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All issues Volume 30 / No 5 (October 2025) Wuhan Univ. J. Nat. Sci., 30 5 (2025) 405-426 References
Open Access
Issue
Wuhan Univ. J. Nat. Sci.
Volume 30, Number 5, October 2025
Page(s) 405 - 426
DOI https://doi.org/10.1051/wujns/2025305405
Published online 04 November 2025
  1. Wirtz J, Hofmeister J, Chew P Y P, et al. Digital service technologies, service robots, AI, and the strategic pathways to cost-effective service excellence[J]. The Service Industries Journal, 2023, 43(15/16): 1173-1196. [Google Scholar]
  2. Zhu Y K, Mottaghi R, Kolve E, et al. Target-driven visual navigation in indoor scenes using deep reinforcement learning[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). New York: IEEE, 2017: 3357-3364. [Google Scholar]
  3. Li F, Guo C, Luo B H, et al. Multi goals and multi scenes visual mapless navigation in indoor using meta-learning and scene priors[J]. Neurocomputing, 2021, 449: 368-377. [Google Scholar]
  4. Li F, Guo C, Zhang H Y, et al. Context vector-based visual mapless navigation in indoor using hierarchical semantic information and meta-learning[J]. Complex & Intelligent Systems, 2023, 9(2): 2031-2041. [Google Scholar]
  5. Sun J W, Wu J, Ji Z, et al. A survey of object goal navigation[J]. IEEE Transactions on Automation Science and Engineering, 2025, 22: 2292-2308. [Google Scholar]
  6. Li C S, Zhang R H, Wong J, et al. BEHAVIOR-1K: A benchmark for embodied AI with 1 000 everyday activities and realistic simulation[C]//Conference on Robot Learning. New York: PMLR, 2022: 80-93. [Google Scholar]
  7. Shadbolt N, Berners-Lee T, Hall W. IFIP International Conference on E-Business, E-Services, and E-Society[M]. Cham: Springer-Verlag, 2003. [Google Scholar]
  8. Ji S X, Pan S R, Cambria E, et al. A survey on knowledge graphs: Representation, acquisition, and applications[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(2): 494-514. [CrossRef] [PubMed] [Google Scholar]
  9. Guo C, Luo B, Li F, et al. Review and verification for brain-like navigation algorithm[J]. Geomatics and Information Science of Wuhan University, 2021, 46(12): 1819-1831. [Google Scholar]
  10. Epstein R A, Patai E Z, Julian J B, et al. The cognitive map in humans: Spatial navigation and beyond[J]. Nature Neuroscience, 2017, 20(11): 1504-1513. [Google Scholar]
  11. Sosa M, Giocomo L M. Navigating for reward[J]. Nature Reviews Neuroscience, 2021, 22(8): 472-487. [Google Scholar]
  12. Ambrose R E, Pfeiffer B E, Foster D J. Reverse replay of hippocampal place cells is uniquely modulated by changing reward[J]. Neuron, 2016, 91(5): 1124-1136. [Google Scholar]
  13. Bhattarai B, Lee J W, Jung M W. Distinct effects of reward and navigation history on hippocampal forward and reverse replays[J]. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117(1): 689-697. [Google Scholar]
  14. Banino A, Barry C, Uria B, et al. Vector-based navigation using grid-like representations in artificial agents[J]. Nature, 2018, 557: 429-433. [Google Scholar]
  15. Rummery G A, Niranjan M. On-line Q-learning Using Connectionist Systems[M]. Cambridge: University of Cambridge, 1994. [Google Scholar]
  16. Mnih V, Kavukcuoglu K, Silver D, et al. Playing atari with deep reinforcement learning[EB/OL]. [2013-12-19]. http://arxiv.org/abs/1312.5602. [Google Scholar]
  17. Williams R J. Simple statistical gradient-following algorithms for connectionist reinforcement learning[J]. Machine Learning, 1992, 8(3): 229-256. [Google Scholar]
  18. Mnih V, Badia A P, Mirza M, et al. Asynchronous methods for deep reinforcement learning[EB/OL]. [2016-06-16]. http://arxiv.org/abs/1602.01783. [Google Scholar]
