, and human-robot interaction. His research continues to push the boundaries of robotic dexterity, making him a notable figure in the field of embodied AI and sensorimotor learning.
Research Focus
Key Achievements
Top Papers
- 1