Masahide Kaneko
Papers
26
Total Citations
224
H-Index
8
About
Masahide Kaneko is a robotics and artificial intelligence researcher whose work spans human-robot interaction, probabilistic motion modeling, and autonomous robot navigation. His most influential contribution lies in the development of Gaussian Process Hidden Semi-Markov Models (GP-HSMM) for unsupervised segmentation of continuous motion and time-series data, with his 2017 paper on the topic accumulating 56 citations—a landmark achievement that has shaped how robots learn to recognize and replicate human actions. Building on this foundation, Kaneko extended the framework to model two-person interactions through a coupled GP-HSMM approach, reflecting his broader interest in enabling robots to understand social dynamics. His work on accompanying robots, grounded in adaptive artificial potential field methods, demonstrates a sustained effort to make mobile robots responsive to dynamic real-world environments while remaining socially considerate of human movement. Kaneko has also contributed to multimodal concept formation, allowing robots to integrate visual, auditory, and haptic information alongside natural language, and to disaster-response robotics, where sound-based localization enables robots to find humans when visual sensing fails. Collectively, his research positions him as a versatile contributor to the field of intelligent, socially aware robotics.
Research Focus
Key Achievements
Top Papers
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- 2Concept formation by robots using an infinite mixture of models19 citations · 2015
- 3A framework for adaptive motion control of autonomous sociable guide robot18 citations · 2016
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