Yasuhiko Arai

Papers

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About

Yasuhiko Arai is a leading researcher in autonomous robotics, with a primary focus on object recognition, manipulation, and path planning for mobile service robots. His most impactful work centers on the Nakanoshima Robot Challenge, where he has pioneered methods for autonomous trash collection in unstructured environments. Arai’s major contributions include the integration of deep learning object detectors, specifically YOLOv3 and YOLO-based models, with dual-arm robotic systems to enable real-time identification and grasping of common waste items like bottles, cans, and bento boxes. His 2023 paper on dual-arm autonomous mobile robots, which combines spline curve path planning with YOLO-based recognition, has garnered 9 citations, while his foundational 2020 work on garbage detection using YOLOv3 has been cited 7 times. Notably, Arai has addressed critical challenges in creating efficient training data for object detectors, reducing the labor-intensive annotation process. His research directly advances the practical deployment of robots in public spaces, demonstrating robust performance under time constraints. Through his work, Arai has established himself as a key contributor to the fields of robotic manipulation, computer vision, and autonomous navigation, with clear implications for smart city waste management and service robotics.