Takashi Kuremoto

About

Takashi Kuremoto is a researcher whose work spans the intersecting frontiers of machine learning, robotics, and human-machine interaction, with particular expertise in neural networks, reinforcement learning, and intelligent control systems. His research has made meaningful contributions to autonomous robot behavior, where he developed neuro-fuzzy systems combined with reinforcement learning algorithms to enable adaptive swarm behaviors and cooperative multi-robot coordination. A recurring theme throughout his career is the design of self-organizing maps (SOMs), most notably his Parameterless-Growing-SOM (PL-G-SOM), which he applied to voice command and hand gesture recognition systems, enabling partner robots to learn from human instructors with greater flexibility. Kuremoto has also explored brain-computer interfaces, investigating EEG signal classification for mental task recognition, and more recently embraced deep learning, proposing convolutional neural network-based visual systems for robotic fruit harvesting — his most cited work, accumulating 30 citations. Across his portfolio, he has consistently bridged theoretical advances in computational intelligence with practical robotic applications, making his research valuable to scholars working in autonomous systems, human-robot interaction, and cognitive computing.

Research Focus

Key Achievements

Top Papers

1. 1
   A Visual System of Citrus Picking Robot Using Convolutional Neural Networks

   30 citations · 2018
2. 2
   Adaptive swarm behavior acquisition by a neuro‐fuzzy system and reinforcement learning algorithm

   17 citations · 2009
3. 3
   Parameterless-Growing-SOM and Its Application to a Voice Instruction Learning System

   12 citations · 2010
4. 4
   A hand shape instruction recognition and learning system using growing SOM with asymmetric neighborhood function

   11 citations · 2015
5. 5
   A Human-Machine Interaction System: A Voice Command Learning System Using PL-G-SOM

   9 citations · 2011
6. 6
   A Self-Organized Fuzzy-Neuro Reinforcement Learning System for Continuous State Space for Autonomous Robots

   9 citations · 2008
7. 7
   Cooperative behavior acquisition of multiple autonomous mobile robots by an objective-based reinforcement learning system

   8 citations · 2007
8. 8
   A Hand Image Instruction Learning System Using Transient-SOM

   8 citations · 2007
9. 9
   An intelligent control system construction using high-level time Petri net and Reinforcement Learning

   7 citations · 2010
10. 10
    To extraction the feature of EEG signals for mental task recognition

    7 citations · 2015

Key Collaborators

Contact & Links