Papers
17
Total Citations
151
H-Index
6
About
Shin-ichiro Kaneko is a robotics and artificial intelligence researcher whose work spans autonomous mobile robots, deep learning-based perception, humanoid systems, and assistive technologies. His most impactful contribution, a 2019 paper on deep learning-based landmark detection for outdoor robot localization (51 citations), addressed critical limitations of GPS systems in variable environmental conditions, offering a robust alternative for real-world deployments. Building on this foundation, his 2017 work on pick-and-place manipulation using deep learning and intuitive teaching interfaces (33 citations) advanced human-robot collaboration in assembly tasks, particularly for small and medium-sized enterprises. Kaneko has consistently bridged cutting-edge AI with practical robotics applications, tackling challenges as diverse as electronic waste recycling through robotic manipulation, precision agriculture automation, and surveillance systems for elderly care. His earlier research into humanoid robot optimization using evolutionary algorithms and novel zero-moment point control methods for legged robots demonstrates a career-long commitment to foundational robotics theory. More recently, his development of walking assistive robots for elderly users in outdoor environments reflects a deepening focus on socially beneficial technologies. With over 130 cumulative citations, Kaneko's body of work represents a meaningful and evolving contribution to intelligent, human-centered robotics research.
Research Focus
Key Achievements
Top Papers
- 1Deep Learning-Based Landmark Detection for Mobile Robot Outdoor Localization51 citations · 2019
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- 6Evolution of Task Switching Behaviors in Real Mobile Robots6 citations · 2008
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- 8Control of the Walking Robots based on the ZMP Defined on the Ceiling5 citations · 2005
- 9Application of a CORBA - based humanoid robot system for accident site inspection through the internet4 citations · 2003
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