Shogo Hamano

Papers

Total Citations

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About

Shogo Hamano is a researcher at the forefront of robotics and artificial intelligence, specializing in imitation learning and human-robot interaction. His work addresses a critical bottleneck in robotics: enabling machines to learn complex manipulation tasks from minimal human demonstration. Hamano’s most cited paper, “Using human gaze in few-shot imitation learning for robot manipulation” (2022), introduces a novel approach that leverages human gaze data to significantly improve the efficiency and generalizability of meta-imitation learning. By integrating gaze as a cue for task-relevant features, his method reduces the high cost of data collection and enhances a robot’s ability to adapt to new tasks with only a few examples—a key step toward more intuitive and practical robotic systems. With 5 citations, this work has already sparked interest in the community for its creative fusion of cognitive science and machine learning. Hamano’s contributions are paving the way for robots that learn more like humans, making his research essential reading for anyone interested in the future of autonomous manipulation and few-shot learning.