Toru Ogawa

Preferred Networks (Japan), Okayama University

Papers

Total Citations

H-Index

About

Toru Ogawa is a pioneering researcher in the intersection of robotics, flexible manipulation, and intelligent tool design. His work centers on enabling robots to perform dynamic tasks with compliant, underactuated bodies—a critical challenge for safe human-robot interaction. Ogawa’s major contributions include developing a dynamic task control method for flexible manipulators using deep recurrent neural networks, which circumvents the difficulties of accurate modeling in soft robotics (2019, 7 citations). He also advanced robotic tool-use through a novel neural network backpropagation approach that optimizes tool shape and trajectory simultaneously (2020, 5 citations). Earlier, Ogawa introduced the dislocation joint, a passive compliance mechanism that provides mechanical softness to prevent damage during human-robot coexistence (2005, 2 citations). Though his citation counts are modest, his work is foundational for emerging fields like soft robotics and adaptive tool-use, bridging control theory, machine learning, and mechanical design. Ogawa’s research offers practical pathways for creating safer, more versatile robots capable of operating in unstructured, human-centered environments.