PanoVLN: Towards Effective Panoramic Vision-and-Language Navigation
Recent vision-language models (VLMs) have advanced vision-and-language navigation (VLN), enabling models to predict navigation actions from visual observations and language instructions. In this work, we explore VLN with panoramic observations and introduce PanoVLN. The motivation is straightforward: more complete visual context should enable better-informed navigation decision
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Recent vision-language models (VLMs) have advanced vision-and-language navigation (VLN), enabling models to predict navigation actions from visual observations and language instructions. In this work, we explore VLN with panoramic observations and introduce PanoVLN. The motivation is straightforward: more complete visual context should enable better-informed navigation decision
This work explores a practical improvement for Vision-and-Language Navigation (VLN) agents by leveraging panoramic observations. It highlights that simply increasing visual context isn't enough, requiring deeper modifications to action prediction and training, which is crucial for developing robust embodied AI agents.