光ファイバ触覚指による剛柔インタラクティブ把持の学習
Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping
多チャネル光ファイバを内蔵した柔軟触覚指を開発し、機械学習で力・トルク・接触をリアルタイム推定、3本指グリッパの配置を触覚データに基づき適応制御して剛柔インタラクティブ把持を実現した。
著者: Linhan Yang, Xudong Han, Weijie Guo, Fang Wan, Jia Pan, Chaoyang Song
分類: cs.RO
原文アブストラクト
This paper presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning methods are used to train a model for real-time prediction of force, torque, and contact using the tactile data collected. We further integrated such fingers in a reconfigurable gripper design with three fingers so that the finger arrangement can be actively adjusted in real-time based on the tactile data collected during grasping, achieving the process of rigid-soft interactive grasping. Detailed sensor calibration and experimental results are also included to further validate the proposed design for enhanced grasping robustness.
