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About

Takuya Igaue is a researcher at the forefront of robotics and computer vision, with a focus on autonomous inspection and human-robot interaction. His work spans three key areas: change detection for industrial infrastructure, 3D tunnel measurement, and the cognitive science of robotics. Igaue’s most notable contribution is his 2023 study on the uncanny valley effect, where he used a Contrastive Language-Image Pre-training (CLIP) neural network to model and predict the negative emotional responses humans have toward near-human robots and characters. This work, with 12 citations, bridges artificial intelligence and psychology, offering a computational framework for understanding a long-standing phenomenon in robotics. In practical applications, Igaue has developed systems for mobile robots to detect changes in plant and pipe surfaces from inspection videos, achieving 5 and 2 citations respectively for his 2024 and 2025 papers. His 2023 method for cooperative 3D tunnel measurement using omnidirectional laser light and 2D–3D registration further demonstrates his ability to solve real-world infrastructure challenges. Igaue’s research is distinguished by its integration of deep learning, 3D geometry, and field robotics, making him a rising figure in autonomous inspection and human-aware robot design.