Envedit: Environment Editing for Vision-and-Language Navigation
Open accessAbstract
In Vision-and-Language Navigation (VLN), an agent needs to navigate through the environment based on nat-ural language instructions. Due to limited available data for agent training and finite diversity in navigation environments, it is challenging for the agent to generalize to new, unseen environments. To address this problem, we propose Envedit, a data augmentation method that cre-ates new environments by editing existing environments, which are used to train a more generalizable agent. Our augmented environments can differ from the seen environ-ments in three diverse aspects: style, object appearance, and object classes. Training on these edit-augmented environments prevents the agent from overfitting to existing en-vironments and helps generalize better to new, unseen en-vironments. Empirically, on both the Room-to-Room and the multi-lingual Room-Across-Room datasets, we show that our proposed Envedit method gets significant im-provements in all metrics on both pre-trained and non-pre-trained VLN agents, and achieves the new state-of-the-art on the test leaderboard. We further ensemble the VLN agents augmented on different edited environments and show that these edit methods are complementary.11Code and data are available at https://github.com/jialuli-luka/EnvEdit.
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KitchenShift is useful for benchmarking imitation learning and reinforcement learning but does not support real-world reproducibility; its domain shift testing provides insight into real-world uncertainty modeling.
“KitchenShift [292] is particularly useful for benchmarking imitation learning (IL) and reinforcement learn-ing (RL) strategies, but like RLBench [288], it does not support real-world reproducibility, though its domain shift testing provides insight into real-world uncertainty modeling.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [292]
Isaac Sim provides high-fidelity physics, is ideal for reinforcement learning, imitation learning, and robotic vision, and is used in KitchenShift and GenSim2.
“It provides high-fidelity physics simulation, making it ideal for reinforcement learning (RL), imitation learning (IL), and robotic vision tasks, and is widely used in KitchenShift [292] and GenSim2 [295] benchmarks.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [292]
KitchenShift focuses on robot generalization under environmental domain shifts in a simulated kitchen, introducing shifts in object materials, lighting, and camera angles to evaluate policy robustness.
“In contrast, KitchenShift [292] focuses on robot generalization under environmental domain shifts, simulating a kitchen environment where the robot interacts with various objects, such as opening an oven, moving utensils, and mixing liquids.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [292]
Applying CLIP ViT-H/14 and EnvEdit to our approach leads to 80% SR.
“we also experiment with applying a more powerful visual encoder, CLIP ViT-H/14 [64], and the image augmentation method EnvEdit [47] to our approach, leading to a remarkable 80% SR”
From Scaling Data Generation in Vision-and-Language Navigation (2023) · cites this paper as [47]
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Multimodal fusion and vision–language models: A survey for robot vision
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“(2) Modality-Specific Encoders, which designs special encoders for different modalities, using Transformer for text, CNN for image, RNN for audio, etc., and then performs feature alignment through Cross-modal Attention [325, 326].”
