Atsumu Hiramoto — accepted AI-conference papers

2025

Focused Blind Switching Manipulation Based on Constrained and Regional Touch States of Multi-Fingered Hand Using Deep Learning

Satoshi Funabashi, Atsumu Hiramoto, Naoya Chiba, Alexander Schmitz, Shardul Kulkarni, Tetsuya Ogata

ICRA 2025

To achieve a desired grasping posture (including object position and orientation), multi-finger motions need to be conducted according to the the current touch state. Specifically, when subtle changes happen during correcting the object state, not only proprioception but also tactile information fro

Cited by 1 Source ↗Scholar ↗

2022

Multi-Fingered In-Hand Manipulation With Various Object Properties Using Graph Convolutional Networks and Distributed Tactile Sensors

Satoshi Funabashi, Tomoki Isobe, Fei Hongyi, Atsumu Hiramoto, Alexander Schmitz, Shigeki Sugano +1

RA-L 2022

Multi-fingered hands could be used to achieve many dexterous manipulation tasks, similarly to humans, and tactile sensing could enhance the manipulation stability for a variety of objects. However, tactile sensors on multi-fingered hands have a variety of sizes and shapes. Convolutional neural netwo

Cited by 45 Source ↗Scholar ↗