Vision-and-Language Navigation (VLN) is a task to guide an embodied agent
moving to a target position using language instructions. Despite the
significant performance improvement, the wide use of fine-grained instructions
fails to characterize more practical linguistic variations in reality. To fill
in this gap, we introduce a new setting, namely Underspecified
vision-and-Language Navigation (ULN), and associated evaluation datasets. ULN
evaluates agents using multi-level underspecified instructions instead of
purely fine-grained or coarse-grained, which is a more realistic and general
setting. As a primary step toward ULN, we propose a VLN framework that consists
of a classification module, a navigation agent, and an
Exploitation-to-Exploration (E2E) module. Specifically, we propose to learn
Granularity Specific Sub-networks (GSS) for the agent to ground multi-level
instructions with minimal additional parameters. Then, our E2E module estimates
grounding uncertainty and conducts multi-step lookahead exploration to improve
the success rate further. Experimental results show that existing VLN models
are still brittle to multi-level language underspecification. Our framework is
more robust and outperforms the baselines on ULN by ~10% relative success rate
across all levels.

介绍了一种基于多层次不确定指令下的视觉 - 语言导航（ULN）设置和相应的评估数据集，以提高导航任务的实用性。通过利用特定细节下的子网格（GSS）地面多级别的指令，结合利用 “Exploitation-to-Exploration (E2E)” 模块估计地面不确定性和导航代理进行多步先知探索，成功率较现有模型提高了约 10%。