Yoshiyuki Hatta
Papers
8
Total Citations
52
H-Index
4
About
Yoshiyuki Hatta is a pioneering researcher at the intersection of robotics, human-machine interaction, and intelligent grasp analysis. His work centers on two key domains: multisensory grasp classification and precision robotic manipulation. Hatta’s most significant contribution is the development of novel methodologies using instrumented data gloves and deep learning to decode human grasping patterns—work that has direct applications in prosthetics, rehabilitation, and robotic dexterity. His 2024 paper “Glove-Net: Enhancing Grasp Classification with Multisensory Data and Deep Learning Approach” (16 citations) introduces a groundbreaking framework that captures intricate finger posture dynamics, while his companion study “From Tactile Signals to Grasp Classification” (11 citations) explores machine learning patterns in hand-object interactions. Hatta has also advanced robotic precision through trajectory correction using force feedback and BiLSTM networks, and designed a radial-gap two-degree-of-freedom motor based on magnetic screw structures for generating simultaneous torque and thrust. His recent work on grasp synergies during reach-to-grasp movements (2025) provides critical insights for developing control strategies for five-fingered prosthetic hands. With a growing citation impact and a portfolio spanning from tactile sensing to high-precision machining force control, Hatta is establishing himself as a leading voice in creating more intuitive, capable robotic systems that learn from human touch.
Research Focus
Key Achievements
Top Papers
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- 8High Precision Machining Force Control of VCM-driven Deburring Equipment2 citations · 2022