NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation
Not on the map. Why?
Cited by 35 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Vision-and-language navigation (VLN) stands as a key research problem of Embodied AI, aiming at enabling agents to navigate in unseen environments following linguistic instructions. In this field, generalization is a long-standing challenge, either to out-of-distribution scenes or from Sim to Real. In this paper, we propose NaVid, a video-based large vision language model (VLM), to mitigate such a generalization gap. NaVid makes the first endeavor to showcase the capability of VLMs to achieve state-of-the-art level navigation performance without any maps, odometers, or depth inputs. Following human instruction, NaVid only requires an on-the-fly video stream from a monocular RGB camera equipped on the robot to output the next-step action. Our formulation mimics how humans navigate and naturally gets rid of the problems introduced by odometer noises, and the Sim2Real gaps from map or depth inputs. Moreover, our video-based approach can effectively encode the historical observations of robots as spatio-temporal contexts for decision making and instruction following. We train NaVid with 510k navigation samples collected from continuous environments, including action-planning and instruction-reasoning samples, along with 763k large-scale web data. Extensive experiments show that NaVid achieves state-of-the-art performance in simulation environments and the real world, demonstrating superior cross-dataset and Sim2Real transfer. We thus believe our proposed VLM approach plans the next step for not only the navigation agents but also this research field.
Key facts. Cited by 222 papers in the map (data through 7 Oct 2026). Builds on 98 of its 118 references that are in the map. First posted on arXiv 24 Feb 2024.
Builds on
98 of 118 references.
References · 98
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- 2023Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulationcited by 353
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- 2021History Aware Multimodal Transformer for Vision-and-Language Navigationcited by 268
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50 most cited of 98 All 98 (JSON)
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Citing papers per year: 2024: 31; 2025: 82; 2026: 109.
222 papers.
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- 2025UniVLA: Learning to Act Anywhere with Task-centric Latent Actionscited by 386
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38 more
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- 2025FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysiscited by 21
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- 2024SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of Expertscited by 16
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50 most cited of 222 All 222 (JSON)
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- 2026RECAST: Recasting Vision-Language Semantics into an Actionable Cost Map for Robot Navigation26 Sep 2026
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50 newest of 222 All 222 (JSON)
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12 most cited of 27 All 27 (JSON)
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12 newest of 27 All 27 (JSON)
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12 most cited of 23 All 23 (JSON)
- 2026PanoVLN: Towards Effective Panoramic Vision-and-Language Navigation28 Sep 2026
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- 2026UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City27 Aug 2026
- 2026Dynamic Context Adapters: Efficiently Infusing History into Vision-and-Language Models11 Aug 2026
- 2026WNM-3D: A World Navigation Model with 3D Scene Conditioning for Closed-Loop VLN7 Aug 2026
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- 2026Guided Diffusion with Distilled Vision-Language Reliability for Aerial Navigation11 Jun 2026
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- 2026PROSPECT: Unified Streaming Vision-Language Navigation via Semantic--Spatial Fusion and Latent Predictive Representation4 Mar 2026
12 newest of 23 All 23 (JSON)
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12 most cited of 22 All 22 (JSON)
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12 most cited of 13 All 13 (JSON)
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- 2026SuperNav: An Agentic Navigation System for Any Task in Any Scene8 Oct 2026
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