  19. Devo A, Mezzetti G, Costante G, et al. Towards generalization in target-driven visual navigation by using deep reinforcement learning[J]. IEEE Transactions on Robotics, 2020, 36(5): 1546-1561. [Google Scholar]
  20. Gupta S, Tolani V, Davidson J, et al. Cognitive mapping and planning for visual navigation[J]. International Journal of Computer Vision, 2020, 128(5): 1311-1330. [Google Scholar]
  21. Mousavian A, Toshev A, Fišer M, et al. Visual representations for semantic target driven navigation[C]//2019 International Conference on Robotics and Automation (ICRA). New York: IEEE, 2019: 8846-8852. [Google Scholar]
  22. Anderson P, Chang A, Chaplot D S, et al. On evaluation of embodied navigation agents[EB/OL]. [2018-07-18]. http://arxiv.org/abs/1807.06757. [Google Scholar]
  23. He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2016: 770-778. [Google Scholar]
  24. Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. [CrossRef] [Google Scholar]
  25. Yang W, Wang X L, Farhadi A, et al. Visual semantic navigation using scene priors[EB/OL]. [2018-10-15]. http://arxiv.org/abs/1810.06543. [Google Scholar]
  26. Krishna R, Zhu Y K, Groth O, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations[J]. International Journal of Computer Vision, 2017, 123(1): 32-73. [Google Scholar]
  27. Du H M, Yu X, Zheng L. Learning object relation graph and tentative policy for visual navigation[C]//European Conference on Computer Vision. Cham: Springer, 2020: 19-34. [Google Scholar]
  28. Ren S Q, He K M, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. [CrossRef] [Google Scholar]
  29. Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: Optimal speed and accuracy of object detection[EB/OL]. [2020-04-23]. http://arxiv.org/abs/2004.10934. [Google Scholar]
  30. Yang J W, Lu J S, Lee S, et al. Graph R-CNN for scene graph generation[C]//European Conference on Computer Vision. Cham: Springer-Verlag, 2018: 690-706. [Google Scholar]
  31. Dang R H, Shi Z F, Wang L Y, et al. Unbiased directed object attention graph for object navigation[C]//Proceedings of the 30th ACM International Conference on Multimedia. New York: ACM, 2022: 3617-3627. [Google Scholar]
  32. Hu X B, Lin Y F, Wang S, et al. Agent-centric relation graph for object visual navigation[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2024, 34(2): 1295-1309. [Google Scholar]
  33. Li W, Song X, Bai Y, et al. ION: Instance-level object navigation[C]//Proceedings of the 29th ACM International Conference on Multimedia. New York: ACM, 2021: 4343-4352. [Google Scholar]
  34. Zhang S X, Song X H, Bai Y B, et al. Hierarchical object-to-zone graph for object navigation[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV). New York: IEEE, 2021: 15110-15120. [Google Scholar]
  35. Izadinia H, Sadeghi F, Farhadi A. Incorporating scene context and object layout into appearance modeling[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2014: 232-239. [Google Scholar]
  36. Zuo Z, Shuai B, Wang G, et al. Learning contextual dependence with convolutional hierarchical recurrent neural networks[J]. IEEE Transactions on Image Processing, 2016, 25(7): 2983-2996. [Google Scholar]
  37. Kuhn H W. The Hungarian method for the assignment problem[J]. Naval Research Logistics Quarterly, 1955, 2(1/2): 83-97. [CrossRef] [Google Scholar]
  38. Munkres J. Algorithms for the assignment and transportation problems[J]. Journal of the Society for Industrial and Applied Mathematics, 1957, 5(1): 32-38. [Google Scholar]
  39. Kwon O, Kim N, Choi Y, et al. Visual graph memory with unsupervised representation for visual navigation[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV). New York: IEEE, 2021: 15870-15879. [Google Scholar]
