Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning

Abstract Visual mapless navigation (VMN), modeling a direct mapping between sensory inputs and agent actions, aims to navigate from a stochastic origin location to a prescribed goal in an unseen scene. A fundamental yet challenging issue in visual mapless navigation is generalizing to a new scene. F...
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Autor*in:	Li, Fei [verfasserIn] Guo, Chi Zhang, Huyin Luo, Binhan

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Context vector Visual mapless navigation Hierarchical semantic information Meta-learning Generalization

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: Complex & intelligent systems - Berlin : SpringerOpen, 2015, 9(2022), 2 vom: 04. Nov., Seite 2031-2041
Übergeordnetes Werk:	volume:9 ; year:2022 ; number:2 ; day:04 ; month:11 ; pages:2031-2041

Links:	Volltext

DOI / URN:	10.1007/s40747-022-00902-7

Katalog-ID:	SPR050101080

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allfields	10.1007/s40747-022-00902-7 doi (DE-627)SPR050101080 (SPR)s40747-022-00902-7-e DE-627 ger DE-627 rakwb eng Li, Fei verfasserin aut Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Visual mapless navigation (VMN), modeling a direct mapping between sensory inputs and agent actions, aims to navigate from a stochastic origin location to a prescribed goal in an unseen scene. A fundamental yet challenging issue in visual mapless navigation is generalizing to a new scene. Furthermore, it is of pivotal concern to design a method to make effective policy learning. To address these issues, we introduce a novel visual mapless navigation model, which integrates hierarchical semantic information represented by context vector with meta-learning to improve the generalization performance gap between known and unknown environments. Extensive experimental results on AI2-THOR benchmark dataset demonstrate that our model significantly outperforms the state-of-the-art model by %$15.79\%%$ for the SPL and by %$23.83\%%$ for the success rate. In addition, the exploration rate experiment shows that our model can effectively improve the invalid exploration behavior of the agent and accelerate the convergence speed of the model. Our implementation code and data can be viewed on https://github.com/zhiyu-tech/WHU-CVVMN. Context vector (dpeaa)DE-He213 Visual mapless navigation (dpeaa)DE-He213 Hierarchical semantic information (dpeaa)DE-He213 Meta-learning (dpeaa)DE-He213 Generalization (dpeaa)DE-He213 Guo, Chi aut Zhang, Huyin aut Luo, Binhan aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 9(2022), 2 vom: 04. Nov., Seite 2031-2041 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:9 year:2022 number:2 day:04 month:11 pages:2031-2041 https://dx.doi.org/10.1007/s40747-022-00902-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 2 04 11 2031-2041
spelling	10.1007/s40747-022-00902-7 doi (DE-627)SPR050101080 (SPR)s40747-022-00902-7-e DE-627 ger DE-627 rakwb eng Li, Fei verfasserin aut Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Visual mapless navigation (VMN), modeling a direct mapping between sensory inputs and agent actions, aims to navigate from a stochastic origin location to a prescribed goal in an unseen scene. A fundamental yet challenging issue in visual mapless navigation is generalizing to a new scene. Furthermore, it is of pivotal concern to design a method to make effective policy learning. To address these issues, we introduce a novel visual mapless navigation model, which integrates hierarchical semantic information represented by context vector with meta-learning to improve the generalization performance gap between known and unknown environments. Extensive experimental results on AI2-THOR benchmark dataset demonstrate that our model significantly outperforms the state-of-the-art model by %$15.79\%%$ for the SPL and by %$23.83\%%$ for the success rate. In addition, the exploration rate experiment shows that our model can effectively improve the invalid exploration behavior of the agent and accelerate the convergence speed of the model. Our implementation code and data can be viewed on https://github.com/zhiyu-tech/WHU-CVVMN. Context vector (dpeaa)DE-He213 Visual mapless navigation (dpeaa)DE-He213 Hierarchical semantic information (dpeaa)DE-He213 Meta-learning (dpeaa)DE-He213 Generalization (dpeaa)DE-He213 Guo, Chi aut Zhang, Huyin aut Luo, Binhan aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 9(2022), 2 vom: 04. Nov., Seite 2031-2041 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:9 year:2022 number:2 day:04 month:11 pages:2031-2041 https://dx.doi.org/10.1007/s40747-022-00902-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 2 04 11 2031-2041
