Takumi Hachimine

According to our database, Takumi Hachimine authored at least 8 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Robotic system for chemical experiment automation with dual demonstration of end-effector and jig operations.

Hikaru Sasaki, Naoto Komeno, Takumi Hachimine, Kei Takahashi, Yu-ya Ohnishi, Tetsunori Sugawara, Araki Wakiuchi, Miho Hatanaka, Tomoyuki Miyao, Hiroharu Ajiro, Mikiya Fujii, Takamitsu Matsubara

Int. J. Intell. Robotics Appl., March, 2026

DecompGrind: A Decomposition Framework for Robotic Grinding via Cutting-Surface Planning and Contact-Force Adaptation.

Shunsuke Araki, Takumi Hachimine, Yuki Saito, Kouhei Ohnishi, Jun Morimoto, Takamitsu Matsubara

CoRR, March, 2026

Task-Relevant and Irrelevant Region-Aware Augmentation for Generalizable Vision-Based Imitation Learning in Agricultural Manipulation.

Shun Hattori, Hikaru Sasaki, Takumi Hachimine, Yusuke Mizutani, Takamitsu Matsubara

CoRR, March, 2026

VoxelDiffusionCut: Non-destructive Internal-part Extraction via Iterative Cutting and Structure Estimation.

Takumi Hachimine, Yuhwan Kwon, Cheng-Yu Kuo, Tomoya Yamanokuchi, Takamitsu Matsubara

CoRR, March, 2026

2025

Cutting Sequence Diffuser: Sim-to-Real Transferable Planning for Object Shaping by Grinding.

Takumi Hachimine, Jun Morimoto, Takamitsu Matsubara

IEEE Robotics Autom. Lett., February, 2025

Diffusion Model and RRT*-Based Methods for Reflected Mass Optimization in Motion Planning.

David Gutierrez-Moreno, Simon Armleder, Yuhwan Kwon, Takumi Hachimine, Yoshihisa Tsurumine, Takamitsu Matsubara, Gordon Cheng

Proceedings of the IEEE/SICE International Symposium on System Integration, 2025

2024

Analysis of Grinding Motion using Force/Tactile Sensation.

Tomoya Kitamura, Takumi Hachimine, Takamitsu Matsubara, Yuki Saito, Hiroshi Asai, Kouhei Ohnishi

Proceedings of the 33rd IEEE International Symposium on Industrial Electronics, 2024

2023

Learning to Shape by Grinding: Cutting-Surface-Aware Model-Based Reinforcement Learning.

Takumi Hachimine, Jun Morimoto, Takamitsu Matsubara

IEEE Robotics Autom. Lett., October, 2023