Self-Correction for Eye-In-Hand Robotic Grasping Using Action Learning

Robotic grasping for cluttered tasks and heterogeneous targets is not satisfied by the deep learning that has been developed in the last decade. The main problem lies in intelligence, which is stagnant, even though it has a high accuracy rate in usual environment; however, the cluttered grasping env...

詳細記述

書誌詳細

主要な著者:	Muslikhin, Jenq-Ruey Horng, Szu-Yueh Yang, Ming-Shyan Wang
フォーマット:	Artigo
言語:	Inglês
出版事項:	IEEE 2021-01-01
シリーズ:	IEEE Access
主題:	Action learning deep learning eye-in-hand manipulator k-nearest neighbor robotic manipulator robotic grasping
オンライン･アクセス:	https://ieeexplore.ieee.org/document/9622215/
タグ:	タグ追加 タグなし, このレコードへの初めてのタグを付けませんか!