Kenjiro Yamamoto
Hitachi (Japan), Hitachi (United States), Center for Innovation
Papers
19
Total Citations
286
H-Index
7
About
Kenjiro Yamamoto is a robotics researcher whose work spans autonomous mobile robotics, human-robot interaction, and deep learning-based motion generation. His early contributions focused on practical challenges in service and security robotics, most notably developing methods for tracking multiple walking humans from a moving robot using laser range finders — a foundational capability for real-world deployment, earning 83 citations. Around the same period, he contributed to the design of EMIEW, a human-symbiotic autonomous mobile robot intended to support people in daily life. Yamamoto's more recent work has shifted toward deep learning approaches for complex robotic manipulation and navigation. His highly cited 2022 paper on embodied predictive models for door-opening tasks (81 citations) demonstrates sophisticated whole-body control through multitask deep reinforcement learning. Complementing this, his research on contact-rich flexible object manipulation integrates vision and tactile sensing, while his spatial attention and modality attention frameworks improve robustness and interpretability in robot motion generation. Across his career, Yamamoto has consistently bridged perception, learning, and physical robot deployment — from crowded pedestrian navigation to language-guided grasping — making meaningful contributions to the challenge of robots that can reliably and intelligently operate alongside humans in unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1People Tracking Using a Robot in Motion with Laser Range Finder83 citations · 2006
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- 9Two-View Monocular Depth Estimation by Optic-Flow-Weighted Fusion6 citations · 2019
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