Knowledge Commons of Institute of Automation,CAS
Robot learning through observation via coarse-to-fine grained video summarization | |
Zhang, Yujia1![]() ![]() ![]() ![]() | |
发表期刊 | APPLIED SOFT COMPUTING
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ISSN | 1568-4946 |
2021-02-01 | |
卷号 | 99期号:/页码:106913 |
摘要 | Learning human daily behavior is important for enabling robots to perform tasks and assist people. However, most prior work either requires specific sensors for capturing data or heavily relies on prior knowledge of human motion, which can be difficult to obtain. To alleviate the above problems, we propose a novel pipeline for robots to learn human behavior based on coarse-to-fine video summarization using a single Kinect camera. Specifically, the robot first retrieves information of general interest followed by a task-specific content retrieval, then focuses on fine-grained motion clips of human behavior, and guides itself by using an object-centric learning method to complete the desired task. Our work has three unique advantages: (1) it enables the robot to effectively capture granularity hierarchies of human behavior which efficiently exploits multi-stage information while alleviating disturbances and redundancies in visual data; (2) it obtains knowledge by focusing on object movements in summarized motion clips which does not require any prior knowledge of human motion; (3) it only requires a single Kinect sensor for the robot to learn human behavior which is fully accessible and easy to equip. Experiments in an office environment were performed to validate the efficiency and effectiveness of the proposed framework, and the results indicate that this approach exhibits good learning efficacy for the robot to understand human behavior and learn to perform tasks. (C) 2020 Elsevier B.V. All rights reserved. |
关键词 | Robotic vision Learning through observation Coarse-to-fine video summarization |
DOI | 10.1016/j.asoc.2020.106913 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2017YFC0820203] ; National Natural Science Foundation of China[61673378] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications |
WOS记录号 | WOS:000608174700007 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 智能机器人 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/42916 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
通讯作者 | Zhang, Yujia |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhang, Yujia,Li, Qianzhong,Zhao, Xiaoguang,et al. Robot learning through observation via coarse-to-fine grained video summarization[J]. APPLIED SOFT COMPUTING,2021,99(/):106913. |
APA | Zhang, Yujia,Li, Qianzhong,Zhao, Xiaoguang,&Tan, Min.(2021).Robot learning through observation via coarse-to-fine grained video summarization.APPLIED SOFT COMPUTING,99(/),106913. |
MLA | Zhang, Yujia,et al."Robot learning through observation via coarse-to-fine grained video summarization".APPLIED SOFT COMPUTING 99./(2021):106913. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Robot Learning Throu(5989KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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