DaViNCi：面向包含连续动作与动态元素的户外视觉语言导航的数据集

中文摘要 AI 辅助

视觉语言导航（VLN）已逐步从室内环境扩展至户外环境，但现有户外VLN数据集仍依赖固定的离散拓扑图构建，无法适配快速变化的真实户外环境，阻碍了VLN智能体的现实迁移。为解决该局限，本文提出DaViNCi（Dynamic Vision-and-Language Navigation in Continuous Environment，即连续环境下的动态视觉语言导航），这是首个同时引入连续与动态因素的户外VLN数据集。智能体不仅需在户外环境中使用连续动作移动，还需应对不可预测的动态元素。该数据集包含6个不同地图，共6933条轨迹。通过全面对比实验发现，与现有数据集相比，在离散环境中DaViNCi上的成功率下降超过10%，在连续设置中下降更为明显，体现了该数据集的挑战性。此外，本文还明确了动作粒度与动态元素的影响，这些结果证明了DaViNCi在推动户外VLN向更真实环境发展的实用价值。项目网址为this https URL。

英文摘要

Vision-and-Language Navigation (VLN) has progressively expanded from indoor to outdoor environments. However, existing outdoor VLN datasets still rely on fixed discrete topological graphs for construction. It fails to align with the rapidly changing real-world outdoor environments and impedes the sim-to-real transfer of VLN agents. To address this limitation, we propose DaViNCi (\textbf{D}yn\textbf{a}mic \textbf{Vi}sion-and-Language \textbf{N}avigation in \textbf{C}ont\textbf{i}nuous Environment), the first outdoor VLN dataset that simultaneously introduces both continuous and dynamic factors. The agent not only moves in the outdoor environment using continuous actions but is also required to handle unpredictable dynamic elements. The dataset encompasses six distinct maps with a total of 6,933 trajectories. Through comprehensive comparative experiments, we find that the success rate on DaViNCi decreased by more than 10\% in discrete environments compared to previous datasets. And there is an even greater decline in continuous settings, demonstrating the challenge of DaViNCi. Furthermore, we clarify the impact of action granularity and dynamic elements. These results demonstrate the practical value of DaViNCi in advancing outdoor VLN toward more realistic environments. The website is https://xzh0312.github.io/DaViNCi/.

发表机构

• Shanghai Jiao Tong University（上海交通大学）

机构由 AI 辅助整理，请以论文原文为准。