Kamath, A., Anderson, P., Wang, S., Koh, J. Y., Ku, A., Waters, A., Yang, Y., Baldridge, J., & Parekh, Z. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01041 · Cited by 39 · see citing works
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Learning from Unlabeled 3D Environments for Vision-and-Language Navigation
Chen, S., Guhur, P.-L., Tapaswi, M., Schmid, C., & Laptev, I. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19842-7_37 · Cited by 35 · see citing works
Embodied navigation with multi-modal information: A survey from tasks to methodology
Wu, Y., Zhang, P., Gu, M., Zheng, J., & Bai, X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102532 · Cited by 34 · see citing works
Learning Vision-and-Language Navigation from YouTube Videos
Lin, K., Chen, P., Huang, D.-W., Li, T. H., Tan, M., & Gan, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00764 · Cited by 30 · see citing works
Improving Vision-and-Language Navigation by Generating Future-View Image Semantics
Li, J., & Bansal, M. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01040 · Cited by 28 · see citing works
Self-citationMemory-Adaptive Vision-and-Language Navigation
He, K., Jing, Y., Huang, Y., Lu, Z., An, D. U., & Wang, L. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110511 · Cited by 26 · see citing works
Self-Supervised 3-D Semantic Representation Learning for Vision-and-Language Navigation
Tan, S., Sima, K., Wang, D., Ge, M., Guo, D., & Liu, H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3395633 · Cited by 24 · see citing works
A Dual Semantic-Aware Recurrent Global-Adaptive Network for Vision-and-Language Navigation
Wang, L., He, Z., Tang, J., Dang, R., Wang, N., Liu, C., & Chen, Q. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/164 · Cited by 24 · see citing works
VLN-PETL: Parameter-Efficient Transfer Learning for Vision-and-Language Navigation
Qiao, Y., Zheng, Y. H., & Wu, Q. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01416 · Cited by 20 · see citing works
Zhao, C. K., Qi, Y., & Wu, Q. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611736 · Cited by 18 · see citing works
Grounded Entity-Landmark Adaptive Pre-training for Vision-and-Language Navigation
Cui, Y., Xie, L., Zhang, Y., Zhang, M., Yan, Y., & Yin, E. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01106 · Cited by 19 · see citing works
Boosting Efficient Reinforcement Learning for Vision-and-Language Navigation With Open-Sourced LLM
Wang, J., Wang, T., Cai, W., Xu, L., & Sun, C. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3511402 · Cited by 19 · see citing works
Constraint-Aware Zero-Shot Vision-Language Navigation in Continuous Environments
Chen, K., An, D. U., Huang, Y., Xu, R., Su, Y., Ling, Y., Reid, I. D., & Wang, L. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3594204 · Cited by 41 · see citing works
Qiao, Y., Lyu, W., Wang, H. Z., Wang, Z., Li, Z., Zhang, Y., Tan, M., & Wu, Q. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127584 · Cited by 38 · see citing works
ESceme: Vision-and-Language Navigation with Episodic Scene Memory
Zheng, Q., Liu, D., Wang, C., Zhang, J., Wang, D., & Tao, D. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02159-8 · Cited by 15 · see citing works
PASTS: Progress-aware spatio-temporal transformer speaker for vision-and-language navigation
Wang, L., Liu, C., He, Z., Li, S., Yan, Q., Chen, H., & Chen, Q. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107487 · Cited by 16 · see citing works
VLN-Video: Utilizing Driving Videos for Outdoor Vision-and-Language Navigation
Li, J., Padmakumar, A., Sukhatme, G. S., & Bansal, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29813 · Cited by 15 · see citing works
Self-citationTowards real-time embodied AI agent: a bionic visual encoding framework for mobile robotics
Hou, X.-Y., Guan, Y.-J., Han, T., & Wang, C. (2024) · International Journal of Intelligent Robotics and Applications · doi:10.1007/s41315-024-00363-w · Cited by 14 · see citing works
Wang, S., Zhou, D., Xie, L., Xu, C., Yan, Y., & Yin, E. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107320 · Cited by 11 · see citing works
Wang, J., Wang, T., Xu, L., He, Z., & Sun, C. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3398300 · Cited by 8 · see citing works
LLM as Copilot for Coarse-Grained Vision-and-Language Navigation
Qiao, Y., Liu, Q., Liu, J., Liu, J., & Wu, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72652-1_27 · Cited by 12 · see citing works