  40. Li J N, Zhou P, Xiong C M, et al. Prototypical contrastive learning of unsupervised representations[EB/OL]. [2021-03-30]. http://arxiv.org/abs/2005.04966. [Google Scholar]
  41. Nguyen T L, Nguyen D V, Le T H. Reinforcement learning based navigation with semantic knowledge of indoor environments[C]//2019 11th International Conference on Knowledge and Systems Engineering (KSE). New York: IEEE, 2019: 1-7. [Google Scholar]
  42. Zhou K, Guo C, Zhang H Y. Visual navigation via reinforcement learning and relational reasoning[C]//2021 IEEE SmartWorld, & , & , & , (/////). New York: IEEE, 2021: 131-138. [Google Scholar]
  43. Qiu Y D, Pal A, Christensen H I. Learning hierarchical relationships for object-goal navigation[EB/OL]. [2020-11-18]. http://arxiv.org/abs/2003.06749. [Google Scholar]
  44. Zhou K, Guo C, Guo W F, et al. Learning heterogeneous relation graph and value regularization policy for visual navigation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(11): 16901-16915. [Google Scholar]
  45. Lyu Y L, Talebi M S. Double graph attention networks for visual semantic navigation[J]. Neural Processing Letters, 2023, 55(7): 9019-9040. [Google Scholar]
  46. Dang R H, Wang L Y, He Z T, et al. Search for or navigate to dual adaptive thinking for object navigation[C]//2023 IEEE/CVF International Conference on Computer Vision (ICCV). New York: IEEE, 2023: 8216-8225. [Google Scholar]
  47. Yang B J, Yuan X F, Ying Z M, et al. HOGN-TVGN: Human-inspired embodied object goal navigation based on time-varying knowledge graph inference networks for robots[J]. Advanced Engineering Informatics, 2024, 62: 102671. [Google Scholar]
  48. Luo J, Cai B, Yu Y X, et al. Learning multimodal adaptive relation graph and action boost memory for visual navigation[J]. Advanced Engineering Informatics, 2024, 62: 102678. [Google Scholar]
  49. Singh K P, Salvador J, Weihs L, et al. Scene graph contrastive learning for embodied navigation[C]//2023 IEEE/CVF International Conference on Computer Vision (ICCV). New York: IEEE, 2023: 10850-10860. [Google Scholar]
  50. Xu N, Wang W, Yang R, et al. Aligning knowledge graph with visual perception for object-goal navigation[EB/OL]. [2024-04-26]. http://arxiv.org/abs/2402.18892. [Google Scholar]
  51. Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[EB/OL]. [2017-02-22]. http://arxiv.org/abs/1609.02907. [Google Scholar]
  52. Yun S, Jeong M, Kim R, et al. Graph transformer networks[EB/OL]. [2020-02-05]. http://arxiv.org/abs/1911.06455. [Google Scholar]
  53. Veličković P, Cucurull G, Casanova A, et al. Graph attention networks[EB/OL]. [2018-02-04]. http://arxiv.org/abs/1710.10903. [Google Scholar]
  54. Moghaddam M M K, Wu Q, Abbasnejad E, et al. Utilising prior knowledge for visual navigation: Distil and adapt[EB/OL]. [2020-12-06]. http://arxiv.org/abs/2004.03222. [Google Scholar]
  55. Lu Y, Chen Y R, Zhao D B, et al. MGRL: Graph neural network based inference in a Markov network with reinforcement learning for visual navigation[J]. Neurocomputing, 2021, 421: 140-150. [Google Scholar]
  56. Lyu Y L, Shi Y M, Zhang X G. Improving target-driven visual navigation with attention on 3D spatial relationships[J]. Neural Processing Letters, 2022, 54(5): 3979-3998. [Google Scholar]
  57. Moghaddam M K, Wu Q, Abbasnejad E, et al. Optimistic agent: Accurate graph-based value estimation for more successful visual navigation[C]//2021 IEEE Winter Conference on Applications of Computer Vision (WACV). New York: IEEE, 2021: 3732-3741. [Google Scholar]
  58. Wani S, Patel S, Jain U, et al. MultiON: Benchmarking semantic map memory using multi-object navigation[EB/OL]. [2020-12-07]. http://arxiv.org/abs/2012.03912. [Google Scholar]
  59. Ehsani K, Han W, Herrasti A, et al. ManipulaTHOR: A framework for visual object manipulation[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2021: 4495-4504. [Google Scholar]
