| Challenge: | Existing vision-and-language navigation models are brittle to multi-level language underspecification. |
| Approach: | They propose to use multi-level underspecified instructions to guide agents . they propose to learn GSS for navigation agent to ground multi- level instructions . experimental results show existing VLN models are still brittle to multi-language underspecification . |
| Outcome: | Experimental results show that the proposed framework outperforms baselines on ULN by 10% relative success rate across all levels. |
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| Challenge: | Vision-and-Language Navigation (VLN) is a subfield of embodied AI that integrates natural language understanding, visual perception, and sequential decision-making to allow autonomous agents to navigate and interact within visual environments. |
| Approach: | They propose a modular framework that introduces structured, skill-based reasoning into Transformer-based VLN agents. |
| Outcome: | The proposed framework decomposes navigation into atomic skills handled by a specialized agent. |
Sub-Instruction Aware Vision-and-Language Navigation (2020.emnlp-main)
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| Challenge: | Despite significant advances, few previous works are able to fully utilize the strong correspondence between visual and textual sequences. |
| Approach: | They propose to provide agents with fine-grained annotations during training and provide them with sub-instructions and their corresponding paths. |
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Navigating the Nuances: A Fine-grained Evaluation of Vision-Language Navigation (2024.findings-emnlp)
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| Challenge: | a new evaluation framework for vision-language navigation is proposed . current evaluation standards hinge on endpoint success rates and path alignment metrics . |
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Diagnosing Vision-and-Language Navigation: What Really Matters (2022.naacl-main)
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Wanrong Zhu, Yuankai Qi, Pradyumna Narayana, Kazoo Sone, Sugato Basu, Xin Wang, Qi Wu, Miguel Eckstein, William Yang Wang
| Challenge: | Existing models claim to be able to align object tokens with specific visual targets, but there are non-negligible gaps between the two. |
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| Outcome: | The results show that indoor and outdoor navigation agents refer to object and direction tokens when making decisions. |
CLEAR: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations (2022.findings-naacl)
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| Challenge: | Using multilingual instructions to learn a better cross-lingual representation is challenging for multilingual agents. |
| Approach: | They propose to use multilingual instructions to learn a shared cross-lingual language representation for the three languages in a Room-Across-Room dataset. |
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Follow the Beaten Path: The Role of Route Patterns on Vision-Language Navigation Agents Generalization Abilities (2025.naacl-long)
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| Challenge: | Vision and language navigation (VLN) is a challenging task towards the creation of embodied agents. |
| Approach: | They propose a solution that combines visual and linguistic features to enable VLN . they propose augmentation of the training data to fill the gap in missing patterns . |
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Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor Areas (2022.acl-long)
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| Challenge: | Recent work on visual-grounded navigation has focused on indoor scenarios with sharp drops in performance when testing on unseen data. |
| Approach: | They focus on visual agent navigation in outdoor scenarios with panorama images . they find that most gain in outdoor VLN on unseen data is due to specific features . |
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GesNavi: Gesture-guided Outdoor Vision-and-Language Navigation (2024.eacl-srw)
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| Challenge: | Existing datasets for outdoor Vision-and-Language Navigation (VLN) tasks do not include verbal instructions for communicating with mobility. |
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Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions (2022.acl-long)
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| Challenge: | Vision-and-Language Navigation (VLN) is a research topic that is gaining attention in the field of artificial intelligence. |
| Approach: | They propose to build an embodied agent that can communicate with humans in natural language and navigate in real 3D environments. |
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Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation (P19-1)
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| Challenge: | Existing models that ground language into visual appearance and route structure are outperforming their visual counterparts in unseen new environments. |
| Approach: | They propose to decompose the grounding procedure into a set of expert models with access to different modalities and ensemble them at prediction time. |
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