3D Gaussian Map with Open-Set Semantic Grouping for Vision-Language Navigation
Gao, J., Liu, R., & Wang, W. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00864 · Cited by 7 · see citing works
Behavioral Analysis of Vision-and-Language Navigation Agents
Yang, Z., Majumdar, A., & Lee, S. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00253 · Cited by 7 · see citing works
Navigating Beyond Instructions: Vision-and-Language Navigation in Obstructed Environments
Hong, H., Wang, S., Huang, Z. H., Wu, Q., & Liu, J. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681640 · Cited by 10 · see citing works
PRET: Planning with Directed Fidelity Trajectory for Vision and Language Navigation
Lu, R., Meng, J., & Zheng, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72848-8_5 · Cited by 6 · see citing works
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 7 · see citing works
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 · see citing works
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 · see citing works
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 6 · see citing works
Wang, L., Tang, J., He, Z., Dang, R., Liu, C., & Chen, Q. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801563 · Cited by 5 · see citing works
GroundingMate: 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 · see citing works
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 4 · see citing works
CLEAR: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations
Li, J., Tan, H., & Bansal, M. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.48 · Cited by 5 · see citing works
Self-citationmonoVLN: 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 · see citing works
Zhang, J., & Ma, K. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801689 · Cited by 5 · see citing works
ULN: Towards Underspecified Vision-and-Language Navigation
Feng, W., Fu, T.-J., Lu, Y., & Wang, W. Y. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.429 · Cited by 3 · see citing works
Narrowing the Gap between Vision and Action in Navigation
Zhang, Y., & Kordjamshidi, P. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681150 · Cited by 4 · see citing works
REGNav: Room Expert Guided Image-Goal Navigation
Li, P., Wu, K., Fu, J., & Zhou, S. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i5.32514 · Cited by 5 · see citing works
Kong, P., Liu, R., Xie, Z., & Pang, Z. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127961 · Cited by 2 · see citing works
A comprehensive review of recent advancements in vision-and-language navigation
Khan, J. T., Aafaq, N., Ali, Q., & Mohsin, M. (2026) · Discover Computing · doi:10.1007/s10791-026-09977-z · Cited by 4 · see citing works
Tree-Structured Trajectory Encoding for Vision-and-Language Navigation
Zhou, X., & Mu, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25494 · Cited by 1 · see citing works
GMM-searcher: efficient object search in large-scale scenes using large language models
Zheng, L., Mei, R., Zou, B., Zhao, Z., Wei, M., & Siliu, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-00788-8 · Cited by 1 · see citing works
History-Guided Prompt Generation for Vision-and-Language Navigation
Guo, W. Q., Wang, Z., Hu, Y., & Gao, J. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3613147 · Cited by 1 · see citing works
Liu, R., Wu, S., Lin, D., & Zhang, W. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247004 · Cited by 2 · see citing works
Observation-Graph Interaction and Key-Detail Guidance for Vision and Language Navigation
Xie, Y., Ou, B., Ma, F., & Liu, Y. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247072 · Cited by 1 · see citing works
MVL-Loc: Leveraging Vision-Language Model for Generalizable Multi-Scene Camera Relocalization
Xiao, Z., Yang, S., Ji, S., Yin, J., Wen, Z., & Wu, W. (2025) · Applied Sciences · doi:10.3390/app152312642 · Cited by 1 · see citing works
FlexVLN: Flexible Adaptation for Diverse Vision-and-Language Navigation Tasks
Zhang, S., Qiao, Y., Wang, Q., Guo, L., Wei, Z., & Liu, J. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3581809 · Cited by 1 · see citing works
ThinkMatter: Panoramic-Aware Instructional Semantics for Monocular Vision-and-Language Navigation
Dai, G., Wang, S., Zhao, H., Zhu, B., Sun, Q., & Shu, X. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3652003 · Cited by 1 · see citing works
Fine-Grained Alignment Supervision Matters in Vision-and-Language Navigation
He, K., Huang, Y., Jing, Y., Wu, Q., & Wang, L. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3658949 · Cited by 1 · see citing works