  60. Xia F, Zamir A R, He Z Y, et al. Gibson Env: Real-world perception for embodied agents[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2018: 9068-9079. [Google Scholar]
  61. Savva M, Kadian A, Maksymets O, et al. Habitat: A platform for embodied AI research[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). New York: IEEE, 2019: 9338-9346. [Google Scholar]
  62. Kolve E, Mottaghi R, Han W, et al. AI2-THOR: An interactive 3D environment for visual AI[EB/OL]. [2022-08-26]. http://arxiv.org/abs/1712.05474. [Google Scholar]
  63. Chang A, Dai A, Funkhouser T, et al. Matterport3D: Learning from RGB-D data in indoor environments[EB/OL]. [2017-09-18]. http://arxiv.org/abs/1709.06158. [Google Scholar]
  64. Ramakrishnan S K, Gokaslan A, Wijmans E, et al. Habitat-matterport 3D dataset (HM3D): 1000 large-scale 3D environments for embodied AI[EB/OL]. [2021-09-16]. http://arxiv.org/abs/2109.08238. [Google Scholar]
  65. Deitke M, Han W, Herrasti A, et al. RoboTHOR: An open simulation-to-real embodied AI platform[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2020: 3161-3171. [Google Scholar]
  66. Deitke M, VanderBilt E, Herrasti A, et al. ProcTHOR: Large-scale embodied AI using procedural generation[EB/OL]. [2022-06-14]. http://arxiv.org/abs/2206.06994. [Google Scholar]
  67. Pennington J, Socher R, Manning C. Glove: Global vectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA: Association for Computational Linguistics, 2014: 1532-1543. [Google Scholar]
  68. Wu Q Y, Manocha D, Wang J, et al. NeoNav: Improving the generalization of visual navigation via generating next expected observations[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(6): 10001-10008. [Google Scholar]
  69. Wortsman M, Ehsani K, Rastegari M, et al. Learning to learn how to learn: Self-adaptive visual navigation using meta-learning[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2019: 6743-6752. [Google Scholar]
  70. Mayo B, Hazan T, Tal A. Visual navigation with spatial attention[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2021: 16893-16902. [Google Scholar]
  71. Du H M, Yu X, Zheng L. VTNet: Visual transformer network for object goal navigation[EB/OL]. [2021-05-20]. http://arxiv.org/abs/2105.09447. [Google Scholar]
  72. Carion N, Massa F, Synnaeve G, et al. End-to-end object detection with transformers[C]//European Conference on Computer Vision. Cham: Springer-Verlag, 2020: 213-229. [Google Scholar]
  73. Zhou K, Guo C, Zhang H Y, et al. Optimal graph transformer viterbi knowledge inference network for more successful visual navigation[J]. Adv Eng Informatics, 2023, 55: 101889. [Google Scholar]
  74. Forney G D. The Viterbi algorithm[J]. Proceedings of the IEEE, 1973, 61(3): 268-278. [Google Scholar]
  75. Sun Q R, Liu Y Y, Chua T S, et al. Meta-Transfer learning for few-shot learning[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2019: 403-412. [Google Scholar]
  76. Kirillov A, Mintun E, Ravi N, et al. Segment anything[C]//2023 IEEE/CVF International Conference on Computer Vision (ICCV). New York: IEEE, 2023: 3992-4003. [Google Scholar]
  77. Strader J, Hughes N, Chen W, et al. Indoor and outdoor 3D scene graph generation via language-enabled spatial ontologies[J]. IEEE Robotics and Automation Letters, 2024, 9(6): 4886-4893. [Google Scholar]
  78. Gervet T, Chintala S, Batra D, et al. Navigating to objects in the real world[J]. Science Robotics, 2023, 8(79): eadf6991. [Google Scholar]
  79. NVIDIA. Nvidia Isaac Sim: Robotics simulation and synthetic data[EB/OL]. [2023-10-25]. https://developer.nvidia.com/isaac-sim. [Google Scholar]

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