Model-Agnostic Metalearning-Based Text-Driven Visual Navigation Model for Unfamiliar Tasks
文献类型:期刊论文
作者 | Xue TF(薛天放)1,2,3,4; Yu HB(于海斌)1,2,4![]() |
刊名 | IEEE ACCESS
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出版日期 | 2020 |
卷号 | 8页码:166742-166752 |
关键词 | Navigation Task analysis Visualization Adaptation models Semantics Robots Feature extraction Mapless-visual navigation semantic segmentation text-driven model-agnostic meta-learning |
ISSN号 | 2169-3536 |
产权排序 | 1 |
英文摘要 | As vision and language processing techniques have made great progress, mapless-visual navigation is occupying uppermost position in domestic robot field. However, most current end-to-end navigation models tend to be strictly trained and tested on identical datasets with stationary structure, which leads to great performance degradation when dealing with unseen targets and environments. Since the targets of same category could possess quite diverse features, generalization ability of these models is also limited by their visualized task description. In this article we propose a model-agnostic metalearning based text-driven visual navigation model to achieve generalization to untrained tasks. Based on meta-reinforcement learning approach, the agent is capable of accumulating navigation experience from existing targets and environments. When applied to finding a new object or exploring in a new scene, the agent will quickly learn how to fulfill this unfamiliar task through relatively few recursive trials. To improve learning efficiency and accuracy, we introduce fully convolutional instance-aware semantic segmentation and Word2vec into our DRL network to respectively extract visual and semantic features according to object class, creating more direct and concise linkage between targets and their surroundings. Several experiments have been conducted on realistic dataset Matterport3D to evaluate its target-driven navigation performance and generalization ability. The results demonstrate that our adaptive navigation model could navigate to text-defined targets and achieve fast adaption to untrained tasks, outperforming other state-of-the-art navigation approaches. |
WOS关键词 | SEARCH |
资助项目 | National Key Research and Development Program of China[2018YFB1700200] ; National Natural Science Foundation of China[61803368] ; National Natural Science Foundation of China[61533015] ; National Natural Science Foundation of China[61972389] ; National Natural Science Foundation of China[61903356] ; China Postdoctoral Science Foundation[2019M661156] ; Liaoning Provincial Natural Science Foundation of China[20180540114] ; Liaoning Provincial Natural Science Foundation of China[20180520029] ; Youth Innovation Promotion Association CAS ; Independent Subject of State Key Laboratory of Robotics |
WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
语种 | 英语 |
WOS记录号 | WOS:000572972100001 |
资助机构 | National Key Research and Development Program of China [2018YFB1700200] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61803368, 61533015, 61972389, 61903356] ; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2019M661156] ; Liaoning Provincial Natural Science Foundation of China [20180540114, 20180520029] ; Youth Innovation Promotion Association CAS ; Independent Subject of State Key Laboratory of Robotics |
源URL | [http://ir.sia.cn/handle/173321/27689] ![]() |
专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
通讯作者 | Yu HB(于海斌) |
作者单位 | 1.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 3.University of Chinese Academy of Sciences, Beijing 100049, China 4.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China |
推荐引用方式 GB/T 7714 | Xue TF,Yu HB. Model-Agnostic Metalearning-Based Text-Driven Visual Navigation Model for Unfamiliar Tasks[J]. IEEE ACCESS,2020,8:166742-166752. |
APA | Xue TF,&Yu HB.(2020).Model-Agnostic Metalearning-Based Text-Driven Visual Navigation Model for Unfamiliar Tasks.IEEE ACCESS,8,166742-166752. |
MLA | Xue TF,et al."Model-Agnostic Metalearning-Based Text-Driven Visual Navigation Model for Unfamiliar Tasks".IEEE ACCESS 8(2020):166742-166752. |
入库方式: OAI收割
来源:沈阳自动化研究所
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