Yuan, Z., Tang, P., Sang, X., Zhang, F., & Zhang, Z. (2024) · The Visual Computer · doi:10.1007/s00371-024-03469-1 · Cited by 1 · see citing works
CLIP feature-based randomized control using images and text for multiple tasks and robots
Shibata, K., Deguchi, H., & Taguchi, S. (2024) · Advanced Robotics · doi:10.1080/01691864.2024.2379381 · Cited by 2 · see citing works
Model Adaptation for Time Constrained Embodied Control
Song, J., Yoo, M., & Woo, H. (2024) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52733.2024.01561
RewardVLN: AnImproved Agent Navigation Based On Visual-Instruction Alignment*
Liu, R., Kong, P., Wu, S., & Zhang, W. (2024) · International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm62033.2024.10715935
Pandey, A., & Parihar, A. S. (2025) · International Conference on Inventive Computing and Informatics (ICICI) · doi:10.1109/icici65870.2025.11069974
Landmark-Guided Knowledge for Vision-and-Language Navigation
Yang, D., Zhu, M., & Yu, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9856-1_35
Wu, S., Liu, R., Xie, Z., & Pang, Z. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127282
Process Adaptive Learning for Visual-Language Navigation
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Language-Conditioned Waypoint Predictor for Continuous Vision-and-Language Navigation
Wang, Z., Qi, Y., An, D. U., Xu, Y., Li, H., & Zhang, Z. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209085
Vision-Language Navigation with Continual Learning for Unseen Environments
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SDA-LLM: Spatial DisAmbiguation via Multi-turn Vision-Language Dialogues for Robot Navigation
Chen, K.-L., Wei, T.-T., Lee, M.-L., Yeh, L.-T., Kao, E., Tseng, Y., & Chen, J. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246115
Error-Driven Scene Editing for 3D Grounding in Large Language Models
Zhang, Y., Wang, Z., Lin, H., Li, J., Yang, J., Bitton, Y., Szpektor, I., & Bansal, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37029-7_24
Self-citationSPENav: Dynamic Object Filtering With Spatial Perception Enhancement for Vision-Language Navigation
Yuan, S., Zhang, H., Liu, L., Zhu, L., & Dong, X. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3651320
Yang, Y., Wang, T., Pan, M., Zhang, Y., Yue, Y., & Fu, M. (2026) · Science China Information Sciences · doi:10.1007/s11432-025-4852-4
Enhancing Memory Ability of LLM Agent for Vision-and-Language Navigation
Bao, Y., Wang, J., & Wang, T. (2025) · China Automation Congress (CAC) · doi:10.1109/cac67268.2025.11486783
Gao, P., Wang, P., Wang, F., Fujita, H., & Aljuaid, H. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00549-1
Xi, Y., Liu, S., Pang, B. J., Zhou, T., Song, Y. H., Yuan, X., & Xu, Q. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3703976
Hierarchical Task Reasoning for Aerial Vision-and-Language Navigation
Zhou, L., Xue, R., & Luo, X. (2026) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2026.3709109
Wang, L., Sheng, K., He, Z., Li, J., Qin, Y., Dai, H., Wang, X. F., Yang, J., Yan, Q., Liu, C., et al. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3711284
Self-citationDCSEN: Dual Channel Semantic Enhancement for Vision-and-Language Navigation
Li, H., Liu, L., Dong, X., Zhu, L., Chang, X., & Zhang, H. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3714818
Yao, X., Gao, J., & Xu, C. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3728426
Yan, Y., Li, P., & Yin, J. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.117088
Trajectory-Diversity-Driven Robust Vision-And-Language Navigation
Li, J., Wan, C., Dong, S., Ding, C., Wang, Q., Ma, Z., & Gong, Y. (2026) · doi:10.1109/cvprf71909.2026.00877
Self-citation
- Authors
- Li, J., Tan, H., & Bansal, M.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 15386-15396
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312434279
- Cited by
- 87
- References
- 72
- Citations by year
- 2026: 14 · 2025: 26 · 2024: 26 · 2023: 16 · 2022: 5
- FWCI
- 6.62
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1846185, CAREER)
- Defense Advanced Research Projects Agency (FA8750-19-2-1004, FA8750)
- Office of Naval Research (N000141812871)
- Army Research Office (W911NF2110220)
- 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 › Automotive Engineering › Spatial Cognition and Navigation
- Keywords
- vision-and-language navigation, data augmentation, generalization to unseen environments, overfitting
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