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Airbert: In-domain Pretraining for Vision-and-Language Navigation
Open accessAbstract
Vision-and-language navigation (VLN) aims to enable embodied agents to navigate in realistic environments using natural language instructions. Given the scarcity of domain-specific training data and the high diversity of image and language inputs, the generalization of VLN agents to unseen environments remains challenging. Recent methods explore pretraining to improve generalization, however, the use of generic image-caption datasets or existing small-scale VLN environments is suboptimal and results in limited improvements. In this work, we introduce BnB1, a large-scale and diverse in-domain VLN dataset. We first collect image-caption (IC) pairs from hundreds of thousands of listings from online rental marketplaces. Using IC pairs we next propose automatic strategies to generate millions of VLN path-instruction (PI) pairs. We further propose a shuffling loss that improves the learning of temporal order inside PI pairs. We use BnB to pretrain our Airbert2model that can be adapted to discriminative and generative settings and show that it outperforms state of the art for Room-to-Room (R2R) navigation and Remote Referring Expression (REVERIE) benchmarks. Moreover, our in-domain pretraining significantly increases performance on a challenging few-shot VLN evaluation, where we train the model only on VLN instructions from a few houses.
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“In contrast, GemBench [289], built upon RLBench, focuses on task generalization, incorporating zero-shot task setups and vision-language extensions to assess robotic performance in previously unseen tasks and objects.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [289]
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“While GemBench [289] presents unique advantages in multimodal fusion and generalization, it also remains constrained to simulation and lacks real-world validation.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [289]
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“Unlike VLMbench [290] and Gem-Bench [289], which primarily evaluate vision-language models (VLMs) in robotic control, VIMA [291] further integrates multi-modal task execution and precise instruction following, making it distinct in its benchmarking approach.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [289]
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Huang, X., Wang, Z. W., Zhou, W., Yang, K., Wen, K., Liu, H., Huang, S., & Lyu, M. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102560 · Cited by 8
MossVLN: Memory-Observation Synergistic System for Continuous Vision-Language Navigation
Yu, T., Wu, Y., Cui, Q., Huang, Q., & Yu, J. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3586105 · Cited by 10
Wang, T., Fan, J., Zheng, P., Yan, R., & Wang, L. (2025) · Engineering · doi:10.1016/j.eng.2025.04.028 · Cited by 6
Controllable Navigation Instruction Generation with Chain of Thought Prompting
Kong, X., Chen, J., Wang, W., Su, H., Hu, X., Yang, Y., & Liu, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73397-0_3 · Cited by 14
Incorporating External Knowledge Reasoning for Vision-and-Language Navigation with Assistant’s Help
Li, X., Zhang, Y., Yuan, W., & Luo, J. (2022) · Applied Sciences · doi:10.3390/app12147053 · Cited by 6
Multiple Visual Features in Topological Map for Vision-and-Language Navigation
Liu, R., Kong, P., & Zhang, W. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10803061 · Cited by 5
Multimodal spatial language maps for robot navigation and manipulation
Huang, C., Mees, O., Zeng, A., & Burgard, W. (2025) · The International Journal of Robotics Research · doi:10.1177/02783649251351658 · Cited by 8
Visual and Textual Commonsense-Enhanced Layout Learning for Vision-and-Language Navigation
Gao, F., Shi, L., Tang, J., Wang, J., Li, S., Ma, S., & Yu, J. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3604018 · Cited by 7
Hierarchical Spatial Proximity Reasoning for Vision-and-Language Navigation
Xu, M., & Xie, Z. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3477129 · Cited by 7
E2Map: Experience-and-Emotion Map for Self-Reflective Robot Navigation with Language Models
Kim, C., Kim, K., Oh, M., Baek, H., Lee, J.-Y., Jung, D.-J., Woo, S., Woo, Y., Tucker, J., Firoozi, R., et al. (2024) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra55743.2025.11128669 · Cited by 9
PANDA: Prompt-Based Context- and Indoor-Aware Pretraining for Vision and Language Navigation
Liu, T., Hu, Y., Wu, W., Wang, Y.-K., Xu, K., & Yin, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53305-1_15 · Cited by 5
AIGeN: An Adversarial Approach for Instruction Generation in VLN
Rawal, N., Bigazzi, R., Baraldi, L., & Cucchiara, R. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00212 · Cited by 6
CAVEN: An Embodied Conversational Agent for Efficient Audio-Visual Navigation in Noisy Environments
Liu, X., Paul, S., Chatterjee, M., & Cherian, A. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i4.28167 · Cited by 6
“…area (Anderson et al. 2018; Hong et al. 2021; Ke et al. 2019; Liu et al. 2021; Majumdar et al. 2020; Ma et al. 2019a,b; Zhu et al. 2020; Chen et al. 2021c; Pashevich et al. 2021; Guhur et al. 2021) in which the agent is given a language instruction at its start describing the intended path.”
BackgroundGroundingMate: Aiding Object Grounding for Goal-Oriented Vision-and-Language Navigation
Liu, Q., Zhang, S., Qiao, Y., Zhu, J., Li, X., Guo, L., Wang, Q., He, X., Wu, Q., & Liu, J. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00180 · Cited by 6
DOPE: Dual Object Perception-Enhancement Network for Vision-and-Language Navigation
Yu, Y., & Yang, D. (2025) · International Conference on Multimedia Retrieval · doi:10.1145/3731715.3733315 · Cited by 10
MatchNav: LLM-based enhanced description and instruction matching in vision-and-language navigation
Wang, C., Wei, S., & Juntong, Q. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103444 · Cited by 5
TransNav: spatial sequential transformer network for visual navigation
Zhou, K., Zhang, H., & Li, F. (2022) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwac084 · Cited by 4
monoVLN: Bridging the Observation Gap between Monocular and Panoramic Vision and Language Navigation
Lu, R., Zhou, Y., Cheng, H., Meng, J., & Zheng, W.-S. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00884 · Cited by 3
Zhang, J., & Ma, K. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801689 · Cited by 5
Bonyani, M., Soleymani, M., Odugu, O., & Wang, C. (2025) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case58245.2025.11163988 · Cited by 3
Improved VLN-BERT with Reinforcing Endpoint Alignment for Vision-and-Language Navigation
Jin, C., Yang, B., & Liu, R. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-6125-8_10 · Cited by 2
Multimodal Evolutionary Encoder for Continuous Vision-Language Navigation
He, Z., Wang, L., Chen, L., Li, S., Yan, Q., Liu, C., & Chen, Q. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802484 · Cited by 4
Metadata
- Authors
- Guhur, P.-L., Tapaswi, M., Chen, S., Laptev, I., & Schmid, C.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- International Institute of Information Technology, Hyderabad (IN)
- PSL Research University,Inria, École normale supérieure, CNRS,Paris,France
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 1614-1623
- OpenAlex
- W3195026654
- Cited by
- 139
- References
- 55
- Citations by year
- 2026: 11 · 2025: 39 · 2024: 38 · 2023: 29 · 2022: 22
- FWCI
- 7.28
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Funders
- Agence Nationale de la Recherche (ANR-19-P3IA-0001)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Keywords
- vision-and-language navigation, few-shot generalization, embodied agents
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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