H. Kubotera
Papers
1
Total Citations
4
H-Index
1
About
H. Kubotera is a researcher in robotics and human-robot interaction, with a focus on enabling robots to learn from human demonstration. Their key research areas include imitation learning, motion planning, and the integration of environmental sensing into robotic task acquisition. Kubotera’s most notable contribution is the development of an imitation algorithm that allows robots to acquire typical tasks from multiple measurements of human actions in daily life, as detailed in their 2004 paper "Robot imitation of human motion based on qualitative description from multiple measurement of human and environmental data." This work, which has garnered 4 citations, proposes a system that first measures human object-transferring tasks on a table, then calculates qualitative descriptions to guide robot motion. By emphasizing the use of environmental data alongside human motion, Kubotera’s approach addresses the challenge of variability in human demonstrations, making robotic learning more robust. Their research has implications for assistive robotics and autonomous systems, contributing to the broader field of learning from demonstration.
Research Focus
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Top Papers
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