LCLA: Language-Conditioned Latent Alignment for Vision-Language Navigation
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917262562164736 |
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| author | Subedi, Nitesh Haroon, Adam Tetteh, Samuel Koirala, Prajwal Fleming, Cody Sarkar, Soumik |
| author_facet | Subedi, Nitesh Haroon, Adam Tetteh, Samuel Koirala, Prajwal Fleming, Cody Sarkar, Soumik |
| contents | We propose LCLA (Language-Conditioned Latent Alignment), a framework for vision-language navigation that learns modular perception-action interfaces by aligning sensory observations to a latent representation of an expert policy. The expert is first trained with privileged state information, inducing a latent space sufficient for control, after which its latent interface and action head are frozen. A lightweight adapter is then trained to map raw visual-language observations, via a frozen vision-language model, into the expert's latent space, reducing the problem of visuomotor learning to supervised latent alignment rather than end-to-end policy optimization. This decoupling enforces a stable contract between perception and control, enabling expert behavior to be reused across sensing modalities and environmental variations. We instantiate LCLA and evaluate it on a vision-language indoor navigation task, where aligned latent spaces yield strong in-distribution performance and robust zero-shot generalization to unseen environments, lighting conditions, and viewpoints while remaining lightweight at inference time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07629 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LCLA: Language-Conditioned Latent Alignment for Vision-Language Navigation Subedi, Nitesh Haroon, Adam Tetteh, Samuel Koirala, Prajwal Fleming, Cody Sarkar, Soumik Robotics We propose LCLA (Language-Conditioned Latent Alignment), a framework for vision-language navigation that learns modular perception-action interfaces by aligning sensory observations to a latent representation of an expert policy. The expert is first trained with privileged state information, inducing a latent space sufficient for control, after which its latent interface and action head are frozen. A lightweight adapter is then trained to map raw visual-language observations, via a frozen vision-language model, into the expert's latent space, reducing the problem of visuomotor learning to supervised latent alignment rather than end-to-end policy optimization. This decoupling enforces a stable contract between perception and control, enabling expert behavior to be reused across sensing modalities and environmental variations. We instantiate LCLA and evaluate it on a vision-language indoor navigation task, where aligned latent spaces yield strong in-distribution performance and robust zero-shot generalization to unseen environments, lighting conditions, and viewpoints while remaining lightweight at inference time. |
| title | LCLA: Language-Conditioned Latent Alignment for Vision-Language Navigation |
| topic | Robotics |
| url | https://arxiv.org/abs/2602.07629 |