Hiroki Goto

Papers

Total Citations

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About

Hiroki Goto is a robotics researcher whose work centers on advancing robot calibration and kinematic modeling, with a particular focus on camera-based systems. His most-cited paper, "Visual-Biased Observability Index for Camera-Based Robot Calibration" (2023, 4 citations), introduces a novel observability index that prioritizes visual measurement data to optimize end-effector pose selection during calibration. This contribution addresses a critical bottleneck in industrial robotics: the need for accurate kinematic parameter estimation to bridge the gap between virtual simulations and real-world robot performance. By refining how positioning errors are detected and corrected, Goto’s work directly enhances the precision and efficiency of robot integration in manufacturing and automation. His research is notable for its practical orientation, offering a systematic method to improve calibration outcomes without requiring expensive external sensors. Though early in his career, Goto’s focus on visual-biased techniques signals a promising trajectory in sensor-driven robotics, with potential applications in collaborative robots and autonomous systems. His work is particularly valuable for students and engineers seeking to understand the intersection of computer vision and robot kinematics.