Naoya Kagawa

Papers

Total Citations

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About

Naoya Kagawa is a robotics researcher specializing in the calibration and precision modeling of robotic manipulators, with a particular focus on camera-based measurement systems. His work addresses the critical challenge of bridging the gap between virtual robot models and their real-world counterparts, a fundamental step for seamless robot integration and high-accuracy automation. Kagawa's major contributions lie in developing novel optimization frameworks for kinematic and compliance calibration. His 2023 work introduced the "Visual-Biased Observability Index," a metric designed to select optimal end-effector poses for camera-based calibration, directly improving the discovery of kinematic parameter errors. More recently, in 2025, he tackled the complex problem of concurrently identifying joint offsets and compliance errors—a task complicated by measurement inaccuracies in camera systems. By proposing a measurement pose optimization strategy, his research enables more accurate modeling of joint flexibility, a critical factor for precise robotic control. Though his most-cited papers are early in their citation lifecycle (with 4 and 1 citations respectively), they represent foundational steps in a promising research trajectory. Kagawa's work is particularly valuable for researchers and engineers in industrial robotics, where achieving sub-millimeter accuracy through cost-effective camera-based calibration is a persistent goal.