LCLA：语言引导的潜在对齐用于视觉-语言导航
LCLA: Language-Conditioned Latent Alignment for Vision-Language Navigation
AI总结：
LCLA通过将视觉-语言观测对齐到专家策略的潜在空间，实现轻量级的视觉-运动学习，提升在不同环境下的泛化能力。
摘要：
我们提出了LCLA（语言引导的潜在对齐），这是一种用于视觉-语言导航的框架，通过将感官观测对齐到专家策略的潜在表示来学习模块化的感知-动作接口。专家首先使用特权状态信息进行训练，诱导出一个足以控制的潜在空间，之后其潜在接口和动作头被冻结。然后训练一个轻量级的适配器，通过冻结的视觉-语言模型，将原始的视觉-语言观测映射到专家的潜在空间，将视觉-运动学习的问题转化为监督的潜在对齐，而不是端到端的策略优化。这种解耦强制了感知与控制之间的稳定契约，使专家行为能够在不同传感模态和环境变化中被重用。我们实例化了LCLA并在视觉-语言室内导航任务中评估了它，其中对齐的潜在空间在分布内表现强劲，并在未见环境、光照条件和视角上表现出鲁棒的零样本泛化能力，同时在推理时保持轻量级。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