allfields_unstemmed	10.1007/s40747-022-00902-7 doi (DE-627)SPR050101080 (SPR)s40747-022-00902-7-e DE-627 ger DE-627 rakwb eng Li, Fei verfasserin aut Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Visual mapless navigation (VMN), modeling a direct mapping between sensory inputs and agent actions, aims to navigate from a stochastic origin location to a prescribed goal in an unseen scene. A fundamental yet challenging issue in visual mapless navigation is generalizing to a new scene. Furthermore, it is of pivotal concern to design a method to make effective policy learning. To address these issues, we introduce a novel visual mapless navigation model, which integrates hierarchical semantic information represented by context vector with meta-learning to improve the generalization performance gap between known and unknown environments. Extensive experimental results on AI2-THOR benchmark dataset demonstrate that our model significantly outperforms the state-of-the-art model by %$15.79\%%$ for the SPL and by %$23.83\%%$ for the success rate. In addition, the exploration rate experiment shows that our model can effectively improve the invalid exploration behavior of the agent and accelerate the convergence speed of the model. Our implementation code and data can be viewed on https://github.com/zhiyu-tech/WHU-CVVMN. Context vector (dpeaa)DE-He213 Visual mapless navigation (dpeaa)DE-He213 Hierarchical semantic information (dpeaa)DE-He213 Meta-learning (dpeaa)DE-He213 Generalization (dpeaa)DE-He213 Guo, Chi aut Zhang, Huyin aut Luo, Binhan aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 9(2022), 2 vom: 04. Nov., Seite 2031-2041 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:9 year:2022 number:2 day:04 month:11 pages:2031-2041 https://dx.doi.org/10.1007/s40747-022-00902-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 2 04 11 2031-2041
allfieldsGer	10.1007/s40747-022-00902-7 doi (DE-627)SPR050101080 (SPR)s40747-022-00902-7-e DE-627 ger DE-627 rakwb eng Li, Fei verfasserin aut Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Visual mapless navigation (VMN), modeling a direct mapping between sensory inputs and agent actions, aims to navigate from a stochastic origin location to a prescribed goal in an unseen scene. A fundamental yet challenging issue in visual mapless navigation is generalizing to a new scene. Furthermore, it is of pivotal concern to design a method to make effective policy learning. To address these issues, we introduce a novel visual mapless navigation model, which integrates hierarchical semantic information represented by context vector with meta-learning to improve the generalization performance gap between known and unknown environments. Extensive experimental results on AI2-THOR benchmark dataset demonstrate that our model significantly outperforms the state-of-the-art model by %$15.79\%%$ for the SPL and by %$23.83\%%$ for the success rate. In addition, the exploration rate experiment shows that our model can effectively improve the invalid exploration behavior of the agent and accelerate the convergence speed of the model. Our implementation code and data can be viewed on https://github.com/zhiyu-tech/WHU-CVVMN. Context vector (dpeaa)DE-He213 Visual mapless navigation (dpeaa)DE-He213 Hierarchical semantic information (dpeaa)DE-He213 Meta-learning (dpeaa)DE-He213 Generalization (dpeaa)DE-He213 Guo, Chi aut Zhang, Huyin aut Luo, Binhan aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 9(2022), 2 vom: 04. Nov., Seite 2031-2041 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:9 year:2022 number:2 day:04 month:11 pages:2031-2041 https://dx.doi.org/10.1007/s40747-022-00902-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 2 04 11 2031-2041
allfieldsSound	10.1007/s40747-022-00902-7 doi (DE-627)SPR050101080 (SPR)s40747-022-00902-7-e DE-627 ger DE-627 rakwb eng Li, Fei verfasserin aut Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Visual mapless navigation (VMN), modeling a direct mapping between sensory inputs and agent actions, aims to navigate from a stochastic origin location to a prescribed goal in an unseen scene. A fundamental yet challenging issue in visual mapless navigation is generalizing to a new scene. Furthermore, it is of pivotal concern to design a method to make effective policy learning. To address these issues, we introduce a novel visual mapless navigation model, which integrates hierarchical semantic information represented by context vector with meta-learning to improve the generalization performance gap between known and unknown environments. Extensive experimental results on AI2-THOR benchmark dataset demonstrate that our model significantly outperforms the state-of-the-art model by %$15.79\%%$ for the SPL and by %$23.83\%%$ for the success rate. In addition, the exploration rate experiment shows that our model can effectively improve the invalid exploration behavior of the agent and accelerate the convergence speed of the model. Our implementation code and data can be viewed on https://github.com/zhiyu-tech/WHU-CVVMN. Context vector (dpeaa)DE-He213 Visual mapless navigation (dpeaa)DE-He213 Hierarchical semantic information (dpeaa)DE-He213 Meta-learning (dpeaa)DE-He213 Generalization (dpeaa)DE-He213 Guo, Chi aut Zhang, Huyin aut Luo, Binhan aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 9(2022), 2 vom: 04. Nov., Seite 2031-2041 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:9 year:2022 number:2 day:04 month:11 pages:2031-2041 https://dx.doi.org/10.1007/s40747-022-00902-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 2 04 11 2031-2041
language	English
source	Enthalten in Complex & intelligent systems 9(2022), 2 vom: 04. Nov., Seite 2031-2041 volume:9 year:2022 number:2 day:04 month:11 pages:2031-2041
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topic_facet	Context vector Visual mapless navigation Hierarchical semantic information Meta-learning Generalization
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authorswithroles_txt_mv	Li, Fei @@aut@@ Guo, Chi @@aut@@ Zhang, Huyin @@aut@@ Luo, Binhan @@aut@@
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author	Li, Fei
