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Vision-and-Language Navigation (VLN) in real-world settings requires agents to process continuous visual streams and generate actions with low latency grounded in language instructions.
A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niessner, M. Savva, S. Song, A. Zeng, and Y. Zhang, “Matterport3D: Learning from RGB-D data in indoor environments,” International Conference on 3D Vision (3DV) , 2017
2017
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P. Anderson, Q. Wu, D. Teney, J. Bruce, M. Johnson, N. Sünderhauf, I. Reid, S. Gould, and A. Van Den Hengel, “Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018
2018
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H. Tan, L. Yu, and M. Bansal, “Learning to navigate unseen environments: Back translation with environmental dropout,” in Proceedings of NAACL-HLT , 2019
2019
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Z. Yu, J. Yu, Y. Cui, D. Tao, and Q. Tian, “Deep modular co-attention networks for visual question answering,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019
2019
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2020
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J. Krantz, E. Wijmans, A. Majundar, D. Batra, and S. Lee, “Beyond the nav-graph: Vision and language navigation in continuous environments,” in European Conference on Computer Vision (ECCV) , 2020
2020
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S. Chen, P.-L. Guhur, C. Schmid, and I. Laptev, “History aware multimodal transformer for vision-and-language navigation,” Advances in Neural Information Processing Systems , 2021
2021
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2021
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2021
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J. Krantz, A. Gokaslan, D. Batra, S. Lee, and O. Maksymets, “Waypoint models for instruction-guided navigation in continuous environments,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021
2021
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K. Chen, J. K. Chen, J. Chuang, M. Vázquez, and S. Savarese, “Topological planning with transformers for vision-and-language navigation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021
2021
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S. Chen, P.-L. Guhur, M. Tapaswi, C. Schmid, and I. Laptev, “Think global, act local: Dual-scale graph transformer for vision-and-language navigation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 537–16 547
2022
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G. Georgakis, K. Schmeckpeper, K. Wanchoo, S. Dan, E. Miltsakaki, D. Roth, and K. Daniilidis, “Cross-modal map learning for vision and language navigation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022
2022
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2022
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Y. Hong, Z. Wang, Q. Wu, and S. Gould, “Bridging the gap between learning in discrete and continuous environments for vision-and-language navigation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022
2022
Cited alongside, same era.
J. Krantz and S. Lee, “Sim-2-sim transfer for vision-and-language navigation in continuous environments,” in European Conference on Computer Vision , 2022
2022
Cited alongside, same era.
D. Azuma, T. Miyanishi, S. Kurita, and M. Kawanabe, “Scanqa: 3d question answering for spatial scene understanding,” in proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022
2022
Cited alongside, same era.
2023
Cited alongside, same era.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
J. Lin, H. Yin, W. Ping, P. Molchanov, M. Shoeybi, and S. Han, “Vila: On pre-training for visual language models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2024
2024
Later among the works it cites.
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Z. Wang, J. Li, Y. Hong, Y. Wang, Q. Wu, M. Bansal, S. Gould, H. Tan, and Y. Qiao, “Scaling data generation in vision-and-language navigation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Z. Wang, X. Li, J. Yang, Y. Liu, and S. Jiang, “Gridmm: Grid memory map for vision-and-language navigation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Z. Zhu, X. Ma, Y. Chen, Z. Deng, S. Huang, and Q. Li, “3d-vista: Pre-trained transformer for 3d vision and text alignment,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 2911–2921
2023
Cited alongside, same era.
Y. Hong, H. Zhen, P. Chen, S. Zheng, Y. Du, Z. Chen, and C. Gan, “3d-llm: Injecting the 3d world into large language models,” Advances in Neural Information Processing Systems , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Y. Li, C. Wang, and J. Jia, “Llama-vid: An image is worth 2 tokens in large language models,” in European Conference on Computer Vision , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
D. Zheng, S. Huang, L. Zhao, Y. Zhong, and L. Wang, “Towards learning a generalist model for embodied navigation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024
2024
Later among the works it cites.
J. Zhang, K. Wang, S. Wang, M. Li, H. Liu, S. Wei, Z. Wang, Z. Zhang, and H. Wang, “Uni-navid: A video-based vision-language-action model for unifying embodied navigation tasks,” Robotics: Science and Systems , 2025
2025
Closest in time.
A.-C. Cheng, Y. Ji, Z. Yang, Z. Gongye, X. Zou, J. Kautz, E. Bıyık, H. Yin, S. Liu, and X. Wang, “Navila: Legged robot vision-language-action model for navigation,” Robotics: Science and Systems , 2025
2025
Closest in time.