Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout (N19-1)
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| Challenge: | Existing approaches perform significantly worse in unseen environments compared to seen ones. |
| Approach: | They propose to use a ‘environmental dropout’ method to generate unseen triplets to generate new paths and instructions to generalize the agent. |
| Outcome: | The proposed agent outperforms the state-of-the-art approaches on the private unseen test set and is ranked top on the leaderboard. |
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| Challenge: | Using multilingual instructions to learn a better cross-lingual representation is challenging for multilingual agents. |
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| Challenge: | Despite significant advances, few previous works are able to fully utilize the strong correspondence between visual and textual sequences. |
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| Challenge: | Language models are a new standard to build state-of-the-art NLP systems. |
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Robust Navigation with Language Pretraining and Stochastic Sampling (D19-1)
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Can MLLMs Find Their Way in a City? Exploring Emergent Navigation from Web-Scale Knowledge (2026.eacl-long)
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| Challenge: | Existing evaluation benchmarks for multimodal large language models (MLLMs) are language-centric or heavily reliant on simulated environments, rarely probing the nuanced, knowledge-intensive reasoning essential for practical, real-world scenarios. |
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EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning (2024.eacl-long)
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Kinjal Basu, Keerthiram Murugesan, Subhajit Chaudhury, Murray Campbell, Kartik Talamadupula, Tim Klinger
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| Challenge: | Existing large language models struggle to support numerous low-resource languages . Existing models lack sufficient training data for effective parameter updating . |
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Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation (D18-1)
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Xiaoxue Zang, Ashwini Pokle, Marynel Vázquez, Kevin Chen, Juan Carlos Niebles, Alvaro Soto, Silvio Savarese
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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. |
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