教師資料查詢 | 類別: 期刊論文 | 教師: 周永山 Chou Yung-shan (瀏覽個人網頁)

標題:Visually Guided Picking Control of an Omnidirectional Mobile Manipulator Based on End-to-End Multi-Task Imitation Learning
學年108
學期1
出版(發表)日期2019/12/25
作品名稱Visually Guided Picking Control of an Omnidirectional Mobile Manipulator Based on End-to-End Multi-Task Imitation Learning
作品名稱(其他語言)
著者Chi-Yi Tsai; Yung-Shan Chou; Ching-Chang Wong; Yu-Cheng Lai; Chien-Che Huang
單位
出版者
著錄名稱、卷期、頁數IEEE Access 8, p.1882-1891
摘要In this paper, a novel deep convolutional neural network (CNN) based high-level multi-task
control architecture is proposed to address the visual guide-and-pick control problem of an omnidirectional
mobile manipulator platform based on deep learning technology. The proposed mobile manipulator control
system only uses a stereo camera as a sensing device to accomplish the visual guide-and-pick control
task. After the stereo camera captures the stereo image of the scene, the proposed CNN-based high-level
multi-task controller can directly predict the best motion guidance and picking action of the omnidirectional mobile manipulator by using the captured stereo image. In order to collect the training dataset, we manually controlled the mobile manipulator to navigate in an indoor environment for approaching and picking up an object-of-interest (OOI). In the meantime, we recorded all of the captured stereo images and the corresponding control commands of the robot during the manual teaching stage. In the training stage, we employed the end-to-end multi-task imitation learning technique to train the proposed CNN model by
learning the desired motion and picking control strategies from prior expert demonstrations for visually
guiding the mobile platform and then visually picking up the OOI. Experimental results show that the
proposed visually guided picking control system achieves a picking success rate of about 78.2% on average.
關鍵字Omnidirectional mobile manipulator;visually guided picking control;deep learning;multi-task imitation learning;end-to-end control
語言英文(美國)
ISSN2169-3536
期刊性質國外
收錄於SCI;
產學合作
通訊作者
審稿制度
國別美國
公開徵稿
出版型式,電子版
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