MapNav: A Novel Memory Representation via Annotated Semantic Maps for VLM-based Vision-and-Language Navigation (2025.acl-long)
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Lingfeng Zhang, Xiaoshuai Hao, Qinwen Xu, Qiang Zhang, Xinyao Zhang, Pengwei Wang, Jing Zhang, Zhongyuan Wang, Shanghang Zhang, Renjing Xu
| Challenge: | Vision-language navigation (VLN) is a key task in Embodied AI . traditional approaches rely on historical observations as spatio-temporal contexts for decision making . |
| Approach: | They propose a vision-language navigation model that leverages an annotation system to replace historical frames. |
| Outcome: | The proposed model can be used as a new memory representation method in vision-language navigation . it can be applied to simulated and real-world environments, and it is validated by experiments . |
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Weichen Zhang, Chen Gao, Shiquan Yu, Ruiying Peng, Baining Zhao, Qian Zhang, Jinqiang Cui, Xinlei Chen, Yong Li
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LangNav: Language as a Perceptual Representation for Navigation (2024.findings-naacl)
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| Challenge: | Existing approaches to vision-and-language navigation use visual features as the perceptual representation of a visual representation of an agent's egocentric panoramic view. |
| Approach: | They propose to use off-the-shelf vision systems to convert an agent’s egocentric panoramic view into natural language descriptions. |
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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. |
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Breaking Down and Building Up: Mixture of Skill-Based Vision-and-Language Navigation Agents (2026.acl-long)
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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. |
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| Challenge: | Existing work on vision-and-language navigation focuses on spatial reasoning and semantic grounding of visual information, but there is still scope for improvement. |
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| Outcome: | The proposed model can reason about the effect of the next action and the next on the destination to a certain extent. |
Synapse: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation (2026.findings-acl)
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Hanqi Jiang, Junhao Chen, Yi Pan, Ling Chen, Weihang You, Yifan Zhou, Ruidong Zhang, Yohannes Abate, Tianming Liu
| Challenge: | Large Language Models excel at generalized reasoning, but lack the ability to accumulate experiences and maintain narrative coherence over long horizons. |
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| Challenge: | Vision-language models often generate excessive visual tokens, leading to poor performance . a novel training-free visual token pruning method is proposed to improve performance despite the computational cost associated with VLMs. |
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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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NavRAG: Generating User Demand Instructions for Embodied Navigation through Retrieval-Augmented LLM (2025.findings-acl)
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| Challenge: | High-performance vision-and-language navigation models require large amounts of training data, the high cost of manual annotating has seriously hindered this field. |
| Approach: | They propose a retrieval-augmented generation framework that generates user demand instructions for vision-and-language navigation. |
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LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning (2024.findings-emnlp)
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| Challenge: | Existing path planning algorithms suffer from significant computational and memory inefficiencies as the state space grows . large language models excel in environmental analysis but fall short in detailed spatial and temporal reasoning . |
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