Papers
15
Total Citations
89
H-Index
5
About
Hitoshi Kono is a robotics researcher whose work spans human-robot interaction, autonomous navigation, reinforcement learning, and disaster response systems. His research consistently addresses one of robotics' central challenges: enabling robots to operate intelligently and safely alongside humans in complex, real-world environments. Kono's most influential contribution, "A Human-Friendly Robot Navigation Algorithm Using the Risk-RRT Approach" (2016, 21 citations), draws on proxemics theory to develop navigation systems that respect human personal space, reducing discomfort in shared environments. Complementing this, his work on transfer learning for heterogeneous multi-agent systems introduced the Knowledge Co-creation Framework (KCF), advancing how robots share and adapt learned policies across different platforms (2014, 17 citations). His research extends into disaster robotics, developing teleoperated rescue systems with on-board LiDAR for perilous environment estimation, and impact-resistant sensor nodes deployable in inaccessible disaster zones. More recently, Kono has explored deep reinforcement learning for autonomous exploration on rough terrain and enhanced LiDAR-based SLAM using intensity and near-infrared data, pushing the boundaries of robot perception and adaptability. Across his career, Kono's work reflects a sustained commitment to making robotic systems more capable, adaptive, and genuinely useful in demanding human environments.
Research Focus
Key Achievements
Top Papers
- 1A human-friendly robot navigation algorithm using the risk-RRT approach21 citations · 2016
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- 3ICP-based SLAM Using LiDAR Intensity and Near-infrared Data10 citations · 2021
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- 9Automatic Transfer Rate Adjustment for Transfer Reinforcement Learning3 citations · 2020
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