Max Robotics — Global Service Robot Directory + US Certification

About

Daisuke Deguchi’s research lies at the intersection of human-robot interaction, computer vision, and assistive robotics, with a focus on how robots perceive and act in complex environments. His work on eye-gaze behavior in robotic wheelchair operation (2019, 7 citations) provides critical insights into how driver experience shapes visual attention, offering design principles for safer, more intuitive assistive devices. Deguchi has also made notable contributions to industrial robotics through his development of encoded markers for working robots (2016–2017), addressing the practical challenge of reliable object identification and picking in tasks like the Amazon Picking Challenge. His markers are designed to be robust under blur and varying positions, filling a gap in standardized robotic vision systems. More recently, he has advanced 3D object pose estimation by proposing a next-viewpoint recommendation method that minimizes pose ambiguity (2019), enabling more accurate multi-view pose estimation for robotic manipulation. While his citation counts are modest, Deguchi’s work is characterized by its applied focus on real-world robotic tasks—from assistive mobility to warehouse automation—demonstrating a commitment to solving tangible problems in human-centered and industrial robotics.