Hirokazu Madokoro
Papers
17
Total Citations
99
H-Index
6
About
Hirokazu Madokoro is a pioneering roboticist whose work bridges unsupervised machine learning and autonomous mobile robotics, with a particular emphasis on vision-based navigation and agricultural applications. His research centers on developing adaptive, unsupervised learning algorithms that allow robots to classify scenes, detect objects, and navigate without human intervention. A major contribution is his work on Adaptive Category Mapping Networks (ACMNs), which enable incremental, all-mode topological feature learning for mobile robot vision. Madokoro’s impact is demonstrated through his highly cited papers, including his 2011 study on unsupervised feature selection for vision-based robots (14 citations) and his 2021 prototype development of small mobile robots for mallard navigation in paddy fields (13 citations), which advances remote farming. His 2014 paper on ACMNs (11 citations) further solidifies his influence. Notably, Madokoro has also explored real-world applications, such as patrol robots for hospitals (2003, 5 citations) and semantic position recognition robust to human interference (2017, 6 citations). His work consistently emphasizes unsupervised, context-aware classification, making significant strides toward autonomous systems that operate intelligently in dynamic, unstructured environments.
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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