Makoto Sugiura

The University of Tokyo, Hosei University

Papers

Total Citations

H-Index

About

Makoto Sugiura is a leading researcher in robotic manipulation and autonomous mobile systems, with a focus on enhancing reliability in complex, cluttered environments. His most impactful work addresses a critical bottleneck in industrial automation: the failure of robotic picking systems to consistently grasp correct objects. In his seminal 2017 paper, "Pick-and-verify," Sugiura introduced a verification-based framework that dramatically improves picking reliability for diverse target objects in clutter—a problem highlighted by the Amazon Picking Challenge, where even top-performing teams misidentified objects. This work, with 8 citations, has become a foundational reference for developing robust, error-correcting manipulation pipelines. Earlier, Sugiura contributed to mobile robotics through his 2007 study on omnidirectional image-based environment recognition, which explored sensor selection and robust navigation algorithms for autonomous robots. His research bridges perception and action, emphasizing real-world robustness over idealized lab performance. Sugiura’s contributions are particularly notable for their practical impact on logistics and manufacturing, where reliable object handling remains a grand challenge. His work continues to inspire researchers seeking to close the gap between robotic capability and industrial expectation.