Papers
17
Total Citations
99
H-Index
6
About
Kazuhito Sato is a leading researcher in autonomous mobile robotics, with a primary focus on unsupervised learning for robot vision and navigation. His work centers on enabling robots to perceive and understand their environments without human intervention, particularly through innovative scene classification and feature selection methods. Sato’s major contributions include developing Adaptive Category Mapping Networks (ACMNs) for topological feature learning and pioneering unsupervised category classification using neural networks like ART-2 and CPNs. He has also advanced real-world agricultural robotics, creating prototypes for mallard navigation in paddy fields to support remote farming. His most cited paper, “Unsupervised Feature Selection and Category Classification for a Vision-Based Mobile Robot” (2011), has garnered 14 citations, while his work on mallard navigation (2021) has 13 citations, demonstrating both foundational and applied impact. Sato’s research on semantic position recognition and visual landmark detection, robust to human interference, has been particularly notable for its potential in human-robot interaction. With over 60 total citations across his top papers, Sato’s work bridges theoretical unsupervised learning and practical robotic systems, making him a key figure in advancing autonomous robot perception and field applications.
Research Focus
Key Achievements
Top Papers
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- 4Scene classification using unsupervised neural networks for mobile robot vision7 citations · 2012
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- 7Parallel implementation of saliency maps for real-time robot vision5 citations · 2014
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- 9Testing and evaluation of a patrol robot system for hospitals5 citations · 2003
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