Ryota Hagiwara

Papers

Total Citations

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About

Ryota Hagiwara’s research focuses on the intersection of computer vision and robotics, particularly addressing the challenge of enabling robots to perceive and manipulate deformable objects like cloth. His most-cited work, “Verification of illumination tolerance for photo-model-based cloth recognition” (2017, 6 citations), and its follow-up (2018, 5 citations) tackle a fundamental problem: while humans effortlessly recognize and handle garments under varying lighting, robots struggle with such non-rigid materials. Hagiwara’s contributions center on developing photo-model-based methods that improve pose estimation and handling of unique clothes, even under diverse illumination conditions—a critical step toward practical robotic laundry or textile sorting. Earlier, he explored CPG-based locomotion learning for four-legged robots using multi-objective genetic algorithms (2014, 2 citations), showing versatility in bio-inspired control. Though his citation counts are modest, Hagiwara’s work addresses a persistent bottleneck in robotics: the reliable recognition and manipulation of deformable objects. His research is particularly relevant for students and engineers interested in bridging visual perception and physical interaction with everyday materials, offering incremental but essential advances in illumination-robust cloth handling.