Takumi Hachimine

Nara Institute of Science and Technology

Papers

Total Citations

H-Index

About

Takumi Hachimine is a robotics researcher specializing in the automation of complex industrial processes, with a particular focus on object shaping by grinding. His work sits at the intersection of model-based reinforcement learning and force/tactile sensing, aiming to endow robots with the dexterity required for precision manufacturing tasks. Hachimine’s key contribution is the development of cutting-surface-aware models that enable robots to predict and control material removal during grinding—a traditionally human-dependent skill. His 2023 paper, "Learning to Shape by Grinding," introduces a novel approach to learning object-shape transition dynamics under varying process conditions, laying the groundwork for fully autonomous robotic grinding. More recently, his 2024 work, "Analysis of Grinding Motion using Force/Tactile Sensation," extends this research by incorporating tactile feedback to model human-like manipulation, moving beyond purely visual analysis to capture the nuanced forces involved in object interaction. While still early in his career, with his most cited paper garnering 8 citations, Hachimine’s research is foundational for advancing robotic skill acquisition in manufacturing, promising to transform how machines learn and execute delicate material-removal tasks.