spellingShingle	Li, Fei misc Context vector misc Visual mapless navigation misc Hierarchical semantic information misc Meta-learning misc Generalization Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning
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topic_title	Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning Context vector (dpeaa)DE-He213 Visual mapless navigation (dpeaa)DE-He213 Hierarchical semantic information (dpeaa)DE-He213 Meta-learning (dpeaa)DE-He213 Generalization (dpeaa)DE-He213
topic	misc Context vector misc Visual mapless navigation misc Hierarchical semantic information misc Meta-learning misc Generalization
topic_unstemmed	misc Context vector misc Visual mapless navigation misc Hierarchical semantic information misc Meta-learning misc Generalization
topic_browse	misc Context vector misc Visual mapless navigation misc Hierarchical semantic information misc Meta-learning misc Generalization
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title	Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning
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title_full	Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning
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author_browse	Li, Fei Guo, Chi Zhang, Huyin Luo, Binhan
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title_sort	context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning
title_auth	Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning
abstract	Abstract Visual mapless navigation (VMN), modeling a direct mapping between sensory inputs and agent actions, aims to navigate from a stochastic origin location to a prescribed goal in an unseen scene. A fundamental yet challenging issue in visual mapless navigation is generalizing to a new scene. Furthermore, it is of pivotal concern to design a method to make effective policy learning. To address these issues, we introduce a novel visual mapless navigation model, which integrates hierarchical semantic information represented by context vector with meta-learning to improve the generalization performance gap between known and unknown environments. Extensive experimental results on AI2-THOR benchmark dataset demonstrate that our model significantly outperforms the state-of-the-art model by %$15.79\%%$ for the SPL and by %$23.83\%%$ for the success rate. In addition, the exploration rate experiment shows that our model can effectively improve the invalid exploration behavior of the agent and accelerate the convergence speed of the model. Our implementation code and data can be viewed on https://github.com/zhiyu-tech/WHU-CVVMN. © The Author(s) 2022
abstractGer	Abstract Visual mapless navigation (VMN), modeling a direct mapping between sensory inputs and agent actions, aims to navigate from a stochastic origin location to a prescribed goal in an unseen scene. A fundamental yet challenging issue in visual mapless navigation is generalizing to a new scene. Furthermore, it is of pivotal concern to design a method to make effective policy learning. To address these issues, we introduce a novel visual mapless navigation model, which integrates hierarchical semantic information represented by context vector with meta-learning to improve the generalization performance gap between known and unknown environments. Extensive experimental results on AI2-THOR benchmark dataset demonstrate that our model significantly outperforms the state-of-the-art model by %$15.79\%%$ for the SPL and by %$23.83\%%$ for the success rate. In addition, the exploration rate experiment shows that our model can effectively improve the invalid exploration behavior of the agent and accelerate the convergence speed of the model. Our implementation code and data can be viewed on https://github.com/zhiyu-tech/WHU-CVVMN. © The Author(s) 2022
abstract_unstemmed	Abstract Visual mapless navigation (VMN), modeling a direct mapping between sensory inputs and agent actions, aims to navigate from a stochastic origin location to a prescribed goal in an unseen scene. A fundamental yet challenging issue in visual mapless navigation is generalizing to a new scene. Furthermore, it is of pivotal concern to design a method to make effective policy learning. To address these issues, we introduce a novel visual mapless navigation model, which integrates hierarchical semantic information represented by context vector with meta-learning to improve the generalization performance gap between known and unknown environments. Extensive experimental results on AI2-THOR benchmark dataset demonstrate that our model significantly outperforms the state-of-the-art model by %$15.79\%%$ for the SPL and by %$23.83\%%$ for the success rate. In addition, the exploration rate experiment shows that our model can effectively improve the invalid exploration behavior of the agent and accelerate the convergence speed of the model. Our implementation code and data can be viewed on https://github.com/zhiyu-tech/WHU-CVVMN. © The Author(s) 2022
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title_short	Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning
url	https://dx.doi.org/10.1007/s40747-022-00902-7
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author2	Guo, Chi Zhang, Huyin Luo, Binhan
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doi_str	10.1007/s40747-022-00902-7
up_date	2024-07-03T13:22:59.865Z
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Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning

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