Yoshiki KANAI
Papers
1
Total Citations
1
H-Index
1
About
Yoshiki Kanai is a robotics researcher whose work centers on advancing imitation learning through improved human-robot interaction and teleoperation systems. His primary research areas include robotic manipulation, demonstration data collection, and dual-arm mobile manipulators. Kanai’s major contribution is the development of AIREC-Basic, a leader–follower teleoperation system designed to address a critical bottleneck in imitation learning: the quality and consistency of human demonstration data. By equipping a dual-arm mobile manipulator with an efficient teleoperation interface, his system enables more reliable data collection for training robots to perform complex tasks. While his most-cited paper is still early in its impact trajectory with 1 citation as of 2026, the work represents a foundational step toward scalable, real-world robotic learning. Kanai’s research is notable for its practical focus on bridging the gap between human demonstration and robot execution, a key challenge in modern robotics. His contributions are particularly relevant for researchers and students interested in imitation learning, human-robot collaboration, and the development of robust data collection pipelines for autonomous systems.
Research Focus
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Top Papers
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