Toward navigation ability for autonomous mobile robots with learning from demonstration paradigm
Learning from demonstration, as an important component of imitation learning, is a paradigm for robot to learn new tasks. Considering the application of learning from demonstration in the navigation issue, the robot can also acquire the navigation task via the human teacher’s demonstration. Based on...
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Online Access: | https://doi.org/10.1177/1729881418777939 |
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doaj-628fd244e2fc4880a1802a1eb7df282c2020-11-25T02:52:41ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142018-05-011510.1177/1729881418777939Toward navigation ability for autonomous mobile robots with learning from demonstration paradigmXinzheng Zhang0Jianfen Zhang1Junpei Zhong2 School of Electrical and Information Engineering, Jinan University, Zhuhai, Guangdong, China School of Electrical and Information Engineering, Jinan University, Zhuhai, Guangdong, China National Institute of Advanced Industrial Science and Technology, Tokyo, JapanLearning from demonstration, as an important component of imitation learning, is a paradigm for robot to learn new tasks. Considering the application of learning from demonstration in the navigation issue, the robot can also acquire the navigation task via the human teacher’s demonstration. Based on research of the human brain neocortex, in this article, we present a learning from demonstration navigation paradigm from the perspective of hierarchical temporal memory theory. As a type of end-to-end learning form, the demonstrated relationship between perception data and motion commands will be learned and predicted by using hierarchical temporal memory. This framework first perceives images to obtain the corresponding categories information; then the categories incorporated with depth and motion command data are encoded as a sequence of sparse distributed representation vectors. The sequential vectors are treated as the inputs to train the navigation hierarchical temporal memory. After the training, the navigation hierarchical temporal memory stores the transitions of the perceived images, depth, and motion data so that future motion commands can be predicted. The performance of the proposed navigation strategy is evaluated via the real experiments and the public data sets.https://doi.org/10.1177/1729881418777939 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xinzheng Zhang Jianfen Zhang Junpei Zhong |
spellingShingle |
Xinzheng Zhang Jianfen Zhang Junpei Zhong Toward navigation ability for autonomous mobile robots with learning from demonstration paradigm International Journal of Advanced Robotic Systems |
author_facet |
Xinzheng Zhang Jianfen Zhang Junpei Zhong |
author_sort |
Xinzheng Zhang |
title |
Toward navigation ability for autonomous mobile robots with learning from demonstration paradigm |
title_short |
Toward navigation ability for autonomous mobile robots with learning from demonstration paradigm |
title_full |
Toward navigation ability for autonomous mobile robots with learning from demonstration paradigm |
title_fullStr |
Toward navigation ability for autonomous mobile robots with learning from demonstration paradigm |
title_full_unstemmed |
Toward navigation ability for autonomous mobile robots with learning from demonstration paradigm |
title_sort |
toward navigation ability for autonomous mobile robots with learning from demonstration paradigm |
publisher |
SAGE Publishing |
series |
International Journal of Advanced Robotic Systems |
issn |
1729-8814 |
publishDate |
2018-05-01 |
description |
Learning from demonstration, as an important component of imitation learning, is a paradigm for robot to learn new tasks. Considering the application of learning from demonstration in the navigation issue, the robot can also acquire the navigation task via the human teacher’s demonstration. Based on research of the human brain neocortex, in this article, we present a learning from demonstration navigation paradigm from the perspective of hierarchical temporal memory theory. As a type of end-to-end learning form, the demonstrated relationship between perception data and motion commands will be learned and predicted by using hierarchical temporal memory. This framework first perceives images to obtain the corresponding categories information; then the categories incorporated with depth and motion command data are encoded as a sequence of sparse distributed representation vectors. The sequential vectors are treated as the inputs to train the navigation hierarchical temporal memory. After the training, the navigation hierarchical temporal memory stores the transitions of the perceived images, depth, and motion data so that future motion commands can be predicted. The performance of the proposed navigation strategy is evaluated via the real experiments and the public data sets. |
url |
https://doi.org/10.1177/1729881418777939 |
work_keys_str_mv |
AT xinzhengzhang towardnavigationabilityforautonomousmobilerobotswithlearningfromdemonstrationparadigm AT jianfenzhang towardnavigationabilityforautonomousmobilerobotswithlearningfromdemonstrationparadigm AT junpeizhong towardnavigationabilityforautonomousmobilerobotswithlearningfromdemonstrationparadigm |
_version_ |
1724728318877499392 |